Prediction of arch dam deformation via correlated multi-target stacking

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–I 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.