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A Bridge Maintenance Decision-Making Method Considering Non-Homogeneous Deterioration and Cost Discounting
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Guangtao LI1, Luqi LIU2, Peng SHI2, Chengming LAN1
Industrial Construction | 2026, 56(5) : 187 - 200
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Industrial Construction | 2026, 56(5): 187-200
A Bridge Maintenance Decision-Making Method Considering Non-Homogeneous Deterioration and Cost Discounting
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Guangtao LI1, Luqi LIU2, Peng SHI2, Chengming LAN1
Affiliations
  • 1Research Institute of Urbanization and Urban Safety, School of Future Cities, University of Science and Technology Beijing, Beijing100083, China
  • 2National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing102206, China
Published: 2026-05-20 doi: 10.3724/j.gyjzG26031401
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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.

intelligent bridge operation and maintenance  /  maintenance decision-making  /  non-homogeneous Markov process  /  reinforcement learning  /  backward curriculum learning
Guangtao LI, Luqi LIU, Peng SHI, Chengming LAN. 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
Year 2026 volume 56 Issue 5
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doi: 10.3724/j.gyjzG26031401
  • Receive Date:2026-03-14
  • Online Date:2026-06-25
  • Published:2026-05-20
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  • Received:2026-03-14
Affiliations
    1Research Institute of Urbanization and Urban Safety, School of Future Cities, University of Science and Technology Beijing, Beijing100083, China
    2National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing102206, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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