The timely analysis of deformation monitoring data and reasonable diagnosis of the structural health are key tasks in dam health monitoring studies. This article presents a spatio-temporal clustering and health diagnosis method for super-high concrete arch dams that uses deformation monitoring data obtained from plumb meters. The spatio-temporal expression of the deformation monitoring data is proposed first by upgrading a punctuated time series to a curved panel time series, including cross-sectional, dam axial, and temporal changing directions. Second, a comprehensive similarity indicator on three aspects, namely, the absolute distance, incremental distance, and growth rate distance, is constructed after a deep discussion on deformation similarity characteristics both temporally and spatially. Next, the temporal clustering method is proposed by keeping the key features, namely, extreme points and turning points, while eliminating extraneous details, namely, noise points. Finally, the optimal spatio-temporal clustering of dam deformation is achieved by designing a multi-scale fuzzy C-means method of data mining and its iterative algorithm. The proposed method is applied to the Jinping-I hydraulic structure, which is the highest concrete arch dam in the world. The clustering results is quite sensitive in different weight coefficients of the comprehensive similarity indicator and clustering numbers of fuzzy C-means method. The dam deformation behaviors on high-water-level, water-falling, and low-water-level periods are analyzed and diagnosed. The advanced version of proposed methods is verified by comparative analysis on dam health diagnosis results obtained from ordinary deformation distribution figures and the spatio-temporal clustering figures. The proposed method will facilitate the recognition of abnormal deformation areas and associated safety diagnoses.

Constructing an accurate dam displacement health monitoring (DHM) model is crucial to ensure the safety of the dam. However, previous studies on DHM focused on the analysis and prediction of a single measurement point, with little work on multiple measurement points, which leads to low efficiency in evaluating the overall status of dams. Furthermore, the majority of these models are based on hydraulic engineering in moderate climatic areas, which results in low accuracy when applied to severely cold regions. To address these issues, the HT _cT model is proposed based on full consideration of extreme climate and engineering measures in cold regions, which replaces the commonly used harmonic function or air temperature with the features extracted from the measured temperature field after clustering as a temperature factor. The method effectively overcomes multicollinearity while ensuring accuracy. Moreover, multi-output least-square support vector regression (MOLSSVR), a multi-output model that can forecast multiple measurement points simultaneously, is proposed. By combining it with the HT _cT, the efficiency of the model is significantly improved. In addition, grey wolf optimization (GWO) is introduced to search for the optimal hyperparameters of the coupling model. The feasibility, accuracy, robustness, and long-term predictive capability of the proposed model are validated with measured displacements of a concrete gravity dam in a severely cold region. The results show that the proposed model outperforms the four popular machine learning (ML) models, including two support vector regression (SVR)-based models and two neural network-based models, which illustrate that the proposed model has outstanding accuracy and excellent long-term predictive capability. It provides an accurate and efficient novel approach for dam displacement safety monitoring in severely cold regions.

Underwater cracks in concrete structures are often difficult to detect due to their complexity of the service environment. With numerical and experimental analysis of concrete beams immersed in water, an active monitoring system, based on a cement-based piezoelectric intelligent module array (CPIMA), was developed to locate and quantify the underwater cracks. Time reversal (TR) of the stress wave field is accomplished to focus on the crack area through the concrete beam specimen by the system. First, a piezoelectric actuator is applied to emit the initial propagating wave, which can be reflected, attenuated, and diffracted by the crack, transmitted through water filled in the crack, as well as diffracted by the coarse aggregates. To extract the damage waveforms associated with the crack and analyze the robust time-reversal invariance under the high-order multiple scattering effect, a pair of homogeneous and heterogeneous forward finite element (FE) models is established. Then, the damage waveforms are time-reversed and re-propagated in the inverse numerical model, where an optimal refocusing is achieved on the crack that behaves as an acoustic source. Finally, the damage area is obtained in the form of the stacked energy distribution of each time step. The focus results are represented by cloud images and compared with root-mean-square deviation (RMSD) values. Numerical simulation and experiments show that this method can identify and quantify underwater cracks effectively.