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.

A roller compacted concrete dam (RCCD) located in Cambodia has been gradually deformed over the operation period (2011–2019), and the creep effect of the dam foundation is significant. In order to make integrity and safety assessments of the dam, it is necessary to know the actual mechanical properties of the foundation. This research proposes an intelligent computational framework for analysing the time-dependent working behaviour of the RCCD combined with viscoelastic finite element methods and advanced software techniques. According to the long-term deformation characteristics of the foundation, the Burgers model is employed to describe the constitutive relation of the bedrock. A finite element formulation describes the relationship between dam deformation and mechanical properties in the creep regime. A structural inverse methodology based on improved parallel grey wolf optimization (IGWO) is developed in order to search and identify viscoelastic parameters of the dam foundation. The nonlinear convergence factor strategy and multi-core parallel computing are introduced to enhance global search capability and improve the accuracy of the optimization algorithm. An example of analysis is performed on a dam section, and the results, which are compared with actual measurements for discussion, demonstrate that the selected constitutive model is reasonable and the designed inverse methodology is feasible. Moreover, the proposed IGWO algorithm is very competitive with other state-of-the-art optimization methods such as basic grey wolf optimization (GWO), particle swarm optimization (PSO) and whale optimization algorithm (WOA) for parameter inversion and real-time problems.