The inverse analysis of the deformation moduli of high arch dams based on displacement monitoring data is essential for structural safety assessment. In traditional inverse analysis methods, the deformation moduli are identified based on the single-objective optimization and the hydrostatic component derived from the statistical model. This type of method has two main shortcomings: First, it treats the essential multi-objective optimization problem as a single-objective problem; second, the extracted hydrostatic component may be biased due to the multicollinearity of variables in the statistical model. This paper presents a methodology for the inverse analysis of the deformation moduli of high arch dams under a multi-objective optimization strategy. The methodology employs empirical mode decomposition to extract the aging component from displacement monitoring data. Then, thermomechanical analysis is used to reconstruct the remaining hydrostatic and temperature components, thereby avoiding the biases encountered in solving the statistical model. The adaptive polynomial chaos expansion method is embedded in the NSGA-III algorithm to establish and solve multi-objective functions in the inverse analysis. Additionally, a composite decision index considering errors and test information is proposed to determine acceptable deformation moduli from the Pareto solution set. A high arch dam is selected to illustrate this methodology with static and dynamic monitoring data. The results show that the identified deformation moduli have errors of 3.8% and 7.2% in displacement and acceleration, respectively. The proposed methodology can yield deformation modulus values that are more consistent with the physical implications than those of the single-objective optimization method.

中文翻译：

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基于位移重建技术和多目标优化的高拱坝变形模量反演分析

基于位移监测数据的高拱坝变形模量反演分析对于结构安全评估至关重要。在传统的反分析方法中，变形模量是基于单目标优化和从统计模型导出的静压分量来确定的。这类方法主要有两个缺点：一是将本质上的多目标优化问题视为单目标问题；其次，由于统计模型中变量的多重共线性，提取的静水成分可能存在偏差。本文提出了一种在多目标优化策略下对高拱坝变形模量进行反分析的方法。该方法采用经验模式分解从位移监测数据中提取老化成分。然后，使用热机械分析来重建剩余的流体静力学和温度分量，从而避免在求解统计模型时遇到的偏差。 NSGA-III算法中嵌入了自适应多项式混沌展开方法，用于建立和求解逆分析中的多目标函数。此外，还提出了考虑误差和测试信息的复合决策指数，以从帕累托解集中确定可接受的变形模量。选择一座高拱坝通过静态和动态监测数据来说明该方法。结果表明，识别的变形模量在位移和加速度方面的误差分别为3.8%和7.2%。所提出的方法可以产生比单目标优化方法更符合物理含义的变形模量值。