DEVELOPMENT OF DETERIORATION MODEL USING ARTIFICIAL NEURAL NETWORK

Abstract

Artificial intelligence techniques have produced excellent results in many diverse fields of engineering. Techniques such as artificial neural network (ANN) found their way into transportation engineering. The increased interest in artificial neural networks (ANNs) seen in government and private research as well as business and industry has included relatively little activity in transportation engineering. Artificial neural networks (ANNs) have proven to be an ii-nportant development in a variety of problem solving areas. Increasing research activity in (ANN) applications has been accompanied by equally rapid growth in.the commercial mainstream use of ANNs. The basic reason lies in the fact that neural networks are able to capture complex relationships and learn from examples and also able to adapt when new data become available Pavement deterioration models, which simulate the deterioration process of pavement conditions and provide forecasting of pavement condition over time, play a pivotal role in pavement management systems. Good pavement management systems require the collection of substantial amounts of road conditions data over time, to assist in the development of pavement deterioration models. Pavement management system also required proper methodology to decide the priority after evaluating the road condition of a network.To develop pavement deterioration model, deflection and roughness measurements on of last two years were taken on 18 sections of PMGSY roads of Uttarakhand and Uttar Pradesh states. Using this data set of pavement performance indicator, pavement deterioration model is developed. Artificial Neural Network (ANN) was used to develop the models. These models can predict the deflection and roughness value of a road at a given data of pavement age, CBR of subgrade, traffic and Pavement thickness. Maintenance Priority Index was also calculated using three parameters named as deflection, roughn, : s and traffic to decide the priority of road for maintenance work.