A Novel Neural Network Based Method for Analysis of Pavement Deflection Data

Abstract

Efficient management of road infrastructure involves planning, construction, maintenance, operation and disposal of road assets. Knowledge of current conditions and deterioration of road pavements are essential to enable effective road asset management. This paper presents a novel neural network based method for the analysis and prediction of Falling Weight Deflectometer (FWD) parameters based on Traffic Speed Deflectometer (TSD) parameters. A neural network based method was designed and applied to analyse the correlation between FWD and TSD data. The method used a feed-forward neural network that was trained with TSD data as an input and FWD data as an output. The proposed method was evaluated on TSD and FWD data provided by the industry partner Australian Road Research Board (ARRB). The prediction results are very promising and within an acceptable range set by the industry partner. A detailed results analysis is included in this paper.