Prediction of the need for maintenance of rigid pavements using finite element models and artificial neural networks

Abstract

Abstract This work is concerned with the simulation of pavement behavior using the Finite Element Method (FEM) with the goal of predicting the appearance of cracks during its useful life. The developed method can help maintenance planning towards providing users with safety and comfort. The effects of both traffic loads and temperature variations are considered. Artificial Intelligence (AI) tools are adopted to reduce the time necessary for the interpretation of simulation results for the development of an efficient pavement management system. The developed rigid pavement management system uses the Artificial Neural Networks (ANN) technique for the prediction of both pavement response to fatigue accumulation and the behavior of the modeled pavement with a high degree of precision. The networks showed 4.5% and 3.6% square errors for positive and negative temperature gradients and 95% and 97% data correlation, respectively. The SR stress ratio obtained by the developed ABAQUS model is 0.614 and the value provided by the pavement management system is 0.648. The developed methodology was applied to a section of highway BR-101/NE, between the states of Paraiba PR and Pernambuco PE, in Brazil. Comparing the results with the limits provided by National Cooperative Highway Research Program (NCHRP) for traffic in the study, this pavement would require maintenance every six months. The maximum values of tensile stress due to bending were obtained by combining the positive temperature gradient with the traffic axis load at the edge during the summer months (December to March) due to the higher thermal gradient values.

Keywords

• ANN
• FEM
• rigid pavement
• temperature gradient