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Uncertainty‐informed regional deformation diagnosis of arch dams
Computer-Aided Civil and Infrastructure Engineering ( IF 8.5 ) Pub Date : 2024-12-20 , DOI: 10.1111/mice.13395
Xudong Chen, Wenhao Sun, Shaowei Hu, Liuyang Li, Chongshi Gu, Jinjun Guo, Bowen Wei, Bo Xu
Computer-Aided Civil and Infrastructure Engineering ( IF 8.5 ) Pub Date : 2024-12-20 , DOI: 10.1111/mice.13395
Xudong Chen, Wenhao Sun, Shaowei Hu, Liuyang Li, Chongshi Gu, Jinjun Guo, Bowen Wei, Bo Xu
Accurately predicting dam deformation is crucial for understanding its operational status. However, existing models struggle to effectively capture the spatiotemporal correlations in monitoring data and quantify uncertainty within dam systems. This paper presents an innovative uncertainty quantification model for evaluating regional deformation in arch dams. First, a method to extract the spatiotemporal correlation features is proposed. Considering the multidimensional deformation at measurement points, correlations among various points are analyzed through improved self‐organizing map clustering and federated Kalman filtering. Second, a temporal convolutional network is employed for improved lower and upper bound estimation, and a quality‐driven loss function is adopted to optimize model parameters. Finally, engineering case studies demonstrate that this model can generate reliable prediction intervals for regional deformation, thereby aiding in risk analysis and diagnostics.
中文翻译:
拱坝不确定性知情区域变形诊断
准确预测大坝变形对于了解其运行状态至关重要。然而,现有模型难以有效地捕捉监测数据中的时空相关性并量化大坝系统内的不确定性。本文提出了一种创新的不确定性量化模型,用于评估拱坝的区域变形。首先,提出了一种提取时空关联特征的方法。考虑到测点的多维变形,通过改进的自组织映射聚类和联合卡尔曼滤波分析了各个点之间的相关性。其次,采用时间卷积网络改进下界和上限估计,并采用质量驱动的损失函数来优化模型参数。最后,工程案例研究表明,该模型可以为区域变形生成可靠的预测区间,从而有助于风险分析和诊断。
更新日期:2024-12-20
中文翻译:
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拱坝不确定性知情区域变形诊断
准确预测大坝变形对于了解其运行状态至关重要。然而,现有模型难以有效地捕捉监测数据中的时空相关性并量化大坝系统内的不确定性。本文提出了一种创新的不确定性量化模型,用于评估拱坝的区域变形。首先,提出了一种提取时空关联特征的方法。考虑到测点的多维变形,通过改进的自组织映射聚类和联合卡尔曼滤波分析了各个点之间的相关性。其次,采用时间卷积网络改进下界和上限估计,并采用质量驱动的损失函数来优化模型参数。最后,工程案例研究表明,该模型可以为区域变形生成可靠的预测区间,从而有助于风险分析和诊断。