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Abstract:
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The country has witnessed tremendous increase in the vehicle population and increased
axle loading pattern during the last decade, leaving its road network overstressed and
leading to premature failure. The type of deterioration present in the pavement should be
considered for determining whether it has a functional or structural deficiency, so that
appropriate overlay type and design can be developed. Structural failure arises from the
conditions that adversely affect the load carrying capability of the pavement structure.
Inadequate thickness, cracking, distortion and disintegration cause structural deficiency.
Functional deficiency arises when the pavement does not provide a smooth riding surface
and comfort to the user. This can be due to poor surface friction and texture, hydro
planning and splash from wheel path, rutting and excess surface distortion such as
potholes, corrugation, faulting, blow up, settlement, heaves etc. Functional condition
determines the level of service provided by the facility to its users at a particular time and
also the Vehicle Operating Costs (VOC), thus influencing the national economy.
Prediction of the pavement deterioration is helpful to assess the remaining effective
service life (RSL) of the pavement structure on the basis of reduction in performance
levels, and apply various alternative designs and rehabilitation strategies with a long
range funding requirement for pavement preservation. In addition, they can predict the
impact of treatment on the condition of the sections. The infrastructure prediction models
can thus be classified into four groups, namely primary response models, structural
performance models, functional performance models and damage models.
The factors affecting the deterioration of the roads are very complex in nature and vary
from place to place. Hence there is need to have a thorough study of the deterioration
mechanism under varied climatic zones and soil conditions before arriving at a definite
strategy of road improvement. Realizing the need for a detailed study involving all types
of roads in the state with varying traffic and soil conditions, the present study has been
attempted.
This study attempts to identify the parameters that affect the performance of roads and to
develop performance models suitable to Kerala conditions. A critical review of the
various factors that contribute to the pavement performance has been presented based on
the data collected from selected road stretches and also from five corporations of Kerala.
These roads represent the urban conditions as well as National Highways, State Highways
and Major District Roads in the sub urban and rural conditions.
This research work is a pursuit towards a study of the road condition of Kerala with
respect to varying soil, traffic and climatic conditions, periodic performance evaluation of
selected roads of representative types and development of distress prediction models for
roads of Kerala. In order to achieve this aim, the study is focused into 2 parts. The first
part deals with the study of the pavement condition and subgrade soil properties of urban
roads distributed in 5 Corporations of Kerala; namely Thiruvananthapuram, Kollam,
Kochi, Thrissur and Kozhikode. From selected 44 roads, 68 homogeneous sections were
studied. The data collected on the functional and structural condition of the surface
include pavement distress in terms of cracks, potholes, rutting, raveling and pothole
patching. The structural strength of the pavement was measured as rebound deflection
using Benkelman Beam deflection studies. In order to collect the details of the pavement
layers and find out the subgrade soil properties, trial pits were dug and the in-situ field
density was found using the Sand Replacement Method. Laboratory investigations were
carried out to find out the subgrade soil properties, soil classification, Atterberg limits,
Optimum Moisture Content, Field Moisture Content and 4 days soaked CBR. The relative
compaction in the field was also determined. The traffic details were also collected by
conducting traffic volume count survey and axle load survey.
From the data thus collected, the strength of the pavement was calculated which is a
function of the layer coefficient and thickness and is represented as Structural Number
(SN). This was further related to the CBR value of the soil and the Modified Structural
Number (MSN) was found out. The condition of the pavement was represented in terms
of the Pavement Condition Index (PCI) which is a function of the distress of the surface at
the time of the investigation and calculated in the present study using deduct value
method developed by U S Army Corps of Engineers. The influence of subgrade soil type
and pavement condition on the relationship between MSN and rebound deflection was
studied using appropriate plots for predominant types of soil and for classified value of
Pavement Condition Index. The relationship will be helpful for practicing engineers to
design the overlay thickness required for the pavement, without conducting the BBD test.
Regression analysis using SPSS was done with various trials to find out the best fit
relationship between the rebound deflection and CBR, and other soil properties for
Gravel, Sand, Silt & Clay fractions.
The second part of the study deals with periodic performance evaluation of selected road
stretches representing National Highway (NH), State Highway (SH) and Major District
Road (MDR), located in different geographical conditions and with varying traffic. 8
road sections divided into 15 homogeneous sections were selected for the study and 6 sets
of continuous periodic data were collected. The periodic data collected include the
functional and structural condition in terms of distress (pothole, pothole patch, cracks,
rutting and raveling), skid resistance using a portable skid resistance pendulum, surface
unevenness using Bump Integrator, texture depth using sand patch method and rebound
deflection using Benkelman Beam. Baseline data of the study stretches were collected as
one time data. Pavement history was obtained as secondary data. Pavement drainage
characteristics were collected in terms of camber or cross slope using camber board
(slope meter) for the carriage way and shoulders, availability of longitudinal side drain,
presence of valley, terrain condition, soil moisture content, water table data, High Flood
Level, rainfall data, land use and cross slope of the adjoining land. These data were used
for finding out the drainage condition of the study stretches.
Traffic studies were conducted, including classified volume count and axle load studies.
From the field data thus collected, the progression of each parameter was plotted for all
the study roads; and validated for their accuracy. Structural Number (SN) and Modified
Structural Number (MSN) were calculated for the study stretches. Progression of the
deflection, distress, unevenness, skid resistance and macro texture of the study roads were
evaluated. Since the deterioration of the pavement is a complex phenomena contributed
by all the above factors, pavement deterioration models were developed as non linear
regression models, using SPSS with the periodic data collected for all the above road
stretches. General models were developed for cracking progression, raveling progression,
pothole progression and roughness progression using SPSS. A model for construction
quality was also developed.
Calibration of HDM–4 pavement deterioration models for local conditions was done
using the data for Cracking, Raveling, Pothole and Roughness. Validation was done
using the data collected in 2013. The application of HDM-4 to compare different
maintenance and rehabilitation options were studied considering the deterioration
parameters like cracking, pothole and raveling. The alternatives considered for analysis
were base alternative with crack sealing and patching, overlay with 40 mm BC using
ordinary bitumen, overlay with 40 mm BC using Natural Rubber Modified Bitumen and
an overlay of Ultra Thin White Topping. Economic analysis of these options was done
considering the Life Cycle Cost (LCC). The average speed that can be obtained by
applying these options were also compared. The results were in favour of Ultra Thin
White Topping over flexible pavements. Hence, Design Charts were also plotted for
estimation of maximum wheel load stresses for different slab thickness under different
soil conditions. The design charts showed the maximum stress for a particular slab
thickness and different soil conditions incorporating different k values. These charts can
be handy for a design engineer.
Fuzzy rule based models developed for site specific conditions were compared with
regression models developed using SPSS. The Riding Comfort Index (RCI) was
calculated and correlated with unevenness to develop a relationship. Relationships were
developed between Skid Number and Macro Texture of the pavement.
The effort made through this research work will be helpful to highway engineers in
understanding the behaviour of flexible pavements in Kerala conditions and for arriving
at suitable maintenance and rehabilitation strategies.
Key Words: Flexible Pavements – Performance Evaluation – Urban Roads – NH – SH
and other roads – Performance Models – Deflection – Riding Comfort Index – Skid
Resistance – Texture Depth – Unevenness – Ultra Thin White Topping |