1. travel
2. transports
3. road travel
4. road accident

The Indian Roads Congress
E-mail: [email protected]/[email protected]
Volume 43
Founded : December 1934
IRC Website: www.irc.org.in
Number 5May 2015
Contents
ISSN 0376-7256
Page
5
Technical Papers
Artificial Neural Network Modeling for Evaluating Roughness Parameters Based on Pavement Distress Values
9
Rumi Sutradhar
Manish Pal
Development of Accident Prediction Models for Safety Evaluation of Urban Intersections
C. Minachi
Jebaselwin Gladsen
A.K. Sarkar
S. Kalaanidhi
14
K. Gunasekaran
Planning of Skywalk at an Institutional Area, Study Area : ITO, Indraprastha Estate, New Delhi
24
Prakash Chand Arya
P.K. Sarkar
Detailing of Deck at Modular Type Expansion Joint
C. Kandasamy
Dhananjay A Bhide
29-32 Amendment/Errata to IRC:6-2014 and IRC:112-2011
33-35Errata to MORD Specifications for Rural Roads
(First Revision) -2014
36-37 MORT&H Circular
38
Tender Notice, NH Salem
39
Tender Notice, MORTH, NH Circle, Madurai
40
Tender Notice, NH Chennai
41
Tender Notice, MORTH Bareily
42
Tender Notice, NH Circle, Lucknow
43
Tender Notice, RO, MORTH, Chennai
44
Tender Notice, RO, MORTH, Lucknow
45
Tender Notice, RO, MORTH, Lucknow
46
Tender Notice, RO, MORTH, Lucknow
47
Tender Notice, NH, Bangalore
Jamnagar House, Shahjahan Road,
New Delhi - 110 011
Tel : Secretary General: +91 (11) 2338 6486
Sectt. : (11) 2338 5395, 2338 7140, 2338 4543, 2338 6274
Fax : +91 (11) 2338 1649
Kama Koti Marg, Sector 6, R.K. Puram
New Delhi - 110 022
Tel : Secretary General : +91 (11) 2618 5303
Sectt. : (11) 2618 5273, 2617 1548, 2671 6778,
2618 5315, 2618 5319, Fax : +91 (11) 2618 3669
No part of this publication may be reproduced by any means without prior written permission from the Secretary General, IRC.
Edited and Published by Shri S.S. Nahar on behalf of the Indian Roads Congress (IRC), New Delhi. The responsibility of the contents
and the opinions expressed in Indian Highways is exclusively of the author/s concerned. IRC and the Editor disclaim responsibility and
liability for any statement or opinion, originality of contents and of any copyright violations by the authors. The opinions expressed in
the papers and contents published in the Indian Highways do not necessarily represent the views of the Editor or IRC.
From the Editor’s Desk
NEGLECT OF ROAD INFRASTRUCTURE,
THE INDISPENSABLE PUBLIC ASSET
S.S. Nahar
Dear Readers,
Amongst the three foremost vogue responsible for the current state of the Indian road network, the first
one is a gradual but persistent mode shift in India from rail to roads. The share of road as compared to the
Railways is almost reversed after independence. The rail carried 85% of goods traffic and 51% of passenger
traffic which has declined to 23% and 13%, respectively during last six decades. The second, with rising
GDP, demand for automotive and freight travel has grown rapidly and consistently. In the first five decades,
the overall size of the vehicle fleet in India expanded from 300 thousand to 12.5 million, a 42-fold increase
and the size of the truck fleet grew from 82 thousand to 2.64 million, a 32-fold gain. With the economic
liberalization in 1990s, the annual growth in road goods and passenger freight has been nearly 12% and 8%,
respectively. The third, despite the stunning growth in road transport demand, investment in new highway
capacity has been anemic.
As a consequence, it is not only the socio-economic growth which has been adversely affected but India has
also attained a dubious distinction of having 11% of the global road crashes. On an average, India is having
one fatality at every four minutes on our roads. The socio-economic loss on account of road casualties
alone (primary factors: over speeding incline to fuel wastage/loss of foreign exchequer as well; drunken
driving; mental stress; fatigueness; overloading incline to high maintenance of vehicle/roads) apportioned
to be nearly 4% of GDP.
Our policy approach of redistribution resulted limited improvements in growth and poverty reduction
with relatively little impact on income distribution. It is scary that momentum on robust, sustained growth
and distribution policy approach is yet to take place in want of sustainable resolution of two chronic
impediments namely acquisition of land, primarily the state subject and mode of investment (warranted
to be in socialistic pattern) in harmony to the constitutional provision under Directive Principles of State
Policy, the suggested way forward. Sincere efforts are indispensable to recognize the potential of Metro
Man, Mr. Sreedharan in Highway Sector.
“
Place : New Delhi
Dated : 27th April, 2015
4
”
(S.S. Nahar)
Secretary General
E-mail: [email protected]
INDIAN HIGHWAYS, May 2015
ARTIFICIAL NEURAL NETWORK MODELING FOR EVALUATING ROUGHNESS
PARAMETERS BASED ON PAVEMENT DISTRESS VALUES
Rumi Sutradhar*, Manish Pal** and A.K. Sarkar***
ABSTRACT
Pavement roughness is a characteristic of pavement unevenness which can be measured by various pavement measuring devices
like MERLIN (Machine for Evaluating Roughness using Low-Cost Instrumentation), Bump Integrator etc. This roughness value
may be expressed in terms of IRI (International Roughness Index), an international parameter used to measure pavement roughness
conditions. But the data collection tasks of measuring IRI for enormous road networks consume substantial cost and time. In this
paper, an attempt has been taken to evaluate roughness value in terms of the IRI and BI (Bump Integrator), which may be treated as a
critical representation index of pavement performance. The analysis is done using artificial neural network methodology, and a model
is developed. The model, is based on different pavement distresses (Alligators, Potholes, Segregation, Edge cracking, Corrugation and
Patching) found in local low volume roads in Tripura, India. Using this model roughness values can be easily evaluated analytically
without going to field.
The model is verified comparing the field values and the predicted values of IRI & BI and coefficient of correlation (R2) are 0.8943
and 0.9414 respectively. Moreover it is found that out of all the distresses, patching works has more effect (25.46%) on road roughness
in low volume roads of Tripura.
1
INTRODUCTION
In India low Volume Roads (LVRs)
constitutes an integral component
of the total road network. Their
importance extends to all aspects of
the social and economic development
of rural communities. So it is required
to properly construct the roads
considering the parameters like material
properties, traffic and environment.
The purpose of a pavement response
model is to determine the pavement
surface roughness that occurs due to
different distresses caused by traffic,
ageing or environmental influences.
Surface roughness causes discomfort
to the road users, reduces vehicular
speed, increases the road user Costs
and ultimately causes structural failure
of pavement.
It also affects several functional
attributes of parts, such as friction,
wear and tear, light reflection, heat
transmission, ability of distributing and
holding a lubricant, coating etc. Two
conventional methods to determine
road roughness are; (i) MERLIN Test
and (ii) Bump Integrator Test.
But there are some difficulties in
conducting these two tests. MERLIN
is very time consuming and laborious
method and almost impossible in case
of large road length. Use of Bump
Integrator instrument is also very
tedious and laborious and heavy to
transport in the field. So it will be
very useful if a model is developed
to determine the roughness values;
based on different pavement distresses
found in local low volume roads in
Tripura, without using any instrument.
However, enough research has not
been carried out for developing such
types of models, which are capable of
expressing the roughness as a function
of distresses. Such model would be
useful to study the impact of various
distresses on roughness.
In this present study roughness
values are measured using MERLIN
and Bump Integrator and different
pavement distresses are measured
manually in some PMGSY (Pradhan
Mantri Gram Sarak Yojana) road
sections of Melaghar, West Tripura
District, India. Six types of distresses
are found in these selected sections
i.e. Alligator Cracking, Pothole, Edge
Failure, Segregation, Corrugation and
Patching. Alligators are the Series
of interconnected cracks caused by
fatigue failure of the HMA (Hot
Mix Asphalt) surface. Potholes are
small, bowl-shaped depressions in
the pavement surface that penetrates
all the way through the HMA layer
down to the base course. Edge failures
occur when the edge of a pavement
breaks up caused by traffic loading
at the edge of the pavement (usually
due to horizontal geometry problem)
and/or the infiltration of water at the
edges of the pavement or shoulder.
Corrugations are the formation of
plastic movement typified by ripples
across the pavement surface. Patching
is an area of pavement that has been
replaced with new material to repair
the existing pavement.
In recent years, ANN (Artificial Neural
Network) has attracted considerable
interest because of growing recognition
of the potential of networks to perform
cognitive tasks. So, in this paper, a
feed forward neural network model
has been adopted to predict roughness
values analytically. A neural network
model is first trained to develop the
relationship between different distress
values and IRI and BI values. Then
the ability of the neural network is
tested to generalize some unknown
datasets. It is observed that the neural
network is able to prove the correlation
between the distress parameters and
the roughness indices.
2LITERATURE REVIEW
2.1 Martin and Roberts (1998)
highlighted about the importance of
the roughness progression model in
life cycle costing analysis. This study
showed that the rates of pavement
deterioration had the most impact
* Research Scholar, ** Associate Professor, Department of Civil Engineering, National Institute of Technology, Agartala,
*** Professor, Department of Civil Engineering, BITS Pilani
INDIAN HIGHWAYS, May 2015
5
TECHNICAL PAPERS
on the annual maintenance and
rehabilitation costs in a pavement life
cycle cost analysis. In other words,
the single most important factor in a
pavement life cycle cost analysis from
a road agency perspective is pavement
performance which includes different
types of distresses.
2.2 Mustafa Birkan Bayrak, Egemen
Teomete and Manish Agarwal, (2004),
analysed the Long Term Pavement
Performance (LTPP) database to predict
the international roughness index (IRI)
in rigid pavements using Artificial
Neural Networks (ANNs). Different
pavement roughness parameters such
as initial IRI value, age, faulting, traffic
data, and transverse cracking data for 3
different severity levels (low, medium,
and high) were used as input data set
for development of ANN model.
2.3 Mehmet Saltan and Serdal
Terzi
(2004)
used
Artificial
Neural Network (ANN) and Gene
Expression Programming (GEP), in
back calculating the pavement layer
thickness from deflection measured on
the surface of the flexible pavement.
2.4 Hamid Behbahani and S.
Mohammad Elahi (2006) worked
out a survey to determine minimum
acceptable conditions and service
level of different types of roadways
in Iran’s highways. They have done
expert opinion survey is to determine
the minimum levels acceptable for
each category of roads. The result,
gives upper and lower IRI values
accepted and recommended for Iran’s
highways.
2.5 Yuksel
Tesdemir
(2009)
estimated the fracture temperature and
fracture strength of an asphalt mixture
in low temperature using Multi Layer
Perceptron (MLP) which is a part of
Artificial Neural Network (ANN) and
General Linear Model (GLM).
2.6 Dattatraya (2011) presented the
timely identification of undesirable
distress in pavements at network level
using pavement management system
6
summarizing the implementation
of a pavement condition prediction
methodology using the Artificial Neural
Network (ANN) to forecast cracking,
raveling, rutting and roughness for
Low Volume Roads (LVR) in India.
3OBJECTIVE
Surface roughness of a road profile is
expressed as IRI or BI which can be
calculated from the profiles obtained
with any valid measurement method,
ranging from static rod and level
surveying equipment to high-speed
inertial profiling systems. These two
indices are very essential for evaluating
the pavement condition. But to find out
these two values MERLIN and Bump
Integrator instruments are required
which are not easily available in the
field. So the present investigation tries
to find out an alternative method of
measuring IRI and BI by employing
ANN numerical model for said
purpose. Present study also aims
to analyze the potential of ANN in
prediction of IRI & BI. Again the
impact of each type of distress on
road roughness is measured by a
sensitivity analysis.
4ANN AS ANALYTICAL TOOL
Artificial neural networks (ANNs)
are valuable computational tools that
are increasingly being used to solve
complex problems as an alternative
to using more traditional techniques.
Over the past two decades, there has
been an increased interest in the use of
ANNs in civil engineering fields as it
possesses the capability to generalize
and predict new outcomes from past
trends at high speed and in a distributed
manner.
5BASIC CONCEPT OF ANN
An artificial neuron is a computational
model inspired in the biological
neurons. Biological neurons receive
signals through synapses located on the
dendrites or membrane of the neuron.
When the signals received are strong
enough, the neuron is activated and
emits a signal though the axon. This
signal might be sent to another synapse
and might activate other neurons. The
complexity of real neurons is highly
abstracted when modeling artificial
neurons. These basically consist of
inputs (like synapses), which are
multiplied by weights (strength of
the respective signals), and then
computed by a mathematical function
which determines the activation of the
neuron. Another function (which may
be the identity) computes the output of
the artificial neuron. ANNs combine
artificial neurons in order to process
information.
Therefore,
a
complex
ANN
architecture is characterized by the
following components: a set of nodes,
and connections between nodes. The
nodes can be seen as computational
units. They receive inputs, and process
them to obtain an output. And the
connections determine the information
flow between nodes.
6MODEL METHODOLOGY
Determination of network methodology is the most important task in the
realization of ANN model. It generally
includes the selection of hidden
layers and the learning algorithm.
Some learning algorithms require
only one layer and others require a
minimum of three layers. The number
of hidden layers is also selected based
on the problem complexity. The
interconnections between nodes are
controlled by the training algorithm
and the nature of the problem.
In this present study 10 PMGSY
(Pradhan Mantri Gram Sarak Yojana)
roads and 10 other district roads are
chosen at Melaghar block, Sonamura
sub-division and Ranirbazar, west
district, in Tripura. These roads are
so chosen as different characteristics
like Traffic, Materials used, Subgrade
CBR; Surrounding Environmental
conditions are same for all. Total 80
stretches of 225m are marked out
(4 sections at each road) according
INDIAN HIGHWAYS, May 2015
TECHNICAL PAPERS
to the majority of distresses and field
survey is conducted on these selected
stretches. In all the roads, less numbers
of vehicle are plying. It is observed that
AADT (Average Annual Daily traffic)
value is 10 to 15 CVPD (Commercial
Vehicle per Day). Other general data
are appended in Table 1.
Table 1 Variation of the Basic
Particulars of the Selected Roads
Particulars
A. Six distress types found in
local low volume roads in
Tripura are selected as
input parameters. These
distress types are: (a) Alligator Cracking; (b) Pothole;
(c) Segregation; (d) Edge
Cracking; (e) Corrugation
and (f) Patching.
B. And the outputs selected for
prediction are: (a) IRI value
and (b) BI value.
C. No of hidden layers grown
are 3 with nodes 6-3-3.
D. Learning Cycles developed
are 1660. Target error is
0.01 and average error
found after training the
network is 0.009732.
Variations
Temperature
4ºC - 39ºC
Annual Rainfall
2500 mm 2570 mm
CBR value of soil
10 - 15
OMC
14% - 14.5%
Sp. Gr. of subgrade soil
2.6 - 2.7
Total pavement
Thickness
170 - 220 mm
Aggregate Used
Stone Chips
Out of the total datasets, about 65%
records of distress areas are used as
training data subset to train the network.
Rest 35% datasets are used to estimate
the performance the developed model.
As the network can only predict data
within its learning boundary, it is
imperative that the training sets should
include all data patterns in the set.
7MODEL CONFIGURATIONS
FOR
GROW
UP THE
NETWORK
The configurations used for growing
up the neural network architecture are
as follows:
Fig. 1 Neural Network Architecture
Table 3 Measured and Predicted
Datasets of IRI & BI
8
DATASETS AND OUTPUT
RESULTS OF ANN
The maximum permissible values
of roughness for BI for road surface
are shown in the Table 2 as per the
recommendation of IRC:SP 16-2004.
The limiting range of roughness
value using MERLIN is considered as
2.4-15.9 m/km as per the manual of
MERLIN by Transportation and Road
Research Laboratory. Fig. 1 shows
the neural network architecture after
growing the network by training data
subsets. And the predicted values after
testing the network by test data subsets
is compared with the field observed
values (Table 3).
Type of Surface
Average
3.85
58
3.7993
64.8087
4.08
92
4.0624
114.4772
4.32
57
4.3988
60.0885
4.77
76
4.7039
83.4656
4.65
69
4.7745
72.75
4.54
108
4.4718
122.2167
4.49
99
4.5646
116.6369
3.66
61
3.8017
57.9485
4.34
112
4.5774
121.8436
3.61
60
3.7325
52.4505
3.94
53
3.7468
54.374
3.84
51
3.7317
52.3741
4.11
63
4.2506
71.1684
4.07
86
4.0113
94.2534
3.8
51
3.7426
53.3193
3.86
50
3.9344
58.5676
4.36
81
4.2206
79.5809
3.63
50
3.744
53.0989
4.2
97
4.1877
108.6716
3.75
66
3.8632
78.1672
4.02
61
3.8361
74.5304
4.27
104
4.4244
118.7972
50
3.7307
52.3138
80
4.0826
96.0656
4.68
73
4.6846
78.6202
Poor
4.48
70
4.4718
75.5539
4.54
98
4.4506
122.2046
4.31
86
4.5042
91.1688
3.6
65
3.7915
62.1562
3.83
63
3.802
59.2194
1.
Surface Dressing
<3500
3500-4500
>4500
2.
Open Graded Premix Carpet
<3000
3000-4000
>4000
3.
Mix Seal Surfacing
<3000
3000-4000
>4000
4.
Semi-Dense Bituminous
Concrete
<2500
2500-3500
>3500
5.
Bituminous Concrete
<2000
2000-3000
>3000
6.
Cement Concrete
<2200
2200-3000
>3000
INDIAN HIGHWAYS, May 2015
IRI
BI
(m/km)
(cm/km)
(predicted) (predicted)
3.7
Condition of Road Surface
Good
BI
(cm/km)
(field)
4.18
Table 2 Maximum Permissible Values of Roughness (mm/km) for Road Surface
Sl. No.
IRI
(m/km)
(field)
9
COMPaRISON
BETWEEN
FIELD
VALUES
AND
PREDICTED VALUES
Fig. 2 reflects the comparison between
measured IRI in field and the predicted
7
TECHNICAL PAPERS
IRI using ANN and Fig. 3 reflects the
similar activity for BI values. Fig. 4
and Fig. 5 reflect the relation between
field IRI vs. predicted IRI and field BI
vs. predicted BI respectively.
10SENSITIVITY ANALYSIS
In order to identify the effect of input
variables on IRI and BI values, a
sensitivity analysis is done through
ANN by examining the connection
weights of trained network. Relative
importance given to each input
parameter is tabulated in Fig. 6. It is
shown that patching works has more
affect (25.46%) on road roughness in
low volume roads of Tripura. whereas
Edge Cracking has less effect (8.07%)
on these parameters.
Fig. 2 Comparison between Field IRI and
Predicted IRI
22.53%, 17.05%, 15.59%, 11.30%
and 08.07% for patching, corrugation,
segregation, pothole, alligator cracking
and edge cracking respectively. Out of
six distresses patching works has more
effect (25.46%) on road roughness in
low volume roads of Tripura. There is
a future scope of validating this work
using other methodology or other
mathematical tools i.e. a judgment can
be done to evaluate the acceptance of
this ANN model with respect to other
predicting tools.
REFERENCES
1.
Saltan M. and Terzi S., “Comparative Analysis of Using Artificial
Neural Networks (ANN) and Gene
Expression Programming (GEP)
in Back Calculation of Pavement
Layer Thickness”, Indian Journal of
Engineering and Material Science,
Vol-12, pp.42-50, 2005.
2.
Mustafa Birkan Bayrak, Egemen
Teomete and Manish Agarwal, “Use
of Artificial Neural Networks for
Predicting
Rigid
Pavement
Roughness”,
Fall
Student
Conference, Ames, Iowa, 2004.
3.
Cundill M.A., “The Merlin Low
Cost Road Roughness Measuring
Machine”, Transportation and Road
Research Laboratory, pp.01-20,
1991.
4.
Suneet Kaur, V. S. Ubboveja and
Alka Agarwal, “Artificial Neural
Network Modeling for Prediction of
CBR”, Vol. 39, No. 1, 2011.
5.
Kasthurirangan
Gopalakrishnan,
“Effect of Training Algorithms on
Neural Networks Aided Pavement
Diagnosis”, International Journal
of Engineering, Science and
Technology, Volume 2, No. 2,
pp.83-92, 2010.
Fig. 6 Relative Importance Provided in
Each Input Parameters
11
Fig. 3 Comparison between Field BI and
Predicted BI
Fig. 4 Field IRI vs Predicted IRI
Fig. 5 Field BI vs Predicted BI
8
CONCLUSION
Artificial Neural Network can be a
reliable, cost effective and quick tool
for reasonably accurate estimation of
road roughness parameters (IRI & BI
value) from different distress types.
This model can be further improved by
testing and validating with additional
datasets of large range. In this paper,
the predicted values (by ANN) of IRI
and BI are compared to the field values
of that, obtained from field experiment
using MERLIN and Bump Integrator
respectively.
After
comparing,
coefficients of correlations of IRI &
BI are 0.8943 and 0.9414 respectively,
which are acceptable as the values
are close to 1. Moreover there are six
distresses viz patching, corrugation,
segregation,
pothole,
alligator
cracking and edge cracking affecting
on pavement roughness value. From
sensitivity analysis it is observed that
the percentages of affects are 25.46%,
INDIAN HIGHWAYS, May 2015
DEVELOPMENT OF ACCIDENT PREDICTION MODELS FOR SAFETY
EVALUATION OF URBAN INTERSECTIONS
C. Minachi*, Jebaselwin Gladsen**, S. Kalaanidhi*** and K. Gunasekaran****
ABSTRACT
Increasing road accident rate is a matter of serious concern. With the limited funding allocations, it is imperative to prioritize the
accident prone locations to identify the location with high potential for accident reduction after improvement. Road accident occurrence
is random in nature and variation in number of accidents over a period of time should not be counted as the result of improvement
measure (often called as regression to mean effect). Empirical Bayes approach is one of the universally accepted safety evaluation
method for performing before and after analysis of treatments. The prerequisite for Empirical Bayes analysis is Accident Prediction
Models also called as Safety Performance Functions. Accident Prediction Models not only help in predicting the number of accidents
and evaluating alternate measures but also help in evaluating the effectiveness of a treatment. It is proven that Accident Prediction
Models are the key for evaluation of improvement measures using before and after analysis. Realising the importance of the Accident
Prediction Models, many countries have developed them and included them as a part in their economic evaluation manuals. Hence
this study is attempted to develop Accident Prediction Models for signalized four arm and T intersections in urban areas. In this study,
106 intersections in Chennai were chosen and their volume count data were obtained from the previous studies for the development
of Accident Prediction Models.
1
INTRODUCTION
The major causes of road accidents
are failure of road users, road
environment and vehicle factors.
Among these factors, road user failure
is the predominant cause for most of
the road accidents followed by road
environment. Accidents due to the road
environmental aspects can be reduced
by adopting engineering measures.
In large road network, with the help of
accident black spots it often becomes
difficult to decide the type of accident
prevention measure required to reduce
accident occurrence and the sites to
be treated. Road accident reduction
can be achieved by several ways in
developed countries. Most common
approach among them safety. When
improvement is proposed for an
intersection, the role of Regression
to the Mean effect is suggested to be
considered by Highway Safety Manual
of US. The large extent of variation in
number of accidents over the period
of time makes it difficult to predict
whether the variation is due to the
improvement or natural changes in the
intersection. It is proven that a period
with comparatively higher accident rate
will be followed by low accident rate
as illustrated in Fig. 1. This tendency
is known as Regression to the Mean
effect and also implies that the high
probability of high accident rate period
is followed by low accident rate period
even if no improvement measures were
made (1). Accident Prediction Models
(APMs) are the mathematical models
that relate the accidents with the road
user volumes and other road layout
and operational features (2). Accident
prediction models are regarded as
critical part of economic evaluation
study of transportation projects.
Hence an attempt was made to develop
Accident Prediction Models for
signalised intersections in urban areas
with the accident details ad recorded
traffic flow at intersections.
Fig. 1 Effect of Regression to the Mean
Intersections are the potential areas
that need attention and treatments for
improving the overall safety in the
road network. It is common practice
to develop separate models for four
arm and three arm intersections, rather
than developing one model for all
intersections which include the type
of intersections as an explanatory
variable (3). In this study, exclusively
for vehicular injury accidents APMs
were developed for signalized four
arm intersections and signalized T
intersections using Negative Binomial
Regression method. Particularly,
these models are expected to enable
priorities for improving the accident
prone locations ensuring that more
appropriate decision is made of limited
budgetary to the road safety activities.
2BACKGROUND
Accident are analysed in many ways
across the world. Geographical
Information System (GIS) is one
among them and widely used by many
road safety projects. Various types of
accident analysis could be performed
in GIS. Deepthi et al. (2010) in their
study identified the accident prone
zones within Kannur district, Kerala
using GIS. They found that the crux
of the problem of urban transport is
congestion of traffic. This result in
increased number of trips, increased
journey time, travel cost, mental agony
and reduced accessibility. Also, they
found that the heterogeneity of traffic
is another problem which causes
severe congestion. The work gave an
insight into the existing traffic scenario
of the area and the accident prone
roads. Among different categories
* Former PG Student, ** Research Scholar, *** Former PG Student, **** Associate Professor, Division of Transportation
Engineering, Anna University, Chennai
INDIAN HIGHWAYS, May 2015
9
TECHNICAL PAPERS
of accidents, injury accidents were
considered for this study. Kanagaraj
(2002) identified and prioritized
the accident prone intersections in
Chennai city using GIS. Road accident
clusters were developed in GIS
platform. Intersection accidents were
clustered and represented in the study.
This provides quick visual displays of
accident patterns by accident categories
and thereby geographical connectivity
of the accident distribution of the
area.
It is a well-known fact that accidents
are not predictable and random
in nature. But through extensive
research, researchers found that
the accidents could be predicted by
aggregation of number of accidents
over a wide area or sufficiently longer
time period with the help of statistical
relationships. Kim Se Hwan et al.
(2005) had developed an accident
prediction model for four legged
signalized intersections of Seoul City
using simple Multivariate model. It
explains the ability of multivariate
model to reflect the characteristics of
unrecognized objects. The outcome
based on this mixed model could be
used to analyse effect of treatments
using before and after analysis for
accident prevention policy and to
classify roads with respect to the
safety levels. Pour Greibe (2003)
established simple and practicable
accident models that can predict the
expected number of accidents at urban
junctions and road links as accurately
as possible. These models can also
be used to identify factors affecting
road safety and in relation to black
spot identification and network safety
analysis undertaken by the local road
authorities.
Frank et al. (2001) founded the
effective functionality of GIS in data
management, spatial referencing and
graphic visualization of data to provide
a platform for analysis of crashes also
developed a model to predict and
analyze the number of crashes that
would occur on a specific portion
10
of highway, along a corridor, or at a
particular intersection for a given time
duration. Salifu (2003) developed
accident prediction models for four
arm and T intersections exclusively.
It was determined that the models
with traffic exposure parameters such
as cross product of flows and sum of
crossing flow products were better fit
than those with total junction flow.
Many literatures explained the
usage of Poisson’s distribution for
the accident prediction models.
Johnson (2012) justified the use of
Negative Binomial Regression for the
prediction of accidents over Poisson’s
distribution
method.
Although
the Poisson’s distribution is more
significant in predicting the accidents,
it has a constraint that it is necessary
to assume that the mean and variance
of the data are equal. But the mean and
variance of accident data would not be
similar as, high number of segments
with zero accidents causes the variance
to exceed the mean, resulting in over
dispersion of the data. Colorado
Department of Transportation (2009)
developed Accident Prediction Models
for total and injury (fatal and injury)
accidents using Negative Binomial
Regression. This work illustrates the
specification of negative binomial
error structure which helps estimating
the over dispersion parameter. Using
other standard measures of good-offit such as the mean residuals and the
value of the over-dispersion parameter,
alternative models were compared.
As far as Indian case studies are
concerned, Jacob and Anjaneyulu
(2013) developed regression models
for the prediction of accidents for the
selected districts of Kerala state. They
considered the geometrical features
of the road for the development of
model. They compared different
types of regression models and
found that Poisson to be the best to
predict accidents. Also, they found
that shoulder width influences
greatly the occurrence of accidents
than carriageway width. Vashi and
Damodariya (2003) attempted to
develop accident prediction models
for Vadodara city using Mathematical
Simulation Model and Artificial Neural
Network Model. They found that
Artificial Neural Network Model gives
results same as that of Mathematical
models.
3STUDY INTERSECTIONS
In tandem with the sprouting growth
of population and vehicles in the
city, road accidents are also facing
an increasing trend. In Chennai city,
on an average nearly 930 persons are
killed and 2500 persons are injured
due to road accidents every year. The
severity wise accident occurrence for
the period of 2006 to 2012 is given
in Table 1. For this study, totally 45
signalized four arm intersections and
61 signalized T intersections were
considered for accident prediction
model development.
Table 1 Road Accidents in Chennai City-Severity Wise (2006 – 2012)
Year
Injury Accidents (Collected)
Fatal
Simple Injury
Grievous Injury
Total
2006
1082
4336
112
5530
2007
1110
4367
1564
7041
2008
859
4296
66
5221
2009
582
3504
121
4207
2010
604
2227
1470
4301
2011
906
4513
271
5690
2012
1367
6581
741
8689
Source: DGP, SCRB, Chennai
INDIAN HIGHWAYS, May 2015
TECHNICAL PAPERS
4METHODOLOGY
OF
ACCIDENT
PREDICTION
MODELING
The methodology adopted in this
study, data collection methods, count
of accidents, development of accident
prediction models and validation of
the models and safety evaluation of
models are described below.
4.1 Data Collection
The data of accident history for six
years from 2006 to 2012 was obtained
from Chennai City Traffic Police.
The obtained accident history data
contains details such as date, time,
location, vehicles involved, severity,
cause and type of collision of the
accidents. The volume count data
for the years 2008, 2010, 2011 and
2012 were obtained from Chennai
Comprehensive
Traffic
study
conducted by Chennai Metropolitan
Development Authority. For the
remaining years, the traffic volume
data was determined by assuming
suitable annual growth rate. The
locations of accidents were geo-coded
using GPS device and transferred to
GIS platform.
4.2 Identification and Count of
Intersection Accidents
The count of accidents at intersections
was obtained by aggregating the
accident spots using Cluster analysis
technique (nearest neighborhood
technique) in the GIS software Arc
GIS 9.3. Fig. 1 shows the cluster
of injury accidents for the year
2006. Intersection accidents for the
interested locations (signalized four
arm intersections and T intersections)
were counted and used.
4.3Accident Prediction Model
Development and Validation
Accident Prediction Models were
developed using the Negative Binomial
regression analysis in statistical
software.
The dependent variable considered
in the model was injury accidents at
INDIAN HIGHWAYS, May 2015
intersections per year. Independent
variables considered were major and
minor road Average Daily Traffic flow
at intersections in PCUs.
5
DEVELOPMENT OF ACCIDENT
PREDICTION MODELS
5.1Signalized
Intersections
Four
Arm
From which 30 signalized four
arm intersections were used for
development of APMs and remaining
15 intersections were used for
validation of APMs.
The number of injury accidents
for seven years was taken as the
dependent variable and the Average
Daily Traffic flow on major and
minor roads at the intersections were
considered as independent variables.
Negative
Binomial
Regression
analysis was carried for developing
APMs. The confidence level chosen
was 95%. The developed model was
found to have the significance value
of 0.023.
Fig. 2 Cluster of Injury Accidents
In general, cross flows are critical at
intersections. Hence those were chosen
as independent variables. In statistics,
Negative Binomial regression is a
form of regression analysis used to
model count data and contingency
tables. Negative binomial assumes
that the response variable has
negative
binomial
distribution
and the logarithm of its expected
value can be modeled by a linear
combination of unknown parameters.
A negative binomial regression
model is sometimes known as a loglinear model, especially when used
to model contingency tables. The
following equation is a common form
for the APMs using negative binomial
regression.
Y = b (X1) a1 (X2) a2
Where,
Y = Dependent variable
X1, X2 =Independent variable
a1, a2 = Coefficient and
b
= Constant
... (1)
The APM developed for Signalized 4
arm intersection is:
Injury accidents per year at 4 arm
intersection
= 1.099 (Major flow)
flow) 0.560
0.345
(Minor
... (2)
The above developed models were
validated by applying the developed
APMs equation to the 15 signalized
four arm intersections which were
not used for model development.
Therefore the average percentage
of root mean square error for the
developed APMs for four arm
intersection was 22.3% and the
accuracy of the model was 77.7%.
5.2Signalized T Intersections
The dependent and independent
variables were same as that of
Signalized four arm intersections
described above. The model was
developed with the data of 46
signalized T intersections.
The APM model developed
Signalized T intersection is:
for
11
TECHNICAL PAPERS
Injury accidents per year at T junction
= 1.04 (Major flow)
flow) 0.994
0.589
(Minor
... (3)
Similarly validation was done by
applying the developed APMs
equation to the 15 signalized T
junctions which were not used for
model development. Therefore the
average percentage of root mean
square error of developed APMs for T
junctions was 26.9% and the accuracy
of the model was 73.1%.
The suitable intersections to apply
the APMs are to be selected. The first
estimate of number of accidents in the
before period and predicted number
of accidents in the before period are
to be obtained. Weighted average
of observed and predicted values is
to be obtained for carrying out EB
approach. The expected accident count
is estimated using a weighted factor
“w”.
AW = w* AT + (1 – w) * AS
AW =
Accident Prediction Models are
used to predict the accident rate at
a particular location having certain
traffic and site conditions. These
models are highly useful for
activities such as network screening,
countermeasure
comparison
and
project evaluation. In network
screening process, the number of
accidents predicted through the
model is checked with the average
accident rate of sites with similar
characteristics. Also, this kind
of screening helps in identifying
the junctions with potential for
improvements.
Countermeasure
comparison is the process in which
the alternate treatments are compared
to determine the effective measure.
In the state-of-art accident analysis
technique, it is common practice to
exercise Empirical Bayes method
for evaluating the effectiveness of
treatments. It is proven that Accident
Prediction Models are a critical
component of Empirical Bayes
Method (4).
AT
The procedure recommended by
Hauer (1997) is detailed as follows.
12
=
AS =
w =
EB adjusted expected accident count
Estimated accident count
using APMs
Observed accident count
Weighting factor which is
calculated using
... (5)
Where,
k
=
αX =
αM =
Dispersion parameter of the
negative Binomial distribution
Reliability factor of the site
specific accident rate and
Reliability factor of the typical accident rate.
It is suggested that the values of
reliability factors vary from 1 to 2
(1 for more reliability and for 2 for
less reliability) (5). The number of
accidents that could have occurred
during the after period if the
improvement had not been made “B”
is estimated accounting differences in
traffic volume and duration between
the before and after study periods.
B = AW * R* Ya
... (6)
Where,
R
=
=
length of after period in
years
To estimate the changes in crashes,
the estimate of B is summed over all
intersections in the converted group
(π = ΣB) and compared with the
total observed count of crashes “A”
(λ = ΣA), during the after period in
that group. The minimum number of
intersections required in the group
to evaluate the safety effects is eight
locations (safety network).
Var(B) = B * R * Ya/(k/AT+Yb) ... (7)
Where,
6APPLICATIONS OF ACCIDENT
PREDICTION MODELS
6.1 Procedure of Empirical Bayes
(EB) Approach
... (4)
Ya
Ratio of the annual regression predictions for the
after and before periods and
The variance of B is summed over all
treated site to get Var(π) and Var(λ) =
λ. The overall unbiased effectiveness
of measure implemented at similar
sites θ and its variance is computed as
θ = (λ/π)/{1+[Var(π)/π2)
... (8)
Var(θ) = θ {[Var(λ)/λ ] + [Var(π)/π2]}/
[1 + Var(π)/π2]2
... (9)
2
2
The estimated effect due to the change,
E is computed as
E = 1 - θ
... (10)
The standard error of θ is simply the
square root of its variance:
SE(θ) = √var(θ)
... (11)
The standard error of the treatment
effectiveness “E” is calculated as:
SE(E) = 100 * SE(θ)
... (12)
Hauer (1997) suggests that the
estimated effect, E, should not be
relied upon unless its estimated
standard error is two to three times
smaller than E.
7
CONCLUSION
With the increasing number of
accidents in urban areas, it is imperative
to undertake accident mitigation
measures to prevent loss of human
lives. With the application of
advanced
technology
such
as
Geographical Information System
(GIS) accident prone locations can be
identified.
INDIAN HIGHWAYS, May 2015
TECHNICAL PAPERS
The developed accident prediction
model can be applied for the four
arm intersections in which the minor
road has Average Daily Traffic flow
between 2504 PCUs to 83,503 PCUs
and the major road has Average Daily
Traffic flow between 7513 PCUs to
1,64,880 PCUs.
Also
the
developed
accident
prediction model can be applied
for the T junctions in which the minor
road has ADT flow between 1860
PCUs to 1,36,000 PCUs and the major
road has ADT flow between 6730
PCUs to 1,73,860 PCUs.
8
FUTURE RESEARCH
As the significance of the Accident
Prediction Models were established
and recognized in many countries, the
attempt of developing such models
for Signalized Urban Intersections of
INDIAN HIGHWAYS, May 2015
Indian cities was made. Similar attempt
may be exercised to develop the same
for uncontrolled intersections, rural
roads, multilane highways and so
forth.
9ACKNOWLEDGEMENTS
The
authors
acknowledge
the
financial support of the All India
Council for Technical Education for
the research study “Decision Support
System for Safety Evaluation of
Urban Intersections”. The paper is
an output of the above study. The
authors thank Chennai City Traffic
Police for providing necessary
accident data.
REFERENCES
1.
Report on “Highway Safety Manual”
(2010), 1st Edition, American
Association of State Highway and
Transportation Officials (AASHTO).
2.
Report on “Crash Prediction Models
for Signalised Intersections: Signal
Phasing and Geometry” (2012), NZ
Transport Agency research report
483.
3.
Report on “Introduction to Safety
Performance
Functions”,
US
Department
of
Transportation,
Federal Highway Administration,
Washington, DC.
4.
Eric Scott Johnson (2012), “Statistical and GIS Modeling of Crashes on
Utah Highways”, Master of Science
report, Department of Civil and
Environmental Engineering, Brigham
Young University.
5.
Anitha Jacob and M. L. R. Anjaneyulu (2013), “Development of Crash
Prediction Models for Two-Lane
Rural Highways Using Regression
Analysis”,
Highway
Research
Journal, pp. 59 – 69.
13
PLANNING OF SKYWALK AT AN INSTITUTIONAL AREA, STUDY AREA: ITO,
INDRAPRASTHA ESTATE, NEW DELHI
Prakash Chand Arya*, P.K. Sarkar** and C. Kandasamy***
ABSTRACT
Pedestrians, the most vulnerable road user, comprise of more than half of the daily trips generated for different purposes. It has been
observed that, over the last few decades the vehicular growth has been exponential resulting in increased vehicle-pedestrian conflicts.
Pedestrians had been continuously neglected over a long period but growing global concern due to the high risk of accidents of
pedestrian traffic is increasingly becoming the focused issue for planning of pedestrian facilities. It’s a universal fact that every trip
starts & ends with walk trip, which include all the purpose of trip making.
The need for the study has arisen of the fact that the space on roads due to heavy movement of traffic has left little or no space
on sidewalk for the movement of pedestrians. A new concept has recently been adopted in American and Asian countries on the
development of elevated corridor exclusively for pedestrians’ movement connecting different activity centres and work centres to
transit areas. While in American and Canadian cities, it was developed to provide weather proof mobility to the pedestrians in extreme
climatic conditions.
The present study addresses on the development of pedestrian facility such as Skywalk in an institutional area known as Income Tax
Office (ITO) area and also to examine its feasibility in terms of economic as well as financial aspects. This study finally concludes with
tentative guidelines for the development of Skywalk. It addresses the present condition of the pedestrian, pedestrian infrastructure and
their experiences about the existing facilities. During the study it was observed that pedestrians walking in the study area are facing
very severe problems in terms of the deteriorating condition of pedestrian infrastructure, inadequate pedestrian facilities. The study
reveals that the pedestrians are willing to shift on the proposed facility to ensure their safety.
1
INTRODUCTION
Mobility and Accessibility are an
integral component for planning and
design of road transport infrastructure.
Pedestrian traffic is most vulnerable
& neglected road users. A significant
number of pedestrians [1, 12] are killed
in the road accidents. Present approach
for planning and designing for
pedestrian traffic is not user friendly.
The fund allocated for pedestrian
facilities is insignificant as compared
to the total cost of road project. The
need for innovative approach is to
ensure safe movement of pedestrian
traffic in urban areas.
Pedestrian Planning gained ground
in 1960’s, focusing on environment
friendly pedestrian facilities. “Safety
Security Convenience Continuity
Coherence and Attractiveness” would
be the vision for any planning and
design of pedestrian facilities.
But unfortunately, till today these
factors seem to be missing in the
planning and plan preparation of
transport system of our cities.
“Road Safety in India: Challenges
and Opportunities” presented in
2009,[2], road traffic fatalities have
been increasing at about 8% annually
for the last ten years. About 60% of
all fatalities in urban areas belong to
pedestrians.
Need of the Study
The study has been carried out with a
view to improving pedestrian facilities
to ensure safe movement of pedestrian
traffic. As the study area is a major
hub for Trans - Yamuna traffic ,the
study area experiences a substantial
movement of vehicles & pedestrian
traffic.
In the light of the large scale movement
of pedestrian traffic and their
associated risks for the movement on
the urban road network due to incessant
movement of incessant movement
of vehicular traffic, there is a need to
develop exclusive pedestrian facilities
to be free from vehicular traffic.
Objectives:
● To study the pedestrian flow
characteristics on a congested
road network in an institutional
area.
● To study the relevant literatures
including the best practices with
respect to provision of Skywalk
on congested road network.
● To examine the feasibility for
development of Skywalk facility
on congested road network.
● To evolve guidelines for
development
of
Skywalk
facilities in urban areas.
Methodology of Study:
The following chart shows the
methodology adopted for the study. It
addresses the various components of
the study, which are associated with
each other. Based on the different
stages of analysis, problems of
pedestrian traffic were identified
and finally detailed economic
and financial analysis carried out
justifies the need for a skywalk
facility.
* Student, ** Professor, Transport Planning Department, School of Planning and Architecture, New Delhi, *** Ex-Director General
MORTH and Past President of IRC
14
INDIAN HIGHWAYS, May 2015
TECHNICAL PAPERS
1.
2.
Fig. 1 Methodology Adopted in the Study
2
PLANNING
&
DESIGN
CONSIDERATION OF SKYWALK
The factors are generally considered
from Planning Point of View as
under:
● Width of Median.
● Magnitude of pedestrian traffic
crossing the road intersections.
● Approximate
number
of
jaywalkers.
● Height and age of the buildings
abutting the development.
● Terrain of the underlying roads.
● Degree of conflicts between
magnitude of vehicular and
pedestrian traffic.
● Abutting Land use.
● Delay and frustration experienced
by the pedestrians in crossing the
roads.
● Magnitude of pedestrian traffic
involved in road traffic accidents
while crossing.
● Skywalk would be appropriate
with the possibility of linking
business activities with the
pedestrian areas.
● Planning & Designing should ensure direct accessibility on link
INDIAN HIGHWAYS, May 2015
between the activity generating
areas.
Points for Consideration for Economic
Feasibility of Developing Skywalks:
i) Benefits of saving in travel time
due to direct link between origin
and destinations.
ii) Improvement in the Level of
Service for pedestrian traffic
along and across the street.
iii) Making zero conflicts between
pedestrian and vehicular traffic
across the street.
iv) Saving in pedestrian accidents on
roads.
v) Possibility of linking business
within and around the pedestrian
skywalks.
vi) Increase in property value due to
increase in accessibility to
pedestrian traffic.
vii) Enhancement of environmental
quality of the urban roads.
3
CASE STUDIES
To study the skywalks and their
importance of pedestrian facility,
skywalks from different parts of the
world are studied to assess the merits
and demerits of the skywalk system:
Calgary +15 Walkway
Downtown Minneapolis Skywalk[3,4]
3. Downtown Saint Paul
4. Downtown of Hong Kong/ Singapore Skywalk
5. Shin-Yi
Skyways/Makuhari
Skywalk [14]
6. Bandra Skywalks, Mumbai [5 ]
- Bandra (E) - Kalanagar
Skywalk, Mumbai
- Bandra
(W)
Skywalk,
Mumbai
Experiences from the Best Practices
The above cities have been benefited
from the development of Skywalk
as an integrated part of the transport
infrastructure system. The major
observations identified from the study
are as under.
Skyways in Foreign Countries:
● Because of the inclement weather
in winter,
● Skyway increases the property
values of those second-story
retailers and had attracted large
number of people to the area.
● The skyway system had been
continuously
developed
in
Minneapolis and Saint Paul and
had formed a traffic network in
the city core.
● Major goal of building the
skyway system is to provide a
climate-controlled environment
had helped the city to grow its
economy.
● To strengthen the public transit
system in Shin-Yi Skyways[14].
● Expand the service area, and
● Improve the entire area’s
development in Hong Kong.
The private developers in USA
developed the linkages to the system
in order to increase their own profits.
The skyway system in the Makuhari
area, Japan is constructed for the
transportation and economic motives.
The objective of building a skyway
system in Singapore is similar to that
of the Hong Kong system - vitalizing
the economic development.
15
TECHNICAL PAPERS
Benefits of Skyways
Creation of a pedestrian-friendly
environment and encourage the
commercial development in a city
core are regarded as the two major and
interrelated advantages of skyways.
● Creating
Pedestrian-Friendly
Environment- As they separate
vehicles & pedestrians improving
safety & allowing pedestrians to
stroll freely if connected to
different buildings of downtown.
● Encouraging Commercial
Development
Traffic Aspect- Travel behaviour has
been changed due to the development
of skyway system in downtown in
American Cities as it allows people
to park in garages of fringes & reach
their destinations in city core.
Economic Aspect- Before planning
of Skyway system, the retailing in
downtown Minneapolis had been
deteriorating and was able to recover
after that system planning. The skyway
system also assists in transforming the
city core into a popular spot with a
mixture of land uses.
Concerns on Skyway Development
While developing skywalk, the
following concerns need to be
addressed:
● Impact on Street-Level Retail
● Urban Design and City Image
● Issues on Traffic
The skyway entrances are usually
located in private buildings, making
it hard for the skyway users to find
their ways to the system. Some of the
skyways in Minneapolis are not open in
the night time or on weekends, which
affects the accessibility of the skyway
system. Moreover, the operating hours
are inconsistent on holidays, causing
users to be unsure of when the skyways
are open.
Conclusions from the Indian Study:
Major issues emerged from the Bandra
Skywalk study are as under:
● Lack of maintenance, damaged
parts on skywalk in terms of
flooring or of roofing, dark
16
stretches and encroachment as
seen at Bandra Skywalk leads to
insecurity and safety issues and
may create law and order
problems especially for women
at night.
● Lack of Proper facility at places
for commuters giving them
flexibility to get over and climb
off the facility.
● It does not connect variety of
places or activities rather
connecting a single activity zone
to make full use of it even in odd
hours.
● Another issue is of encroachment
of areas under the skywalk
structure, so proper monitoring
by the local authorities and the
police needs to be maintained.
Learning’s/Inclusions
that
can
be adopted from international
case-studies:
● A skywalk must be designed in a
proper way that it would not
deteriorate the urban design of
that area.
● A Skywalk network must be
developed by keeping in mind
the different variety of land uses
and activities.
● Such facility can be developed to
connect buildings on upper floors
to make it more feasible and more
efficient by providing a concourse
level/area
for
pedestrian
circulation.
4
VARIOUS FUNDING OPTIONS
TO DEVELOP SKYWALK
In order to develop the skywalk system,
it is extremely important to explore the
possibilities to work out the funding
options. The following could be some
of the options attempted elsewhere to
fund infrastructure projects.
● Involve private sector to reduce
costs through higher efficiency
● Compromise service quality to
reduce costs
● Exploit other assets - property
development
● Make non-user beneficiaries pay
● Public subsidy
● Explore a number of PPP options
5STUDY AREA PROFILE
The study area was chosen depending
on various reasons. The growth of
Indraprastha area as a major work
centre in Delhi has been very fast
over the past decades and the problem
of peak hour congestion is severe
resulting poor level of service both
for pedestrian and vehicular traffic.
Indraprastha, as a major work centre
of Delhi, has almost 25000 employees
working here in 101 different
institutions of government departments
as well as private sector offices. It is
one of the main institutional areas in
Delhi. It has a railway station, 2 metro
stations andmany bus stops generating
a large scale of pedestrian trips. Fig. 2
shows the location of the study area.
Fig. 2 Zonal Map of Delhi Showing the Study Area in Zone D
INDIAN HIGHWAYS, May 2015
TECHNICAL PAPERS
Fig. 3 shows the land use distribution
in study area with recreational area
of 44%. Apart from this, Public Semi
Public area is of 28% while Govt.
Office area is of 17.5%, followed
by commercial area with 12% and
Educational area with 0.5%. Fig. 4
shows the proposed skywalk network
whose feasibility is to be determined.
of the facility is to be analyzed at
different times of the day for different
age groups.
6.1 Primary
Data
Analysis
Pedestrian Flow Across the
Roads
Pedestrian volume count is conducted
at different locations in the area to get
an idea about the pedestrian flow and
quantum of pedestrian. The peak hours
are identified at sections and pedestrian
crossing. For the road crossing, it was
observed that the FOB is used to a
maximum in afternoon hours while in
morning and evening the pedestrian
count is observed to even one-third
of peak hour. Fig. 5 shows, hourly
variation in Pedestrian flow across
Foot Over Bridge. The peak hour
pedestrian flow on FOB is at noon
time (1:30- 2:30) with 1524 footfalls
while least pedestrian flow is observed
at evening time (7:30- 8:30) with 53
counts as the pedestrian movement in
area tends to decrease in evening.
Fig. 6 shows shows hourly variation
in pedestrian flow across Ram Charan
Agrawal Chowk. The peak hour at this
junction is in morning (9:30- 10:30)
with 1740 Pedestrians while least
pesestrian flow is observed in the
afternoon during the time (2:30- 3:30)
with 1114 footfalls.
Fig. 3 Land Use Map of Study Area with
Survey Locations
Fig. 5 Across Pedestrian Volume Count at FOB
Fig. 4 Proposed Skywalk Network
6
DATA ANALYSIS
The survey was conducted on the
above stretch where the feasibility
INDIAN HIGHWAYS, May 2015
Fig. 6 Across Pedestrian Volume Count at RCA Chowk
17
TECHNICAL PAPERS
Along Movement of Pedestrian:
Fig. 7, shows a maximum pedestrian
flow in morning (9:30- 10:30) along the
road between Pragati Maidan - RCA
Chowk with 2777 pedestrians walking
along road and minimum pedestrian
flow is observed in the afternoon
(2:30-3:30) with 1194 pedestrians.
Fig. 8 indicates a maximum
pedestrian flow along the road
between between RCA Chowk and
SPA in morning peak hour during
the period of (9:30- 10:30) with 3680
pedestrians and least pedestrian flow
is observed in the evening during
the period of (6:30- 7:30) with 1404
pedestrians.
and 30 years while the 42% are
between 30 and 40 years.
A total 70% of the pedestrians
have an opinion of unsafe
crossing in the area while 78%
thinks the surrounding walking
area is also not safe.
The 80% of the surveyed
pedestrians are ready to use
skywalk facility while the 39%
of pedestrians are willing to pay
for that.
-
-
6.2Analysis of Level of Service
In order to carry out the analyses
on pedestrian level of service, the
guidelines prepared by the IRC are
referred [10] as presented in the
Table 1 and Table 2.
Table 1 Pedestrian LOS with Area &
Flow Rate
Fig. 7 Pedestrian Flowa Along Pragati Maidan RCA Chowk
LOS
Area/
Pedestrian
Flow Rate (ped./
min/Sq. meter)
A
5 Sq m
12
B
3.3- 5 Sq m
12- 15
C
1.9- 3.3 Sq m
15- 21
D
1.3- 1.9 Sq m
21- 27
E
0.6- 1.9 Sq m
27- 45
F
0.6 Sq m
Varying flow rate
Source: IRC:103-2012
Table 2 Pedestrian LOS at Road
Crossing (Ref. 5)
Pedestrian LOS at Road Crossing
Level of Service Waiting Time in Seconds
A
Fig. 8 Pedestrian Flow Along Pragati Maidan Station - RCA Chowk
Pedestrians’
Attitude
towards
Skywalk
The Pedestrian Questionnaire survey
is conducted on this I.P. Marg stretch
to examine the feasibility of the
facility with respect to different age
groups. The conclusions emerged are
as under:
-
It is observed that 75% of
pedestrians perform work trips,
as this area is an important Work
Centre dominated by institutional
18
-
-
-
area where significant portion of
education trips constitute a share
of 8%.
The 85% of persons coming to
and leaving ITO area are daily
visitors with a total of 65% from
the different parts of NCR.
The Male - female ratio of
pedestrian at ITO is observed to
be 78:22.
The 55% of the pedestrians belong to the age group between 20
≤3
B
>3 and 13≤
C
>13 and 38≤
D
>38 and 64≤
E
>64 and 90≤
F
≥90
Source: IRC:103-2012
Above standard shows that the delay
experienced by the pedestrian at the
various intersections is of the LOS
F. However the proposed facility
will eliminate waiting time/delay
of any pedestrian at the crossings/
intersections and would result in level
of service of A.
INDIAN HIGHWAYS, May 2015
TECHNICAL PAPERS
6.3Secondary Data Analysis
as 192 in a decade (2003-2012),
constituting a share of over 25% of
fatalities.
Various components of secondary
data are collected from the different
sources i.e., from public and private
departments and agencies. The
secondary data collected covers the
data on estimation of cost of skywalk,
advertisement cost, accident data from
the Police Station for calculating the
economic loss of human lives and the
cost for installation & maintenance of
lifts & escalators.
minimum economic loss due to road
accidents is of the order of 32.94 lakhs
in the year of 2008.
Accident Data:
To calculate the actual accident
cost, different studies [11,13,15] are
referred and it is identified that cost
of accidents varies in a range of
approx. 7 lakhs to 8.6 lakhs for 1
fatality. For calculation of accident
cost of pedestrian, the referred cost
of one Fatal accident, Major &
Minor accidents are Rs.8,64,350/-,
Rs.5,70,530/&
Rs.84,864/respectively.
It was observed that the numbers
of accident cases registered in the
police station at ITO area are as many
Fig. 10 Economic Losses Due to
Accident Cases at ITO
Cost Estimate
No. of
Accidents
Fatality
Major
Minor
Economic Loss
(in Rs.)
2003
46
9
14
28
1,81,42,762
2004
31
7
8
16
1,19,72,514
2005
17
5
4
9
73,67,646
2006
20
6
5
10
88,87,390
2007
12
3
3
6
48,13,824
The cost of the skywalk is estimated
by referring the cost of the various
items to be used in the construction
of the facility with the reference
of the cost of construction of the
Bandra Skywalks of Mumbai.
The cost estimation also refers the
estimate adopted by the IL&FS for
its work for “Pre-Feasibility Report
for Development of Modern Foot
over Bridges on PPP format for each
City in Karnataka to Infrastructure
Development Department”. (Ref. 11)
2008
9
2
2
5
32,94,080
Data Used For Calculation:
2009
12
2
3
7
40,34,338
Length of Skywalk-
1000 m (approx.)
2010
9
3
2
5
41,58,430
Cost of Skywalk-
40,00,00,000 (approx.)
Table 1 Decadal Accident Trend & Economic Loss Incurred
Year
Type of Injury
2011
15
9
2
4
92,59,666
Tentative time required to 12 months
Construct Skywalk-
2012
21
7
5
11
98,36,604
Total
192
53
48
101
8,17,67,254
Installation & Maintenance 75 lakhs for 3 years
cost of Escalators-
It is clear from the Table 1 that the
data collected over the last decade
in the cases of occurrence of road
accidents fluctuated to a great extent
but on an average the quantum is in
decreasing trend with 21 accidents
reported in the last year with 7
fatalities.
It is shown in Fig. 9 that in the last
decade, the maximum number of
accident occurred in 2003 in all the
categories including major, minor
and fatal cases with total number of
51 people. While the least cases of
accidents are reported in the year 2008
with 9 victims.
INDIAN HIGHWAYS, May 2015
Installation & Maintenance 10 lakhs for installation
cost of Lift-varies
& maintenance
Revenue to be generated due to
Advertisement for development of
the Skywalk:
Fig. 9 Trend of Accident Cases Over the
Last Decade
In Fig. 10, the trend of the economic
loss due to accident cases at ITO
area over the last decade is shown.
The maximum number of accident
occurred in 2003 resulting in
economic loss of 181.43 lakhs, with
major, minor and fatal cases. The
In order to work out the revenue due
to advertisement to fund the
development
of
skywalk,
the
municipal data on advertisement
charges of MCD is referred & used
for the calculation of the revenue
considering the 20% of the length of
the skywalk to be used for
advertisement on both sides of
the walkway. It is calculated that
the total revenue in 1st year, from
advertisements will be Rs. 6.35 Crore.
19
TECHNICAL PAPERS
7
FEASIBILITY STUDY
The detailed Feasibility study has
conducted with respect to both
Economic & Financial aspects [13].
For the feasibility analysis cost
estimate
is
prepared,
some
assumptions were also made, the
calculations are finally carried out
with respect to for both financial and
economic analysis.
7.1Assumptions
The following are the key assumptions
in Economic Analysis:
Construction period-
12 months (July
2013- June 2014)
Project concession period-
30 years
Project cost-
40 Crore
(approx.)
Discount factor-
10%
The average monthly income-
35000/-
No major repair works needed after a time period as
maintenance work is carried out every year will be
sufficient.
The following are the key assumptions in Financial
Analysis:
Key assumptions in Financial
Analysis:
Tax rate-
35%
Discount rate-
10%
Depreciation Rate of structure 25%
costEquity @33.33% and Debt
@66.67%
Loan Amount-
13.5 Crore in each
1st & 2nd year
Future value of 20 Cr for 1st 23.2 Crore
yearFuture value of 20 Cr for 2nd 26.912 Crore
yearTotal Future Value of Loan-
50.112 Crore
Loans repay time-
10 years
Interest rate-
16%
Annuity-
7.00 Crore
7.2Economic Analysis
While working out the economic
losses due to the delay incurred for
crossing a road in busy ITO area
by pedestrian traffic, the above
assumptions supported with observed
average delay due to crossing of road
by pedestrians of 3 minutes along
with the total economic losses by the
20
pedestrians using ITO area on a normal
day are considered & works out to
Rs.3.92 Crore per year.
Further analysis is carried out with
respect to NPV, B/C ratio & EIRR
method which are Rs.89.390 Crore,
2.74 & 10% respectively as presented
in the Annexure 1.
7.3 Financial Analysis
The financial analysis is carried out
with the above specified assumptions
and the further analysis shows the
values of NPV, B/C ratio & FIRR are
of the order of Rs.36.97 Crore, 5.78 &
19.685% respectively as presented in
the Annexure 2.
7.4Summary of the Analyses
Analysis of the pedestrian traffic with
respect to magnitude walking along
the road reveals that pedestrians are
exposed to very deteriorating level of
service primarily dictated by level of
service F.
1. Average
pedestrian
delay
observed in front of Police Head
Quarters is more than one and
half minutes reflecting the quality
of Level of Service F.
2. The study reveals that the delay
experienced by the pedestrians
at the intersection is more than
2 minutes; this also reflects
deteriorating
condition
of
pedestrian movement dictated by
Level of Service F.
3. Analysis further reveals at least
there are 2 victims of pedestrian
traffic killed in the ITO area per
year.
4. The study also reveals that 39%
of the pedestrians are willing
to pay at least Rs.2/- for using
the skywalk facility & a total of
80% pedestrians of the total
pedestrian interviewed would be
interested to shift for the skywalk
facility.
5. Economic & financial analysis
also suggest a significant
benefit to be accrued as against
the total cost of the development
of skywalk yielding EIRR &
NPV of 10% & Rs. 89.30 Crore
while FIRR & NPV would result
in 19.685% & Rs.36.97 Crore
respectively.
The problems of pedestrian traffic
moving along & across the road in the
area are as severe as indicated above
with respect to poor LOS & coupled
with total accident more than 5 per
year. This is one of the serious issues
of pedestrian problem to be addressed
& can be addressed only through the
grade-separated facility like Skywalk.
8.TENTATIVE GUIDELINES
By appreciating merits and demerits of
the various case studies reviewed and
the present condition of the study area,
a tentative Guideline for development
of Skywalk has been attempted [7, 8, 9,
16] as under. The need for the skywalk
may be addressed on the basis of the
following situations:
8.1 General
-
Where appropriate facilities for
pedestrian traffic at grade, cannot
be accommodated or not feasible.
-
If strong desire line for pedestrian
movement exist within 150 m of
the landing of an existing flyover.
-
Preferably
Skywalk/grade
separated crossings to be planned
& designed as a part of an
integrated BRT proposal.
-
Exceptional Skywalk/FOB may
be permitted where a facility for
pedestrian movement at grade is
not possible to be accommodated
or feasible.
8.2Location
Should be developed at a large scale
pedestrian generating areas possibly
linking of areas such as Shopping,
School, Civic, Public - Semi-public,
Recreational/Bus Station, Railway
Station, etc.
8.3Accessibility Consideration
-
Skywalk should be free from
encroachment.
INDIAN HIGHWAYS, May 2015
TECHNICAL PAPERS
-
Free from all kinds
encroachments in general.
of
-
8.4Engineering Feasibility
-
A minimum of 3 m of width of
skywalk is preferable where in
the absence of any commercial
exploitation & minimum of 5 m
is required for commercial
exploitation with respect to
accommodating shops within the
skywalk.
-
Minimum width of staircase- 3 m
-
All
year
protection-
round
-
-
Vehicle clearance/ headroom of
5.5 m from road surface.
All public staircase, ramp/
elevator design standards to be
followed.
Cycle Elevators should be
provided at every alternate FOB
and should be 1400 x 2000 mm
Relocation of overhead services
must be considered while
designing the structure.
Lighting for safety and visibility- Skywalk must deliver a sense of security
and safety even during evening/night.
Adequate lighting must be provided at both
access points and along the Skywalk.
Lighting level on and around the Skywalk
must be minimum 20 lux.
Access to the FOB should also be well lit.
Seating-
Resting places and seating must be
provided at minimum two locations along
the Skywalk and at every 100 m length of
skywalk.
Garbage Disposal-
Garbage bins must be located adjacent to
both access points.
Way Finding/information maps-
Signage indicating the location of various
activity zones as per standards must be
provided where appropriate, particularly
near pedestrian attractors, way-finding/
information maps must be provided.
8.6 Guidelines Based on Financial
Analysis
-
FIRR should be ≥18% with
substantial NPV values.
-
PPP modal should be one of
the
means
of
successful
implementation of for a Skywalk
facility.
INDIAN HIGHWAYS, May 2015
8.5Usability
weather It should ensure all year round weather
protection.
Guidelines based on Economic
Analysis:
-
It should have minimum EIRR of
10% with positive NPV coupled
with acceptable B/C ratio to be
more than 1.
-
Based on minimum number of
fatalities 2 in a year.
-
The following funding options
could be used for successful
development of Skywalk facility:
i) Revenues from Hoardings/
advertisements on sides of
Skywalk
ii) Revenues from Rental from
Kiosk along and below the
staircases
iii) Revenues
from
some
portion of ticket fares of
metro ride.
iv) Revenues from tickets
purchased by the tourist
visiting tourist destinations.
v) Revenues from the private
shopping
centers/malls
being connected through the
skywalks.
vi) Revenues from the places of
sightseeing being connected
with the skywalks (Museums, Libraries etc.)
vii) Funding from PPP model
(concessioner
generating
revenue from by developing
the skywalk)
viii)Revenues from exhibits
displayed on the walls of
skywalks
9
CONCLUSIONS
From the above study it is observed and
established that the existing condition
of the study area is very deteriorating
with respect to the heavy pedestrian
and vehicular flow. Hence an effort
for improvement of this area is needed
with extreme urgency for betterment
of the condition. The study also proves
that from the aspect of Economic &
Financial Analysis, the planning of a
facility like Skywalk is feasible and
the pedestrian are ready to use such
facility for safety, time saving and
convenience.
REFERENCES
1.
2.
3.
4.
5.
Annual Report- Government of
India Ministry of road Transport and
Highways Transport Research Wing,
2011, New Delhi, Road Accidents in
India.
Sarkar, P.K, “Road Safety in India,
Challenges and Opportunities, 2009,”
presented at Road safety Conference,
July, ICT, New Delhi.
Kaufman S, 1985 The Skyway Cities
(CSPI, Minneapolis, MN).
Michael J Corbett etc al, “Evolution
of the Second-Story City: the
Minneapolis
Skyway
System”,
Environment and Planning B:
Planning and Design 2009, volume
36, pages 711 -724.
Manual on Economic Evaluation
of Highway Projects in India, 2011,
IRC, New Delhi.
21
22
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
14
15
16
17
18
19
20
21
22
23
24
30 yrs
2027
13
Total
2026
2044
2025
11
12
30
2024
10
2043
2023
9
2042
2022
8
29
2021
7
28
2020
6
2041
2019
5
27
2018
4
2039
2017
3
2040
2016
2
25
0.00
2015
1
26
0.00
2014
0
40.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
16.00
24.00
2013
-1
Capital
Cost
Year
Project
Yrs.
32.65
2.04
1.02
1.02
1.02
1.02
1.94
0.97
0.97
0.97
0.97
1.85
0.93
0.93
0.93
0.93
1.76
0.88
0.88
0.88
0.88
1.68
0.84
0.84
0.84
0.84
1.60
0.80
0.80
0.80
0.80
0.00
0.00
Operation
Cost
26.07
0.98
0.98
0.98
0.98
0.98
0.93
0.93
0.93
0.93
0.93
0.89
0.89
0.89
0.89
0.89
0.85
0.85
0.85
0.85
0.85
0.81
0.81
0.81
0.81
0.81
0.77
0.77
0.77
0.77
0.77
0.00
0.00
Maintenance
Cost
Cost
98.72
3.02
2.00
2.00
2.00
2.00
2.88
1.90
1.90
1.90
1.90
2.74
1.81
1.81
1.81
1.81
2.61
1.73
1.73
1.73
1.73
2.49
1.65
1.65
1.65
1.65
2.37
1.57
1.57
1.57
1.57
16.00
24.00
Total Cost
88.85
2.72
1.80
1.80
1.80
1.80
2.59
1.71
1.71
1.71
1.71
2.47
1.63
1.63
1.63
1.63
2.35
1.55
1.55
1.55
1.55
2.24
1.48
1.48
1.48
1.48
2.13
1.41
1.41
1.41
1.41
14.40
21.60
Considered
Total Cost
713.98
68.85
62.59
56.90
51.73
47.03
42.75
38.87
35.33
32.12
29.20
26.55
24.13
21.94
19.94
18.13
16.48
14.98
13.62
12.38
11.26
10.23
9.30
8.46
7.69
6.99
6.35
5.78
5.25
4.77
4.34
0.00
0.00
Delay
Cost
134.50
12.97
11.79
10.72
9.75
8.86
8.05
7.32
6.66
6.05
5.50
5.00
4.55
4.13
3.76
3.42
3.11
2.82
2.57
2.33
2.12
1.93
1.75
1.59
1.45
1.32
1.20
1.09
0.99
0.90
0.82
0.00
0.00
Accident
Cost
Benefit
848.48
81.82
74.39
67.62
61.48
55.89
50.81
46.19
41.99
38.17
34.70
31.55
28.68
26.07
23.70
21.55
19.59
17.81
16.19
14.72
13.38
12.16
11.06
10.05
9.14
8.31
7.55
6.87
6.24
5.67
5.16
0.00
0.00
Total
Benefits
COST BENEFIT ANALYSIS (in Crore)
Annexure 1 Economic Cost Benefit Analysis
0.0573
0.0630
0.0693
0.0763
0.0839
0.0923
0.1015
0.1117
0.1228
0.1351
0.1486
0.1635
0.1799
0.1978
0.2176
0.2394
0.2633
0.2897
0.3186
0.3505
0.3855
0.4241
0.4665
0.5132
0.5645
0.6209
0.6830
0.7513
0.8264
0.9091
1.0000
0.0000
Discount
Factor- 10%
51.36
0.16
0.11
0.12
0.14
0.15
0.24
0.17
0.19
0.21
0.23
0.37
0.27
0.29
0.32
0.36
0.56
0.41
0.45
0.50
0.54
0.86
0.63
0.69
0.76
0.84
1.32
0.96
1.06
1.17
1.28
14.40
21.60
Discounted
Cost
140.66
4.69
4.69
4.69
4.69
4.69
4.69
4.69
4.69
4.69
4.69
4.69
4.69
4.69
4.69
4.69
4.69
4.69
4.69
4.69
4.69
4.69
4.69
4.69
4.69
4.69
4.69
4.69
4.69
4.69
4.69
0.00
0.00
Discounted
Benefit
89.30
4.53
4.57
4.56
4.55
4.54
4.45
4.51
4.50
4.48
4.46
4.32
4.42
4.40
4.37
4.33
4.13
4.28
4.24
4.19
4.14
3.83
4.06
4.00
3.93
3.85
3.37
3.73
3.63
3.52
3.41
-14.40
-21.60
NPV
TECHNICAL PAPERS
INDIAN HIGHWAYS, May 2015
INDIAN HIGHWAYS, May 2015
2043
2044
30 yrs
30
Total
2033
19
29
2032
18
2042
2031
17
28
2030
16
2041
2029
15
2040
2028
14
27
2027
13
26
2026
2039
2025
11
12
25
2024
10
2038
2023
9
2037
2022
8
24
2021
7
23
2020
6
2036
2019
5
22
2018
4
2034
2017
3
2035
2016
2
20
0.00
2015
1
21
0.00
2014
0
40
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
16.00
24.00
2013
-1
Capital
Cost
Year
Project
Yrs.
421.70
26.13
24.88
23.70
22.57
21.49
20.47
19.50
18.57
17.68
16.84
16.04
15.28
14.55
13.86
13.20
12.57
11.97
11.40
10.86
10.34
9.85
9.38
8.93
8.51
8.10
7.72
7.35
7.00
6.66
6.35
0.00
0.00
Advertisement
Revenue
1,565.08
207.27
180.24
156.73
136.28
118.51
103.05
89.61
77.92
67.76
58.92
51.23
44.55
38.74
33.69
29.29
25.47
22.15
19.26
16.75
14.56
12.66
11.01
9.58
8.33
7.24
6.30
5.48
4.76
4.14
3.60
0.00
0.00
Commercial
Revenue
1,986.79
233.40
205.12
180.42
158.85
140.00
123.52
109.10
96.49
85.44
75.76
67.27
59.83
53.29
47.54
42.49
38.04
34.12
30.66
27.60
24.90
22.51
20.39
18.51
16.83
15.34
14.01
12.82
11.76
10.80
9.95
0.00
0.00
Total
Revenue
58.72
3.02
2.00
2.00
2.00
2.00
2.88
1.90
1.90
1.90
1.90
2.74
1.81
1.81
1.81
1.81
2.61
1.73
1.73
1.73
1.73
2.49
1.65
1.65
1.65
1.65
2.37
1.57
1.57
1.57
1.57
0.00
0.00
O&M
Expenses
1,928.06
230.38
203.12
178.42
156.85
138.00
120.64
107.20
94.58
83.54
73.86
64.53
58.01
51.47
45.73
40.68
35.43
32.39
28.93
25.88
23.18
20.03
18.75
16.86
15.19
13.70
11.64
11.26
10.19
9.24
8.38
0.00
0.00
Net
Operating
Income
36.16
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.97
1.80
2.51
3.13
3.67
4.13
4.52
4.86
5.16
5.41
0.00
0.00
Interest
Paid
33.82
0.00
0.00
0.00
0.00
0.01
0.01
0.01
0.02
0.02
0.03
0.04
0.05
0.06
0.08
0.11
0.15
0.20
0.27
0.36
0.48
0.63
0.85
1.13
1.51
2.01
2.68
3.57
4.76
6.34
8.46
0.00
0.00
Depreciation
FINANCIAL STATEMENT (in Crore)
1,858.08
230.38
203.12
178.42
156.85
138.00
120.64
107.19
94.57
83.52
73.83
64.50
57.97
51.41
45.64
40.56
35.28
32.19
28.66
25.52
22.70
18.43
16.10
13.22
10.55
8.02
4.84
3.17
0.57
-2.26
-5.49
0.00
0.00
Profit
Before
Taxes
Annexure 2 Financial Analysis
643.08
80.63
71.09
62.45
54.90
48.30
42.22
37.52
33.10
29.23
25.84
22.57
20.29
17.99
15.98
14.20
12.35
11.27
10.03
8.93
7.94
4.51
3.94
3.24
2.58
1.97
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Taxes
1,215.01
149.74
132.03
115.97
101.95
89.70
78.41
69.67
61.47
54.29
47.99
41.92
37.68
33.42
29.67
26.37
22.93
20.92
18.63
16.59
14.75
13.91
12.16
9.98
7.96
6.06
4.84
3.17
0.57
-2.26
-5.49
0.00
0.00
Net Profit
After Taxes
1,221.48
149.75
132.03
115.98
101.96
89.70
78.42
69.68
61.48
54.31
48.02
41.96
37.73
33.48
29.75
26.48
23.08
21.12
18.90
16.95
15.23
14.88
13.63
11.99
10.57
9.35
8.96
8.32
7.03
5.89
4.86
-16.00
-24.00
Cash Flow
After Taxes
145.59
8.58
8.32
8.04
7.78
7.53
7.24
7.07
6.87
6.67
6.49
6.24
6.17
6.02
5.89
5.76
5.53
5.56
5.47
5.40
5.34
5.74
5.78
5.59
5.42
5.28
5.57
5.68
5.28
4.86
4.42
-16.00
-24.00
Present
Value of
CFAT
0.0573
0.0630
0.0693
0.0763
0.0839
0.0923
0.1015
0.1117
0.1228
0.1351
0.1486
0.1635
0.1799
0.1978
0.2176
0.2394
0.2633
0.2897
0.3186
0.3505
0.3855
0.4241
0.4665
0.5132
0.5645
0.6209
0.6830
0.7513
0.8264
0.9091
1.0000
0.0000
Discount
Factor10%
36.97
0.66
0.71
0.76
0.81
0.86
0.92
0.99
1.07
1.14
1.23
1.31
1.43
1.54
1.66
1.80
1.91
2.12
2.30
2.50
2.73
2.88
3.27
3.57
3.92
4.30
4.48
5.23
5.76
6.36
7.02
-14.40
-23.87
NPV
TECHNICAL PAPERS
23
DETAILING OF DECK AT MODULAR TYPE EXPANSION JOINT
Dhananjay A Bhide*
Synopsis
Construction of bridges with long continuous lengths is definitely a preferred option now days. Advantages are lesser number of
joints, better riding quality and durability of deck & wearing coat. Longer continuous lengths require joints with more expansion and
contraction capacities. The solution then rests at modular strip seal type of expansion joint.
This type of joint essentially consists of required number of strip seals assembled together. Capacity of single strip is about 70 to
75 mm. This is limited on account damage caused to wheels of the vehicles if gap is more than 80 mm. Therefore the capacity of
modular type of joint is always in multiples of 75 mm.
The assembly of the strip seals makes load transfer of this type of joint in a unique pattern. This is totally different than load transfer
mechanism of single strip seal. If the connection details are not prepared with due consideration to the said transfer mechanism, the
joint will invariably fail early during service.
The paper highlights the load transfer mechanism of the modular joint and discusses about the detailing of the deck slab for proper
connection of the joint with it.
1
INTRODUCTION
The author noticed one expansion
joint covered with steel plates and
another with depressed middle
beam while travelling. (It was later
covered with steel plate as well.)
On closer view it was realized that
joints had suffered some damage and
were not functional. As an immediate
remedial measure, steel plates were
provided over modular type strip
seal expansion joints, obviously the
necessary on account some damage
to the joint. (Photographs 1 & 2) It
may be noted that photographs are
from different locations. This indicates
the problem may not be an isolated
one.
Photo 1 Showing Steel Plate Over
Modular Joint Fixed with Dash Fasteners
at one end. This allowed Free Movement
of Structure
Photo 2 Showing Steel Plate over
Modular Joint, Laid in a Recess Cut
over Joint Restricting Free Movement of
Structure
Photo 3 Showing Damage to Underside
of Modular Joint
(No hanger reinforcement or
reinforcement below the box/
around the joint)
At one such location the damage
was clearly seen as the joint was
not covered with steel plate at that
juncture. The said joint was two seal
joint. The middle separation beam
showed significant settlement.
It was easily possible to check the
damage from underside of the joint
(Photo 3) at this location. On underside
the box assembly of the support bar,
supporting middle separation beam
had punctured the concrete and was
in hanging condition. It was quite
clear that detailing at the location was
done without any consideration to
functioning of modular type expansion
joint.
The need of modular type joint is
normally clear at the detail design stage
itself. As such proper detailing needs
to be considered right from the start.
In case it is missed or need of modular
joint arises as result of changes in
proposal, then the details at the joint
have to be modified accordingly.
This paper explains the functioning
of the modular joint, the load transfer
mechanism and suggests detailing
that should be followed for installing
the said joint. Author hopes this will
provide enough insight for necessary
detailing, either at design stage or
during modification or repair.
2STRIP SEAL TYPE OF
JOINTS
Two types of strip seal joints are used
presently, single strip seal and modular
strip seal. Typical details of modular
strip seal joint are in Fig. 1 to 3.
2.1Single Strip Seal Joint
Single strip seal joint has one
elastomeric seal fixed within two
edge beams. These edge beams are
* Chartered Engineer, E-mail: [email protected]
24
INDIAN HIGHWAYS, May 2015
TECHNICAL PAPERS
connected to deck slabs on either side.
An elastomeric seal provided between
edge beams prevents dirt or water to
pass through the joint.
2.2Modular Strip Seal Joint
The typical modular strip seal joint
consists of edge beams, same as that
of single strip seal joint and internal
across all internal separation beams,
termed support bars. These support bars
are generally spaced at 1.5 m centers.
The special connection between
them allows for closure or expansion
of the gap between the separation
beams with equal movement of each
of the separation beams and effects
movement of the joint.
separation beams. Strip seals are
provided between each pair of beams.
The number of seals corresponds to
required joint movement capacity.
The edge beams are same as for single
strip seal joint.
Internal separation beam(s) always
form an assembly that rest on beams
Fig. 1 Typical Details of 7 Seal Modular Joint
Fig. 2 Typical Details of 2 Seal Modular Joint
3TRANSFER OF TRAFFIC
LOAD FROM THE JOINTS
TO DECK
3.1 From Single Strip Seal Joint
The wheels of vehicles passing over
joint span across the edge beams. The
gap between edge beams is limited to
INDIAN HIGHWAYS, May 2015
70 to 75 mm as larger gap would harm
the wheels. The seal between edge
beams is never in contact with wheels.
Thus the load is directly transferred to
the edge beams and in turn to deck.
Edge beams are properly anchored
to deck slab thus effectively transfer
Fig. 3 Components of Modular Joint
vehicular load to deck. The load transfer
to deck is direct from the wheels. The
connection between edge beam and
slab has to withstand primarily the
shear and tractive forces on account
of this load transfer. The deck slab
projecting from diaphragm or behind
25
TECHNICAL PAPERS
the joint needs to be designed for this
direct load transfer.
Projecting deck slab is designed as
cantilever slab beyond diaphragm and
detailed accordingly.
3.2 From Modular Strip Seal Joint
The wheels of vehicles passing over
modular joint span across the edge or
internal separation beams, as the case
may be. The seals of this joint also
never come in contact with wheels. At
edge beams the load transfer to deck
is identical to that of single strip seal
joint.
The wheels while passing over internal
separation beams or spanning across
the beams, transfer the load directly to
the respective beams. The wheel area
in contact with beam area will decide
the actual load transferred to the beam.
The full load from wheels will be
transferred to internal separation beam
when wheels pass over it. The internal
beam is supported on support bars
spaced in transverse direction. The
separation beam is a continuous beam
across these support bars. All wheels of
the axle will impart load to separation
beam and then to the support bars,
accordingly. The reaction on support
bar has to be derived from continuous
separation beam over it.
Support bar spans across full width of
the joint and is supported on deck slab
or front wall of the abutment. Thus
the load from axle is transferred as a
concentrated load at the support point
of the support bar. The intensity of the
load will be function of the movement
capacity of the joint i.e. width of the
joint. For a two seal joint the load will
be shared equally at both supports.
For three seal joint the load from
separation beam near joint support
will impart around 2/3rd load to deck
support through support bar while
balance 1/3rd on deck support away
from it. It may be seen that the support
load will progressively increase as
number of seals will increase. Except
for very large capacity joints, not more
26
than one axle is expected on joint as
axle spacing is generally not less than
1m. Thus load transferred by one axle
will normally be the limiting load on
the joint. For purpose of design load,
the loads derived need to be accounted
for appropriate impact factor.
Edge beam top is necessarily in line
with wearing coat. Separation beams
are normally below the edge beams.
Load transfer point to support bar
is bottom of the separation beam.
Therefore this load transfer to deck
slab will be as a suspended load if the
main support or reinforcement in deck
slab is at its top.
Alternatively a slab or individual
support can be detailed below support
bars for direct load transfer.
Thus the modular joint has to cater
for two types of load transfers; at
formation level, akin to single strip
seal joint at edge beams and from
support bars that are at lower level
than formation level.
At abutment end normally the joint
can be supported on front wall itself,
including the box assembly. However
the width of front wall at top has to be
sufficient to accommodate the length
of the recess for the support bars. Here
the load transfer will be direct in such
case.
The modular expansion joints are
heavy assemblies. However the selfweight of the joint as a linear load will
be very small, within 2% to 3% of the
basic load from traffic. As such full
axle/wheel load with allowance for
impact may amply cover for the design
purpose.
4
CONNECTION
OF
JOINT WITH DECK
THE
4.1 Connection for Single Strip Seal
Joint
Normally a recess is left in deck slab
with a thin bottom slab at the joint
location. The edge beams of single
strip seal joint are connected to deck
slabs on either side of joint, either
through plates welded to edge beams
or studs welded to edge beams. Deck
slab reinforcement in direction of
traffic is welded to plates or the edge
beams. Transverse reinforcement in
deck slab also passes through holes
in plates or studs as the case may be.
This results in a secured connection of
the joint with the deck slab. The recess
is cast after placing and aligning the
joint.
Some designers prefer not to leave the
recess but to leave reinforcement from
slab. Bottom shutter is provided in
expansion gap and joint is assembled
in usual manner. Gap is then cast.
The reinforcement left from deck slab
has to be sufficient to cater for axle
load effects when wheels are on edge
beam. Span of the deck slab beyond
transverse diaphragm will normally
decide the reinforcement requirement
as well as dimensions of the deck
slab. Since axle can occupy any place
within the allowable limits, a uniform
reinforcement is required for deck
width for practical purposes.
4.2 Connection for Modular Strip
Seal Joint
Load transfer from modular joint is by
two ways, as explained in 4.2. At edge
beams it is essentially same as single
strip seal type joint. The detailing, as
necessary for single strip seal holds
good for the purpose.
The load transfer at support bars of the
joint is indirect. The detailing can be
either as an indirect support from deck
slab or by providing a suitable ledge or
individual support below each of the
support bars. Slab, if provided will act
as cantilever from the nearest support,
deck slab or diaphragm, as the case may
be. If individual support is provided, it
will act as bracket. For support from
deck slab the load transfer will be
indirect. Hanger reinforcement behind
and on sides of the support box housing
support bars is required to carry the
load to top reinforcement in deck sab.
This hanger reinforcement needs to
INDIAN HIGHWAYS, May 2015
TECHNICAL PAPERS
be continued below the support bar
and behind it in proper shape. If a slab
(ledge), cantilevering from diaphragm
is provided below support bars then
its reinforcement will be as for normal
cantilever below support bar level.
The major reinforcement has to be
spaced within the effective width that
will be function of the cantilever span.
In balance area nominal reinforcement
may suffice. If an individual support
is provided below each of the support
bars then the reinforcement for the
same has to be properly anchored in
diaphragm. The diaphragm has to be
detailed accordingly, for full length
if slab is provided or for local load
transfer in case of brackets.
In most of the situations the exact
details of the modular joint will not
be available at deck design stage. The
detailing with indirect load transfer to
deck slab or with direct load transfer
from ledge slab to diaphragm; both
having uniform reinforcement details
for full deck width will be simpler
and more practical. If individual
support for each of the support bar is
contemplated then the same has to be
detailed as per the specific joint.
Clashing of locations of support
bars with beams or webs is quite
possible and shall be avoided. The
recess therefore may protrude in the
beam/web as well. Careful detailing
to ensure that the adequate recess is
provided during casting/pre-casting
and does not foul in anchorage area
of prestressing tendons is absolutely
essential. This can be achieved, either
by providing the sufficient clear gap
between beam ends or by informing the
joint manufacturer the beam locations
for arranging support bar locations
accordingly. The former would be an
easier way.
It may be possible to rest the support
bars through some arrangement from
pier cap, independent of deck. This will
induce some complications in detailing
as free movement of structure has to be
ensured, unhampered by support from
pier cap. A clear gap between said
supports and deck components needs
to be ensured, an avoidable alternative
in the opinion of the author.
Typical reinforcement details (shape
and configuration) of the reinforcement
are shown in Fig. 4. Obviously the
same are one of the ways of the
detailing. Any detail allowing for
the proper load transfer from both
edge beams and support bars to
their respective support would be
satisfactory.
Fig. 4 Typical Details of Reinforcement at Modular Expansion Joint
(All Reinforcements Shown are Indicative. The Actual Diameters, Numbers, Spacing etc. Shall be as per Design Requirement)
INDIAN HIGHWAYS, May 2015
27
TECHNICAL PAPERS
5
GAPS AT THE JOINT
Normally a significant time difference
exists between installation of the joint
and completion of deck structure.
This time difference can be put to
optimize the movement requirement
of the joint as some movement due
to creep and shrinkage (generally
major portion) would have taken
place prior to installation. This
movement is necessarily permanent
and unidirectional, increasing the
gap between faces of the structures.
If reliable estimate of contraction
and expansion after installation
at design stage is possible, some
further optimization can be done.
This optimization has to be done with
extreme caution as any error can lead
to no availability of gap for expansion
in certain cases or gap beyond
capacity of the joint. As a conservative
measure, this optimization can
be ignored if fail safe approach is
desired.
Thus for modular type joint, gaps two
locations have to be determined and
provided.
5.2 Gap Between Supports Holding
Expansion Joint
The expansion joint is held in position
or connected to deck through the deck
slab overhang and supports for support
bars. The support bars always extend
on either side of edge beams due to
movement assembly. As such the gap
to accommodate joint in between
support structures is significantly
more than that at edge beams. The
recess for installing joint, normally
much later than deck construction,
shall correspond to this.
The clear gap between faces of the
deck diaphragms or the deck slabs,
as the case may be shall never be less
than the required total width of the
joint plus recess requirements.
28

Routinely followed dimensions
of projections of beams & slabs,
especially as for single strip seal
joint, recess sizes and thickness
of dirt wall of abutment etc. will
not generally suffice for modular
joint.

At modular joint the edge beams
transfer wheel loads to deck slab
directly while support bars
indirectly. The detailing for
proper load transfer needs to be
done accordingly.

The support bars can be
suspended from deck slab with
appropriate hanger reinforcement
or by directly resting them on
slab/local support below them.
Specifically detailed reinforcement is necessary at support
bar locations. The reinforcement
detailing shall cater for the
specific load transfer mechanism
of modular joint, at support boxes
and space in between them.

The gap between faces of deck
structures shall be sum of movement from temperature, creep,
shrinkage considerations plus the
width of all separation beams plus
space for providing specifically
detailed reinforcement.
Providing a little more gap than
required is always desirable as it
would provide better tolerances for
installation.
6
CONCLUSIONS

Based on length of continuous
structure the requirement of
modular joint shall be assessed at
initial design stage itself.
5.1 Gap between Edge Beams
The edge beams of the joint are also
aligned with faces of the structure,
generally deck slab. The minimum
gap has to be equal to total thickness
of the internal separation beams plus
tolerance specified by manufacturer.
The maximum gap has to be this
minimum gap plus the required
movement of the joint. Optimization
on line single seal joint is also possible.
In fact it may be more important
as modular joints are substantially
costlier.
avoiding recess along main beam
alignment.

The load transfer from modular
joint is different than other type
of joints.

Installation of the modular joint
has specific requirements and
detailing shall include the same
to avoid complication afterwards.

Support locations for support
beams can be avoided along
beams in consultation with joint
manufacturer. This will facilitate
proper housing of support boxes,
ACKNOWLEDGEMENTS
The typical details of modular joint
were provided by M/s Sanfield [India]
Ltd. and the reinforcement details
were prepared by Mr. P.N.S. Sastry
& Mr. A K Vivekananda of M/s L&T
Infrastructure Engendering Ltd. Author
sincerely thanks them for the help.
INDIAN HIGHWAYS, May 2015
Amendment to IRC:6-2014
Amendment No. 1/IRC:6-2014/January, 2015
IRC:6-2014 “Standard Specifications and Code of Practice for Road Bridges,
Section II – Loads and Stresses” (Revised Edition)
S. No.
Clause No.
For
Read
1.
204.4 Congestion For bridges, flyovers/grade separators close to
Factor
areas such as ports, heavy industries and mines and
any other areas where frequent congestion of heavy
vehicles may occur, additional check for congestion
of vehicular live load on the carriageway shall be
considered. In the absence of any stipulated value,
the congestion factor, as mentioned in Table 3
shall be considered. This factor shall be used as
multiplying factor on the global effect of vehicular
live load only. Under this condition, horizontal
force due to braking/acceleration, centrifugal action
and temperature effect need not be included, but
the effect of live load impact shall be included.
For bridges, flyovers/grade separators close to
areas such as ports, heavy industries and mines
and any other areas where frequent congestion of
heavy vehicles may occur, as may be decided by
the concerned authorities, additional check for
congestion of vehicular live load on the carriageway
shall be considered. In the absence of any stipulated
value, the congestion factor, as mentioned in
Table 3 shall be considered as multiplying factor on
the global effect of vehicular live load (including
impact). Under this condition, horizontal force
due to braking/acceleration, centrifugal action,
temperature effect and effect of transverse
eccentricity of live load shall not be included.
2.
Under Clause 201
IRC Class SV Loading: This loading is to be
adopted for design of new bridges in select corridors,
as may be decided by the concerned authorities,
where passage of trailer vehicles carrying stator
units, turbines, heavy equipment and machinery
may occur occasionally. This loading represents
a spectrum of special vehicles in the country and
should be considered for inclusion in the design
wherever applicable.
IRC Class SV Loading: This loading is to be
adopted for design of new bridges in select corridors
where passage of trailer vehicles carrying stator
units, turbines, heavy equipment and machinery
may occur occasionally. This loading represents
a spectrum of special vehicles in the country and
should be considered for inclusion in the design
where ever applicable.
Notes below Table 3.2 and Table 3.4
S. No.
3.
Clause No
For
Table 3.2, 2.5
under Variable
Loads
Read
-
Basic Combination
Accidental
Combination
Seismic
Combination
a) As leading load
1.5
-
-
b) As Accompanying load
0.9
0.5
0.5
2.5 Thermal Loads
Note No. 4
below Table 3.2
-
Thermal loads indicated, consists of either restraint effect generated by portal frame or arch or
elastomeric bearing or frictional force generated by bearings as applicable.
Note No. 10
below Table 3.4
-
Thermal loads indicated, consists of either restraint effect generated by portal frame or arch or
elastomeric bearing or frictional force generated by bearings as applicable.
S. No.
Clause No.
4.
219.1.2
For
Read
Special investigations should be carried out for the Special investigations should be carried out for the
bridges of following description:
bridges of following description:
a)
b)
c)
d)
Bridges more than 150 m span
Bridges with piers taller than 30 m in Zones IV
and V
Cable supported bridges, such as extradosed,
cable stayed and suspension bridges
Arch bridges having more than 50 m span
INDIAN HIGHWAYS, May 2015
a)
b)
c)
d)
Bridges more than 150 m span
Bridges with piers taller than 30 m in Zones IV
and V
Cable supported bridges, such as extradosed, cable
stayed and suspension bridges
Arch bridges having more than 50 m span
29
Amendment to IRC:6-2014
S. No.
Clause No.
For
Read
e)
Bridges having any of the special seismic e)
resistant features such as seismic isolators, dampers etc.
f)
f)
Bridges using innovative structural arrangements
and materials.
g)
Notes for special investigations:
1.
2.
3.
5.
Part of
Table 9 of
IRC:6
In all seismic zones, areas covered within 10
km from the known active faults are classified
as ‘Near Field Regions’. For all bridges located
within ‘Near Field Regions’, except those exempted in Clause 219.1.1, special investigations
should be carried out. The information about
the active faults should be sought by bridge authorities for projects situated within 100 km
of known epicenter as a part of preliminary
investigations at the project preparation stage.
In all seismic zones, areas covered within 10 km
from the known active faults are classified as
‘Near Field Regions’. The information about the
active faults should be sought by bridge authorities for projects situated within 100 km of known
epicenter as a part of preliminary investigations
at the project preparation stage.
For all bridges located within ‘Near Field
Regions’, except those exempted in Clause
219.1.1, special investigations should be carried
out.
Special investigations should include aspects Notes for special investigations:
such as need for site specific spectra,
1.
Special investigations should include aspects
independency of component motions, spatial
such as need for site specific spectra,
variation of excitation, need to include soilindependency of component motions, spatial
structure interaction, suitable methods of
variation of excitation, need to include
structural analysis in view of geometrical and
soil-structure interaction, suitable methods of
structural non-linear effects, characteristics and
structural analysis in view of geometrical and
reliability of seismic isolation and other special
structural non-linear effects, characteristics and
seismic resistant devices, etc.
reliability of seismic isolation and other special
Site specific spectrum, wherever its need is
seismic resistant devices, etc.
established in the special investigation, shall be
2.
Site specific spectrum, wherever its need is
used, subject to the minimum values specified
established in the special investigation, shall be
for relevant seismic zones, given in Fig. 11.
used, subject to the minimum values specified for
relevant seismic zones, given in Fig. 11.
Bridge Component
‘R’ with
Ductile
Detailing
‘R’ without
Ductile
Detailing (For
Bridges in Zone
II only)
Substructure
30
Bridges having any of the special seismic resistant
features such as seismic isolators, dampers etc.
Bridges using innovative structural arrangements
and materials.
Bridges in near field regions
Bridge Component
‘R’ with
Ductile
Detailing
‘R’ without
Ductile
Detailing (For
Bridges in
Zone II only)
Substructure
i)Masonry/ PCC
Piers/Abutments
-
1.5
i)Masonry/PCC Piers/
Abutments (where
plastic hinge cannot
develop)
1.0
1.0
ii)RCC Wall piers
and abutments
transverse
direction (where
plastic hinge can
not develop)
-
1.0
ii)RCC Wall piers
and abutments
transverse direction
(where plastic hinge
can not develop)
1.0
1.0
INDIAN HIGHWAYS, May 2015
Amendment to IRC:112-2011
Amendment No. 1/IRC:112-2011/January, 2015
IRC:112-2011 “Code of Practice for Concrete Road Bridges”
S. No.
Clause No.
& Page No.
For
Read
1.
6.4.2.7(1)
(Page 46)
Creep of concrete depends, on the stress in the
concrete, age at loading and duration of loading in
addition to the factors listed in Clause 6.4.2.6(1). As
long as the stress in concrete does not exceed 0.36
fck creep may be assumed to be proportional to the
stress.
Creep of concrete depends, on the stress in the concrete,
age at loading and duration of loading in addition to the
factors listed in Clause 6.4.2.6(1). As long as the stress
in concrete does not exceed 0.36 fcm (t0) creep may be
assumed to be proportional to the stress.
2.
6.4.2.7(2)
(Page 47)
The values given in Table 6.9 can be considered as
final creep co-efficient for design for normal weight
concrete, subject to condition that the compressive
stress does not exceed 0.36 fck at the age of loading
and mean temperature of concrete is between 10ºC
and 20ºC with seasonal variation between – 20ºC
to 40ºC. For temperature greater than 40ºC the coefficient given may be increased by 10 percent in
absence of accurate data. In case the compressive
stress exceeds 0.36 fck, at loading, non-linear creep
shall be considered.
The values given in Table 6.9 can be considered as
final creep co-efficient for design for normal weight
concrete, subject to condition that the compressive
stress does not exceed 0.36 fcm at the age of loading
and mean temperature of concrete is between 10ºC and
20ºC with seasonal variation between – 20ºC to 40ºC.
For temperature greater than 40ºC the co-efficient given
may be increased by 10 percent in absence of accurate
data. In case the compressive stress exceeds 0.36 fcm (t0),
at loading, non-linear creep shall be considered.
3.
Table No.
11.1
Note : Positional restraints are given for directions at Notes :
right angles to the member
1.
Positional restraints are given for directions at
right angles to the member.
2.
Cases 1 to 5 shows superstructure held in position
which means the deck is held in position at some
location other than the pier under consideration
(say typically either at another pier or at the
abutment).
3.
In case of any floating deck on elastomeric
bearings (simply supported or continuous), Case 7
will be applicable.
4.
For a continuous deck fixed at any pier/abutment,
Case 7 applies for the design of fixed pier/
abutment. For design of other piers in the
longitudinal direction, Case 4 applies for piers
with elastomeric bearings and Case 5 applies for
piers with free metallic bearings.
Note below
Table
(Page 114)
4.
11.3.2.2(1)
(Page 115)
Add at the end of the Clause.
The effect of imperfection may be represented by an
eccentricity in mm,
limited to 50 mm
lo is the height of pier in mm.
INDIAN HIGHWAYS, May 2015
31
Amendment/Errata to IRC:112-2011
S. No.
Clause No.
& Page No.
For
Read
5.
12.2.1(2)
(Page 120)
Where compressive stress in concrete under
quasi-permanent loads is within 0.36fck, linear creep
may be assumed. In case compressive stress exceeds
0.36fck, non-linear creep shall be considered, for
which Annexure A-2 may be referred.
Where compressive stress in concrete under
quasi-permanent loads is within 0.36 fcm(t0), linear creep
may be assumed. In case compressive stress exceeds
0.36fcm(t0), non-linear creep shall be considered.
For stress level in the range of 0.36 fcm(to) < σc ≤
0.48fcm(to) the non-linearity of creep may be taken into
account using the following equation:
ϕσ (t, to) is the non-linear creep coefficient.
ϕ (t, to) is the linear creep coefficient.
kσ =
6.
12.3.4(3)
Under
Eq. 12.9
(Page 127)
c is the clear
reinforcement.
cover
to
the
is the strength ratio.
longitudinal c is the clear cover to the longitudinal reinforcement.
Wherever the clear cover exceeds 50 mm a value of 50
mm shall be used in the calculation.
Errata No. 1/ IRC:112-2011/January, 2015
IRC:112-2011 “Code of Practice for Concrete Road Bridges”
S. No.
Clause No.
& Page No.
For
Read
1.
6.4.2.2(3)
(Page 39)
To avoid irreversible damage like local cracking (eg. due to
early age prestressing) the achievement of early age strength
shall be verified by testing. It is to be noted that the field
testing results based on small number of samples are a
measure of the mean value of early age strength and not of
the characteristic value of early age. The values thus obtained
should be reduced by 1.645 x (standard deviation for the
grade of concrete). The value of the standard deviation to be
used for early age is required to be established by testing at
least 30 numbers of samples at site, unless it is know from
past experience. Refer Section 18 for details.
To avoid irreversible damage like local cracking (eg. due
to early age prestressing) the achievement of early age
strength shall be verified by testing. Refer Section 18 for
details.
2.
11.3.1(3)
First line
(Page 115)
Stress – strain relationships for concrete given in In so far as material non-linearity is concerned, stress –
Annexure (A2.7) and for steel given in Section 6 strain relationships for concrete given in Annexure A2-7
(Fig. 6.2 and 6.4) may be used.
and for steel given in Section 6 (Fig. 6.2 and 6.4) may
be used.
3.
11.3.1(4)
Last line
(Page 115)
In the absence of more refined models, creep may be taken
into account by modifying all strain values in the concrete
stress-strain diagram using effective E value as per Clause
6.4.2.5.4 (iii).
In the absence of more refined models, creep may be
taken into account by modifying all strain values in the
concrete stress-strain diagram using effective E value as
per Clause 6.4.2.5(4) (iii).
4.
15.2.5.1(3)
(d)
Last line
(Page 156)
For splicing of bars in beams and columns the stirrups or
links provided for other considerations can be taken into
account to satisfy the requirement of (2) and its spacing shall
not exceed 150 mm.
For splicing of bars in beams and columns the stirrups
or links provided for other considerations can be taken
into account to satisfy the requirement of (b) and their
spacing shall not exceed 150 mm.
5.
15.2.5.6.1 (10)
n1 = 1 and n2 = 2
n1 = 2 and n2 = 2
Fig 15.6 (under
RHS sketch)
(Page 160)
32
INDIAN HIGHWAYS, May 2015
Errata to MORD Specifications for Rural Roads (First Revision) - 2014
ERRATA NO. 1 TO “MORD SPECIFICATIONS FOR RURAL ROADS (FIRST REVISION)”- 2014
Page 81 Title of Table 400.3 - A “Grading Requirements for Surface Gravel” may be read as:
“Grading Requirements of Gravel for Surface Course”
Page 89 Table 400.5 may be read as:
Table 400.5 Grading Limits of Material for Stabilisation with Cement
IS Sieve
Percent by Weight Passing Within the Range
Sub-Base/Base
53.0 mm
100
37.5 mm
95-100
19.0 mm
45-100
9.5 mm
35-100
4.75 mm
25-100
600 micron
8-65
300 micron
5-40
75 micron
0-10
Page 93 Table 400.8 may be read as:
Table 400.8 Grading Requirements of Course Aggregates
Size Range
1)
2)
3)
90 mm to 45 mm
63 mm to 45 mm
53 mm to 22.4 mm
INDIAN HIGHWAYS, May 2015
IS Sieve Number
Passing Percent by Weight
125 mm
100
90 mm
90-100
63 mm
25-60
45 mm
0-15
22.4 mm
0-5
75 mm
100
63 mm
90-100
53 mm
25-75
45 mm
0-15
22.4 mm
0-5
63 mm
100
53 mm
95-100
45 mm
65-90
22.4 mm
0-10
11.2 mm
0-5
33
Errata to MORD Specifications for Rural Roads (First Revision) - 2014
Page 115 Table 400.19 may be read as:
Bold font of last two rows to be converted to normal font.
Page 140 Table 500.6 may be read as:
Table 500.6 Grading Requirements for Aggregates used for Surface Dressing
IS Sieve Designation
(mm)
Cumulative Percent by Weight of Total Aggregates Passing for the
Following Nominal Sizes (mm)
19
13
10
6
26.5
100
-
-
-
19.0
85-100
100
-
-
13.2
0-40
85-100
100
-
9.5
0-7
0-40
85-100
100
6.3
-
0-7
0-35
85-100
4.75
-
-
0-10
-
3.35
-
-
-
0-35
2.36
0-2
0-2
0-2
0-10
0.60
-
-
-
0-2
0.075
0-1.5
0-1.5
0-1.5
1-5
Passing 9.5 mm,
retained 6.3 mm
Passing 6.3 mm retained
on 3.35 mm
Minimum 65% by weight Passing 19 mm, retained Passing 13.2 mm,
of aggregate
13.2 mm
retained 9.5 mm
Page 144 Table 500.9 may be read as:
Quantity of Binder for a)
9.5 kg
b)
5.1 kg
Page 165 Table 500.20 may be read as:
Table 500.20 Properties of Modified Bitumen
Highest Mean Air Temperature
< 20ºC
20ºC to 35ºC
Above 35ºC
Lowest Mean Air Temp
>.10 < .10
>-10≤10
>.10
Sl.
No.
Characteristics
(1)
(2)
Specified values for the bitumen
Method of IS
Ref to
No.
Annexure
(3)
(4)
(5)
(6)
60 to 120
50 to 80
30 to 50
1203
(7)
i)
Penetration at 25ºC, 0.1 mm, 100g, 5s
ii)
Softening point, (R&B), ºC, Min.
50
55
60*
1205
-
iii)
FRAASS* breaking point, ºC, Max.
-20
-16
-12
9381
-
iv)
Flash Point, COC, ºC, Min.
220
220
220
1209
-
v)
Elastic recovery of half thread in
ductilometer at 15ºC, percent, min.
50
60
60
2
vi)
Complex modulus (G*/sin δ) as Min 1.0
kPa at 10 rad/s, at a temperature, ºC
58
70
76
1
34
INDIAN HIGHWAYS, May 2015
Errata to MORD Specifications for Rural Roads (First Revision) - 2014
vii) Separation, difference in softening point
(R&B), ºC, Max.
viii) Viscosity at 150ºC, Poise
ix)
Thin film oven test and tests on residue:
a) Loss in mass, percent, Max.
b) Increase in softening point, ºC,
Max.
c) Reduction in penetration of
residue, at 25ºC, percent, Max.
d) Elastic recovery of half thread in
ductilometer at 25ºC, percent, Min.
Or
Complex modulus as (G**/sin δ as Min
2.2 kPa at 10 rad/s, at temperature ºC
3
3
3
3
1-3
3-6
5-9
1206 (Part 2)
-
1.0
7
1.0
6
1.0
5
9382
1205
-
35
35
35
1203
-
35
50
50
-
4
58
70
76
-
1
* Fraass breaking point requirement will be applicable for areas of subzero temperatures
** Where max temperature exceeds 40ºC, Softening Point should be 65ºC
INDIAN HIGHWAYS, May 2015
35
Ministry of Road Transport & Highways Circular
36
INDIAN HIGHWAYS, May 2015
Ministry of Road Transport & Highways Circular
Circulars and Annexures are available on Ministery’s Website (www.morth.nic.in) and same are also available in Ministery’s Library.
INDIAN HIGHWAYS, May 2015
37
38
INDIAN HIGHWAYS, May 2015
INDIAN HIGHWAYS, May 2015
39
40
INDIAN HIGHWAYS, May 2015
INDIAN HIGHWAYS, May 2015
41
42
INDIAN HIGHWAYS, May 2015
INDIAN HIGHWAYS, May 2015
43
44
INDIAN HIGHWAYS, May 2015
INDIAN HIGHWAYS, May 2015
45
46
INDIAN HIGHWAYS, May 2015
INDIAN HIGHWAYS, May 2015
47