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

Jiyu Xin, Mitsuyoshi Akiyama and Dan M. Frangopol

Reliability Engineering and System Safety, 2023, vol. 238, issue C

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.

Keywords: Asphalt pavement; Multi-objective maintenance optimization; Sustainability; Risk; Multiple performance indicators; Deep neural network (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832023003629
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:238:y:2023:i:c:s0951832023003629

DOI: 10.1016/j.ress.2023.109448

Access Statistics for this article

Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares

More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu ().