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.

The modeling of cracks and identification of dam behavior changes are difficult issues in dam health monitoring research. In this paper, a time‐varying identification model for crack monitoring data is built using support vector regression (SVR) and the Bayesian evidence framework (BEF). First, the SVR method is adopted for better modeling of the nonlinear relationship between the crack opening displacement (COD) and its influencing factors. Second, the BEF approach is applied to determine the optimal SVR modeling parameters, including the penalty coefficient, the loss coefficient, and the width coefficient of the radial kernel function, under the principle that the prediction errors between the monitored and the model forecasted values are as small as possible. Then, considering the predicted COD, the historical maximum COD, and the time‐dependent component, forewarning criteria are proposed for identifying the time‐varying behavior of cracks and the degree of abnormality of dam health. Finally, an example of modeling and forewarning analysis is presented using two monitoring subsequences from a real structural crack in the Chencun concrete arch‐gravity dam. The findings indicate that the proposed time‐varying model can provide predicted results that are more accurately nonlinearity fitted and is suitable for use in evaluating the behavior of cracks in dams.

Deformation monitoring data provide a direct representation of the structural behavior of reservoir bank rock slopes, and accurate deformation prediction is pivotal for slope safety monitoring and disaster warning. Among various deformation prediction models, hybrid models that integrate field monitoring data and numerical simulations stand out due to their well-defined physical and mechanical concepts, and their ability to make effective predictions with limited monitoring data. The predictive accuracy of hybrid models is closely tied to the precise determination of rock mass mechanical parameters in structural numerical simulations. However, rock masses in rock slopes are characterized by intersecting geological structural planes, resulting in reduced strength and the creation of multiple fracture flow channels. These factors contribute to the heterogeneous, anisotropic, and size-dependent properties of the macroscopic deformation parameters of the rock mass, influenced by the coupling of seepage and stress. To improve the predictive accuracy of the hybrid model, this study introduces the theory of equivalent continuous media. It proposes a method for determining the equivalent deformation parameters of fractured rock mass considering the coupling of seepage and stress. This method, based on a discrete fracture network (DFN) model, is integrated into the hybrid prediction model for rock slope deformation. Engineering case studies demonstrate that this approach achieves a high level of prediction accuracy and holds significant practical value.

Reservoir water level fluctuations and rainfall drive bank slope deformation, typically exhibiting spatiotemporal lags. Existing prediction models often fail to characterize these complex coupled relationships and rely on manual variable selection, causing information loss and reduced performance. To address these issues, a novel multi-point prediction model for reservoir bank slope deformation based on lag-aware clustering (LAC), referred to as LAC-MOGP, is proposed in this study. First, the Maximal Information Coefficient (MIC) quantifies the lag between influencing factors and deformation for objective factors screening. Next, an improved dynamic time warping (DTW) algorithm with a lag-difference-constrained matching window is combined with affinity propagation (AP) to cluster monitoring points based on asynchronous temporal correlations. Finally, a DTW-based similarity weighting scheme is embedded into a multi-output Gaussian Process (MOGP) kernel to refine covariance modeling and predict deformation within each cluster. Validated using observations from the Jinlongshan slope of the Ertan arch dam, the proposed model outperformed traditional methods in prediction accuracy and long-term stability. Achieving the lowest average root mean square error (2.677 mm) and average mean absolute error (2.325 mm), the LAC-MOGP model demonstrates significant effectiveness and practical applicability for reservoir slope deformation forecasting.