Prediction of arch dam deformation via correlated multi-target stacking

Siyu Chen; Chongshi Gu; Chaoning Lin; Mohammad Amin Hariri-Ardebili

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Prediction of arch dam deformation via correlated multi-target stacking

机译：相关多目标堆叠预测拱坝变形

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Majority of the existing dam deformation monitoring models focus on the prediction of individual displacement, and ignore the spatial correlation of data. In this study, we propose a method dealing with multi-target prediction called the Maximum Correlated Stacking of Single-Target. The proposed method can provide reliable predictions of multi-target simultaneously, while fully exploiting the internal relationships between target variables via the strategy of targets stacking. Moreover, it can be coupled with different existing baseline models for the prediction and anomaly detection of arch dam deformation. Jinping-1 arch dam is taken as a case study, where the monitoring displacement of 23 different points are analyzed and modeled simultaneously. Three kernel-based machine learning algorithms (i.e., support vector machine, relevance vector machine, and kernel extreme learning machine) and the partial least squares regression are adopted as baseline models for multi-target regression methods. Compared with the single-target regression and two state-of-the-art multi-target regression methods, the simulated results reveal the higher accuracy of the proposed method. Furthermore, model performance is validated in terms of anomaly detection capability, where two progressive anomalous scenarios (i.e., anomalies of single or multiple points) are investigated. The proposed method can be adapted for the health monitoring of other infrastructures in which multiple responses (e.g., displacement, temperature, or stress) need to be predicted simultaneously.

机译：大多数现有的大坝变形监测模型专注于对单个位移的预测，并忽略数据的空间相关性。在本研究中，我们提出了一种处理多目标预测的方法，称为单目标的最大相关堆叠。该方法可以同时提供多目标的可靠预测，同时通过目标堆叠的策略充分利用目标变量之间的内部关系。此外，它可以与不同现有的基线模型耦合，用于预测和异常检测拱坝变形。 Jinping-1 Arch Dam是一种案例研究，其中分析了23种不同点的监测位移和建模。基于内核的机器学习算法（即，支持向量机，相关矢量机器和内核极端学习机器）和部分最小二乘回归作为用于多目标回归方法的基线模型。与单目标回归和两个最先进的多目标回归方法相比，模拟结果揭示了所提出的方法的更高精度。此外，在异常检测能力方面验证了模型性能，其中调查了两个渐进异常情景（即单个或多个点的异常）。所提出的方法可以适用于对其他基础设施的健康监测，其中需要同时预测多重响应（例如，位移，温度或应力）。

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来源
《Applied Mathematical Modelling》 |2021年第3期|1175-1193|共19页
作者
Siyu Chen; Chongshi Gu; Chaoning Lin; Mohammad Amin Hariri-Ardebili;
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作者单位

Hohai University College of Water Conservancy and Hydropower Engineering Nanjing China Department of Civil Environmental and Architectural Engineering University of Colorado Boulder CO USA;

Hohai University College of Water Conservancy and Hydropower Engineering Nanjing China State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai University Nanjing China National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety Hohai University Nanjing China;

Hohai University College of Water Conservancy and Hydropower Engineering Nanjing China Faculty of Technology Policy and Management Delft University of Technology Delft the Netherlands;

Department of Civil Environmental and Architectural Engineering University of Colorado Boulder CO USA University of Maryland College Park MD USA;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Multi-target regression; Maximum correlated stacking of single-target; Machine learning; Prediction; Dam health monitoring;

机译：多目标回归;单目标的最大相关堆叠;机器学习;预言;大坝健康监测;

Prediction of arch dam deformation via correlated multi-target stacking

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