Sustainability-informed management optimization of asphalt pavement considering risk evaluated by multiple performance indicators using deep neural networks

Abstract

Sustainability considerations throughout the entire pavement life-cycle in the decision-making process under uncertainty are needed to achieve optimal pavement management from the perspective of the economy, environment, and society. A novel sustainability-informed management optimization of asphalt pavement is presented in this study. First, a deep neural network (DNN) model is trained using the Long-Term Pavement Performance (LTPP) database to learn the nonlinear and complex relationships among multiple performance indicators of asphalt pavement (i.e., the international roughness index (IRI), rut depth, and alligator and transverse cracking) and their associated parameters (i.e., the climate, traffic, and pavement structure and properties). Based on the multiple time-dependent limit-state functions incorporating the uncertainties associated with these parameters, the DNN model prediction, and the IRI measurement, Monte Carlo simulation is conducted to estimate the system failure probability of asphalt pavement. Finally, a genetic algorithm-based tri-objective optimization is utilized to find the optimal maintenance and rehabilitation actions that reduce the extent of detrimental economic, environmental, and social consequences during the pavement's life-cycle. The capabilities of the proposed approach are illustrated using LTPP asphalt pavement sections in Pennsylvania and Florida, USA.