FLEXIBLE PAVEMENT PERFORMANCE FOR LOW VOLUME ROADS PDF

FLEXIBLE PAVEMENT PERFORMANCE FOR LOW VOLUME ROADS PDF

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Development of an intelligent flexible pavement performance model is the need of
implementing organizations to prioritize pavement maintenance and rehabilitation works,
as this involves cost economics. Pavement performance model, is an equation that relates
to some extrinsic ‘time factor’ (age or number of load applications) to a combination of
intrinsic factors (structural responses, etc.) and performance indicators which simulate the
deterioration process of pavement condition and provide forecasting of pavement
condition over a period of time. These pavement performance or deterioration models
play a pivotal role in pavement management systems. To develop these models, structural
and functional response measurement of 18 sections of low volume pavements were
carried out for two years continuously in Uttarakhand and Uttar Pradesh states of India.
Statistical analysis tools and Artificial Neural Network (ANN) are used to develop the
models. Statistical performance indicators and logical relationships between input
parameters and output parameters are used to select the best fit model. Polynomial
relationship best relates to the input parameters such as pavement age, CBR of subgrade,
traffic, pavement thickness and also to the output parameters i.e. pavement condition
indicators such as deflection, riding quality, surface roughness. Paired t-test is also
carried out for the validation purpose of chosen best fit models.

KEY WORDS: Deflection, Riding Quality, Regression Analysis, Artificial Neural
Network (ANN), Pavement Deterioration Model.

INTRODUCTION
Efficient road transportation plays a vital role in the economy of any nation. Road
transport in India occupies a dominant position in the overall transportation system of the
country due to its advantages like easy availability, flexibility of operation, door to door
service and reliability. India owns the second largest network of roads in the world, next
to the USA. As per statistics of year 2009, the total road length in the country is over 3.3
million km, which gives the spatial road density of about 1 km/sq. km. of area. Out of the
total road network of India, village and other roads (Low Traffic Volume Roads) consist
of 80% of the share. Low traffic volume roads are mainly rural roads in India carrying
daily traffic less than 450 Commercial Vehicles per Day (CVPD) (1). Cross sectional
view of Low Volume Roads in India is given in Fig. 1. The thickness of sub-base layer is
around 200 mm and the total thickness of each layer of base course varies from 100 – 120
mm. Surface course is usually 20 mm thick premix carpet layer (closely graded aggregate
and asphalt mixed before laying, i.e. material premixed). Shoulder materials consists of
earthen material compacted according to standard Proctor compaction. Low volume roads
serve as one of the key infrastructure works, placed for integrated rural development,
Rastogi, Kumar and Gupta which has become a matter of growing urgency for considerations of social justice, national integration and economic uplift of the rural areas. The importance of preserving a road network in good condition is widely recognized and therefore, performance
evaluation of the existing roads is an absolute necessity.





FIGURE
Cross-sectional detail of low volume roads in India
Performance of flexible pavements has long been recognized as an important parameter
in their design and maintenance. In order to measure and prepare a model for pavement
performance, it is necessary to clearly define the pavement performance. According to
American Association of State Highway and Transport Officials (AASHTO) (2),
pavement performance is defined as “the serviceability trend of pavement over a design
period of time, where serviceability indicates the ability of the pavement to serve the
demand of the traffic in the existing conditions”. In other words, pavement performance
can be obtained by observing its structural and functional performance or predicting the
serviceability of a pavement from its initial service time to the desired evaluation time.
Normally, pavement condition can be evaluated on the basis of four aspects i.e. riding
quality, surface distress, structural capacity and skid resistance.
Deterioration of pavement can be attributed to various factors like age, traffic,
environment, material properties, pavement thickness, strength of pavement as well as
subgrade properties which affect the mechanical characteristics of a pavement. These
factors affect the performance of the pavement in a complex manner. To understand the
mechanism and to forecast the future condition of pavement, these deterioration models
are necessary. Pavement deterioration model is a mathematical relationship between the
pavement condition and the factors listed above. The pavement deterioration model
predicts the future condition of the pavement, which is helpful in development of
Maintenance Management Models (3, 4, 5, and 6).
Due to constrained budget and increasing challenges in pavement maintenance and
rehabilitation, Pavement Management System (PMS) has become a very beneficial
management tool for highway maintenance agencies. Pavement deterioration model,
which acts as the hub of analysis component, is the engine of whole management activity.
The pavement deterioration model is the very essence of a pavement management system
and is used to determine several fundamentals, including:

•  Rate of asset degradation at both micro (project) and macro (network) level,
•  Valuation of road assets (service life remaining), and
•  Road user costs, including the vehicle operating costs, incurred by the public.

Pavement deterioration models are developed in the present study to evaluate and predict
the condition of low volume roads and based on pavement condition, maintenance
priority model is suggested in this study. Deflection and riding quality (in International
Roughness Index) are considered as pavement condition indicators and pavement age,
traffic, CBR of soil subgrade and pavement thickness are considered as independent
parameters for the prediction of the same. Pavement material quality is not taken as an
independent parameter due to the homogenous material used for construction of these
roads. The pavement material used was according to the norms and requirements of
Indian practice code IRC: SP: 20-1997 (7). Deflection data was collected using
Benkelman Beam and ride quality (roughness) measurements were done using MERLIN
for two years on the same section of the pavement. After data collection linear and nonlinear
regression analysis were carried out using DATAFIT statistical software package.
Artificial Neural Network model is also developed using MATLAB.


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