Development of Pavement Distress Models for Khulna – Jessore Highway in Bangladesh

Abstract

General function of a pavement is to provide a safe and comfortable riding surface to road users. Pavements represent an important infrastructure to all countries. However, pavement distress is major problems faced by road users. This pavement requires great care through conducting periodic evaluation and timely maintenance to keep the pavement operating under acceptable level of service. An adequate assessment of pavement distresses is necessary before carrying out maintenance and rehabilitation. Since maintenance of pavement is a vital and continuous activity, maintenance shall be done effectively to avoid any reoccurrence and repeatedly works.Thus, in fulfill and meet pavement goals,the aim of this study is to determine pavement distress model for effective maintenance works in term of cost, quality and time at National Highway. These models allow pavement authorities to predict the deterioration of the pavements and consequently determine the maintenance needs and activities, predicting the timing of maintenance or rehabilitation, and estimating the long range funding requirements for preserving the performance of the network.In this study, data of pavement distresses on the Khulna - Jessore National Highway (N7) were collected.Visual distress assessment using measuring tape, ruler and camera were carried out on selected test sections of that highway in order to have better understanding of the pavement response.These data were categorized, processed, and analyzed. These data have been employed to generate pavement distress models by statistical software SPSS. Throughout the study, the most common types of pavement distress on the national highway have been identified. The behavior of these distress types has been investigated.A S-curve regression equation was found to be an excellent representation of the data. Six for national highway pavement distress models (NHPDM) have been developed.The developed models provide a reasonable prediction of pavement condition. The models were assessed by regression analysis.The implementation of the models for maintenance work has also been proposed.