A Bridge Maintenance Decision-Making Method Considering Non-Homogeneous Deterioration and Cost Discounting

LI Guangtao; LIU Luqi; SHI Peng; LAN Chengming

doi:10.3724/j.gyjzG26031401

Volume 56 Issue 5

May 2026

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LI Guangtao, LIU Luqi, SHI Peng, LAN Chengming. A Bridge Maintenance Decision-Making Method Considering Non-Homogeneous Deterioration and Cost Discounting[J]. INDUSTRIAL CONSTRUCTION, 2026, 56(5): 187-200. doi: 10.3724/j.gyjzG26031401

Citation:

LI Guangtao, LIU Luqi, SHI Peng, LAN Chengming. A Bridge Maintenance Decision-Making Method Considering Non-Homogeneous Deterioration and Cost Discounting[J]. INDUSTRIAL CONSTRUCTION, 2026, 56(5): 187-200. doi: 10.3724/j.gyjzG26031401

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A Bridge Maintenance Decision-Making Method Considering Non-Homogeneous Deterioration and Cost Discounting

doi: 10.3724/j.gyjzG26031401

1. Research Institute of Urbanization and Urban Safety, School of Future Cities, University of Science and Technology Beijing, Beijing 100083, China;
2. National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 102206, China

Received Date: 2026-03-14
Available Online: 2026-06-06
Publish Date: 2026-05-20

Abstract

Abstract

To meet the demand for optimizing long-term maintenance decisions in intelligent bridge operation and maintenance， considering the characteristics of time-varying bridge deterioration under a finite horizon， financial discounting of maintenance costs， and difficulty in long-term reward propagation， a life-cycle maintenance decision model incorporating non-homogeneous deterioration and discounting effects was developed. The bridge deterioration process was characterized by non-homogeneous Markov state transitions. Based on discrete health states and maintenance actions， maintenance costs and risk costs were integrated into a unified cost function， while cash-flow discounting was introduced into the decision-making process. To address the limitations of conventional reinforcement learning methods in handling finite-horizon stage-wise decision tasks， non-homogeneous state transitions， and unstable training， a reinforcement learning framework combining state augmentation and backward curriculum learning was proposed. Expanded state representation enhanced the policy’s capability to capture finite-horizon characteristics， while backward curriculum learning gradually extended the training interval to improve learning stability and convergence efficiency. Numerical results demonstrated that the proposed method effectively adapted to finite-horizon maintenance decision problems under non-homogeneous bridge deterioration and achieved favorable performance in both policy quality and training stability， thereby providing methodological support for maintenance planning in life-cycle bridge operation and maintenance management.

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References(22)

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