Pavement Deterioration and Maintenance Model for Low Volume Roads

Abstract

Development of an intelligent flexible pavement deterioration and maintenance management model is the need of implementing organizations to prioritize pavement maintenance and rehabilitation works, as this involves cost economics. Pavement deterioration 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 deterioration or performance 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 continuous two years 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. Maintenance Priority Index (MPI) is developed using three parameters named as deflection, riding quality and traffic to decide the priority.