基于AHP-MDC法的城市轨道交通桥梁维修决策模型研究

Research on AHP-MDC Method-based Maintenance Decision-making Model for URT Bridges

  • 摘要:
    目的 城市轨道交通(以下简称“城轨”)桥梁结构长期处于列车动荷载作用下,其检测和维修任务变得越来越重要。目前对城轨桥梁的评价体系仍缺乏系统性和完整性,有必要建立城轨桥梁维修决策模型。
    方法 提出了一种基于AHP(层次分析法)- MDC(矩阵式双控法)的维修决策模型,以评估城轨桥梁的技术状态,指导桥梁的养护和管理。首先通过AHP法对城轨桥梁维修决策的关键因素进行了层次化分析,建立了维修决策体系。其次基于桥梁病害检测数据,采用MDC法对病害样本数据进行了处理和评估,建立了城轨桥梁的维修决策模型。最后将该维修决策模型应用于某城轨线路全线桥梁的技术状态评估,验证了模型的有效性和实用性。
    结果及结论 所提模型能够全面地评估城轨桥梁各部件的重要性和健康状况。基于该模型对各部件权重的合理分配和桥梁得分的精准计算,其结果可为城轨桥梁制定切实可行的维修决策。

     

    Abstract:
    Objective URT (urban rail transit) bridge structures are subjected to train dynamic loads over extended periods, making their inspection and maintenance tasks increasingly critical. At present, the current evaluation system for URT bridges still lacks systematicity and completeness, necessitating the establishment of a maintenance decision-making model for URT bridges.
    Method A maintenance decision-making model based on AHP (analytic hierarchy process) and MDC (matrix double control method) is proposed, aiming to assess the technical condition of URT bridges and guide bridge upkeep and management. First, the key factors in URT bridge maintenance decision-making are hierarchically analyzed using the AHP method, establishing a maintenance decision-making system. Second, based on bridge disease inspection data, the MDC method is employed to process and evaluate disease sample data, establishing a maintenance decision-making model for URT bridges. Finally, the maintenance decision-making model is applied to the technical condition assessment of bridges along an entire rail transit line to verify the model's effectiveness and practicality.
    Result & Conclusion  The proposed model is capable of comprehensively evaluating the importance and health condition of different components of URT bridges. Based on the model's rational weight allocation of each component and accurate calculation of bridge scores, the results can provide practical and feasible maintenance decisions for URT bridges.

     

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