基于改进粒子群算法的地铁隧道维护策略优化
顾亦宁1艾青1袁勇2
Optimization of Metro Tunnel Maintenance Strategy Based on Improved Particle Swarm Algorithm
GU Yining1AI Qing1YUAN Yong2
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作者信息:1.上海交通大学船舶海洋与建筑工程学院， 200240， 上海
2.同济大学地下建筑与工程系， 200092， 上海
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Affiliation:1.School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, 200240, Shanghai, China
2.Department of Geotechnical Engineering, Tongji University, 200092, Shanghai, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2024.01.008
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中图分类号/CLCN:U457
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栏目/Col:学术专论
摘要:
［目的］维护策略是影响地铁隧道全寿命期服役性能和维护成本的关键因素，因此需研究更适宜的维护策略优化算法。［方法］建立了基于Gamma过程的隧道服役性能退化模型，并对检查计划和维修行为进行了参数化假设；针对维护策略优化数学模型中的随机性问题，提出了一种改进粒子群算法，并与网格枚举法对比验证了该算法的有效性；分析了不同预防性维修阈值和初始检查时间间隔对维护成本的影响。［结果及结论］改进粒子群算法可提升地铁隧道维护策略优化的计算效率；与初始检查时间间隔相比，地铁隧道全寿命周期维护成本对预防性维修阈值更加敏感。
Abstracts:
［Objective］ Maintenance strategy is the key factor influencing the lifetime service performance and maintenance cost of metro tunnel. Therefore, it is essential to develop more suitable optimization algorithms for maintenance strategies. ［Method］ A tunnel service performance degradation model is established based on Gamma process, and parametric assumptions are made for inspection schedule and maintenance activities. The improved particle swarm optimization (PSO) algorithm is proposed to solve the stochastic problem in maintenance strategy optimization mathematical model, and its effectiveness is verified through a comparison with gridded enumeration algorithm. The impact of different preventive maintenance thresholds and initial inspection time intervals on maintenance costs is analyzed. ［Result & Conclusion］ The improved PSO algorithm enhances the computational efficiency of optimizing metro tunnel maintenance strategies. Compared to the initial inspection time interval, the lifetime maintenance cost of metro tunnel is more sensitive to preventive maintenance threshold.
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