Prediction of asphalt pavement condition using FWD deflection basin parameters and artificial neural networks

Abstract

Applications of non-destructive testing devices such as Falling Weight Deflectometer (FWD) provide crucial estimates of pavement health that assist in the optimisation of pavement management systems. However, regularly conducting these tests at a network level and post-processing of the collected data is cumbersome, which requires technical expertise, significant time, funds, and other resources. Due to this structural aspect of pavements during the selection of maintenance or repair, decisions are often ignored. This study attempts to develop reliable correlations for estimates of two different deflection basin parameters using a number of structural, functional, environmental, and subgrade soil attributes as input. The data has been obtained through field tests over a 124 km long pavement network. Different artificial neural network-based models are trained by varying the number of hidden layers and neurons in these layers, for the above-mentioned purpose. The coefficient of determination and mean square error is decisive for the selection of best network architecture. These outcomes are also compared to the results of the classical multiple linear regression method, and the superiority of neural networks over non-intelligent approaches for non-linear problems of pavement engineering is appreciated. In addition to this, the results justify the fact that the properties of the asphalt layer predominantly impact the entire pavement condition. The proposed approach is an alternative way to facilitate quick pavement condition assessment by reducing the frequency of deflection testing without compromising with the accuracy of its estimates. It would encourage the increased application of structural condition data in pavement maintenance and rehabilitation necessities with ease. However, the study does not intend to completely avoid conducting deflection testing and serve as a base for future studies.