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    林涛, 贾磊, 董优, 张佳鑫. 基于多属性效用的桥梁综合性能维养决策算法[J]. 应用基础与工程科学学报, 2023, 31(5): 1278-1296. DOI: 10.16058/j.issn.1005-0930.2023.05.017
    引用本文: 林涛, 贾磊, 董优, 张佳鑫. 基于多属性效用的桥梁综合性能维养决策算法[J]. 应用基础与工程科学学报, 2023, 31(5): 1278-1296. DOI: 10.16058/j.issn.1005-0930.2023.05.017
    LIN Tao, JIA Lei, DONG You, ZHANG Jiaxin. Bridge Optimal Maintenance Using the Multi-attribute Utility Assessment Method[J]. Journal of Basic Science and Engineering, 2023, 31(5): 1278-1296. DOI: 10.16058/j.issn.1005-0930.2023.05.017
    Citation: LIN Tao, JIA Lei, DONG You, ZHANG Jiaxin. Bridge Optimal Maintenance Using the Multi-attribute Utility Assessment Method[J]. Journal of Basic Science and Engineering, 2023, 31(5): 1278-1296. DOI: 10.16058/j.issn.1005-0930.2023.05.017

    基于多属性效用的桥梁综合性能维养决策算法

    Bridge Optimal Maintenance Using the Multi-attribute Utility Assessment Method

    • 摘要: 近年来,我国交通路网迅猛发展,桥梁结构作为其中的重要载体,不仅要满足技术安全,也要满足社会和经济发展的要求,亟需对桥梁的综合性能进行有效准确地评估,并选取对应维养策略以保证其维养周期内综合性能处于最优状态.首先提出基于模糊评价的多属性效用综合评估法(MAUFE),对桥梁养护规划中的多种性能评估指标进行多属性效用分析;并结合马尔可夫模型、LSTM算法建立桥梁退化过程预测模型;而后建立智能维养决策框架保证维养周期内MAUFE评估结果的最优.评估模型以多属性效用函数将桥梁技术状况评分、全寿命周期费用及可持续性指标转化为反映综合性能的效用值,采用层次分析法和熵权法权重进行融合权重计算(AHP-EW),结合模糊分析理论计算指标隶属度并完成综合性能评估;根据桥梁退化实际观测数据结合数据驱动手段对桥梁性能进行准确预测;建立指定目标及约束下桥梁全寿命维养决策数学模型,采用遗传算法进行求解.最终,选取代表性桥梁对其全寿命周期内的综合性能进行评估,设定不同维养场景分析对比不同的评估指标作为目标与约束对桥梁全寿命周期性能的影响,并对某单体桥梁30a周期内的维养策略进行求解.结果表示,评估方法能充分反映决策者的风险态度,避免综合性能评估中的"指标偏好";预测模型能对桥梁健康状态进行准确预测;以综合评估模型作为维养目标时,能得到令各单项评估指标均较优的维养决策场景,维养决策模型能在有限预算内最大限度提升桥梁综合性能.

       

      Abstract: With the rapid development of highway networks, the bridges not only need to meet the safety requirement but also social and economic development goals.In order to plan maintenance strategies to maintain the maximum performance of the bridge during its service life, it is necessary to develop a methodology that evaluates the comprehensive performance of the bridge effectively.In this paper, the multi-attribute utility incorporating the fuzzy evaluation method (MAUFE) is proposed to assess the bridge performance.Furthermore, the Markov model and LSTM algorithm are utilized to build a bridge degradation prediction model.The optimal model is established to maximize the MAUFE results.Various performance indicators are integrated by using a multi-attribute utility function.The bridge condition, life-cycle cost, and sustainability index are selected to assess the comprehensive performance of the bridge.The fusion weight of the performance index is determined by combining the analytical hierarchy process and entropy weight method (AHP-EW).The fuzzy comprehensive evaluation method is adopted to construct the fuzzy membership matrix of different indicators, and the comprehensive performance of bridges is calculated.A data-driven framework is used to predict the bridge's performance based on actual observation data.The mathematical model of bridge life-cycle maintenance decision-making under specified objectives and constraints is established, which is solved by a genetic algorithm to obtain an optimal maintenance strategy.Finally, the representative bridges are selected to assess the life-cycle comprehensive performance.And the maintenance strategies over thirty years of a particular bridge under different evaluation indicators as objectives and constraints are planned by using the proposed optimal model.The results show that the assessment method can fully reflect the decision-makers attitudes and cover many aspects within the performance assessment process.The prediction model is capable of accurately predicting the bridge's health status.The maintenance schedule that makes each evaluation indicator better can be obtained with the objective of comprehensive assessment model.The maintenance decision-making model can maximize the comprehensive performance of the bridge within the limited budget.

       

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