Multi-objective optimisation of in-service asphalt pavement maintenance schedule considering system reliability estimated via LSTM neural networks

Jiyu Xin, Mitsuyoshi Akiyama*, Dan M. Frangopol, Mingyang Zhang

*この研究の対応する著者

研究成果: Article査読

抄録

This article presents a novel system reliability-based framework for multi-objective optimisation of preventive maintenance (PM) management of in-service asphalt pavement. To accurately predict the international roughness index (IRI) sequence of pavement sections, a long short-term memory (LSTM) neural network that considers the spatiotemporal correlations between IRI sequences is trained with data retrieved from the long-term pavement performance (LTPP) program. Based on time-dependent limit-state functions (LSFs) incorporating the uncertainty associated with LSTM neural network prediction and the observational error involved in IRI measurement, Monte Carlo simulation (MCS) with importance sampling (IS) is adopted to calculate the reliability of pavement sections. Pavement sections located in New Mexico and Montana are selected as illustrative examples. Tri-objective optimisation processes are investigated by maximising user benefits (i.e. improved system reliability) and agency benefits (i.e. extended service life) while minimising the associated life-cycle cost (LCC) (i.e. user and agency costs) with multi-objective genetic algorithms (GAs). The obtained Pareto solution sets may assist decision-makers in the selection of well-balanced solutions to identify the optimal timing for applying PM treatments to in-service asphalt pavement.

本文言語English
ページ(範囲)1002-1019
ページ数18
ジャーナルStructure and Infrastructure Engineering
18
7
DOI
出版ステータスPublished - 2022

ASJC Scopus subject areas

  • 土木構造工学
  • 建築および建設
  • 安全性、リスク、信頼性、品質管理
  • 地盤工学および土木地質学
  • 海洋工学
  • 機械工学

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