This paper introduces a novel stochastic inverse method that utilizes perturbation theory and advanced intelligence techniques to solve the multi‐parameter identification problem of concrete dams using displacement field monitoring data. The proposed method considers the uncertainties associated with the dam displacement monitoring data, which are comprised of two distinct sources: the first is related to stochastic mechanical properties of the dam, and the second is due to observation errors. The displacements at different measuring points generated by dam mechanical properties exhibit spatial correlation, while the observation errors at different points can be considered statistically random. In this context, the inversion formulas are derived for unknown stochastic parameters of the dam by combining perturbation equations and Taylor expansion methods. An improved meta‐heuristic optimization method is employed to identify the mean of stochastic parameters, while mathematical and statistical methods are used to determine the variance of stochastic parameters. The feasibility of the proposed method is verified through numerical examples of a typical dam section under different conditions. Additionally, the paper discusses and demonstrates the applicability of this method in a practical dam project. Results indicate that this method can effectively capture the uncertainty of dam's mechanical properties and separates them from observation errors.

中文翻译：

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混凝土坝的多参数识别：一种新颖的随机逆方法


本文介绍了一种新颖的随机反演方法，该方法利用摄动理论和先进的智能技术，利用位移场监测数据解决混凝土坝的多参数识别问题。该方法考虑了与大坝位移监测数据相关的不确定性，这些数据由两个不同的来源组成：第一个与大坝的随机力学特性有关，第二个与观测误差有关。大坝力学特性产生的不同测点的位移表现出空间相关性，而不同点的观测误差可以认为是统计随机的。在此背景下，结合摄动方程和泰勒展开法，推导了大坝未知随机参数的反演公式。采用改进的元启发式优化方法来确定随机参数的均值，同时使用数学和统计方法来确定随机参数的方差。通过不同工况下典型坝段的数值算例验证了该方法的可行性。此外，本文还讨论并论证了该方法在实际大坝工程中的适用性。结果表明，该方法可以有效捕捉大坝力学特性的不确定性，并将其与观测误差分开。