Approximately 30,000 dams in China are aging and are considered to be high-level risks. Developing a framework for analyzing spatial multicriteria flood risk is crucial to ranking management scenarios for these dams, especially in densely populated areas. Based on the theories of spatial multicriteria decision analysis, this report generalizes a framework consisting of scenario definition, problem structuring, criteria construction, spatial quantification of criteria, criteria weighting, decision rules, sensitivity analyses, and scenario appraisal. The framework is presented in detail by using a case study to rank dam rehabilitation, decommissioning and existing-condition scenarios. The results show that there was a serious inundation, and that a dam rehabilitation scenario could reduce the multicriteria flood risk by 0.25 in the most affected areas; this indicates a mean risk decrease of less than 23%. Although increased risk (

The seepage field of tailings dam is closely related to the safety state. Real-time evaluation of seepage field safety based on monitoring data is of great significance to ensure the safe operation of tailings pond. The premise of accurately evaluating the safety status is to ensure reliability of the data, and it is necessary to identify the anomalies of the monitoring data. Because of the complex influence factors of seepage field of tailings dam, the traditional anomaly identification method based on regression model fails due to its low fitting accuracy. Therefore, a novel abnormal identification method of monitoring data based on improved cloud model and radial basis function neural network model, which can accurately identify anomaly data and distinguish the environmental quantity response. Based on the coupling relationship between the seepage field and the slope stability, the surrogate model between the depth of saturation line and the safety factor of slope stability is constructed, and the real-time safety evaluation method of seepage field is put forward. The proposed methods are applied to an engineering example. The misjudgment rates of the abnormal data identification method are less than 5%, and it has better applicability than the traditional regression model. The constructed real-time safety evaluation model accurately reflected the health status of the seepage field, and realized the quantitative assessment of the safety of tailings dam. This provides reliable data support for the operation management and the risk control of tailings pond.

Deformation is one of the critical response quantities reflecting the structural safety of dams. To enhance outlier identification and denoising in dam deformation monitoring data, this study proposes a novel preprocessing method based on optimized Variational Mode Decomposition (VMD) and Kernel Density Estimation (KDE). The approach systematically processes data in three steps: First, VMD decomposes raw data into intrinsic mode functions without recursion. The parallel Jaya algorithm is used to adaptively optimize VMD parameters for improved decomposition. Second, the intrinsic mode functions containing outlier and noise characteristics are identified and separated using sample entropy and correlation coefficients. Finally, KDE thresholds are applied for outlier localization, while a data superposition method ensures effective denoising. Validation using simulated deformation data and Global Navigation Satellite Systems (GNSS)-based observed horizontal deformation from dam engineering demonstrates the method’s robustness in accurately identifying outliers and denoising data, achieving superior preprocessing performance.

There is a temperature difference in the abnormal seepage area of a dam body. Infrared thermal imaging technology can effectively obtain temperature information and use infrared images to analyze the area of a dam seepage anomaly. Three indoor experiments were conducted to study the effects of the temperature of the abnormal seepage location, shooting distance, and shooting angle on the acquisition of infrared images. On this basis, the target area is obtained adopting a temperature threshold and image segmentation, and the calculation results are then compared with the actual area. Experimental results show that within a certain range, the temperature of the target area and the shooting distance and angle do not appreciably affect the calculation of the target area in the infrared image, and the temperature threshold and image segmentation methods can effectively obtain the abnormal seepage area in practical engineering applications.

Crack detection in cement infrastructure is imperative to ensure its structural integrity and public safety. However, most existing methods use multi-scale and attention mechanisms to improve on a single backbone, and this single backbone network is often ineffective in detecting slender or variable cracks in complex scenarios. We propose a novel network, FD²-YOLO, based on frequency-domain dual-stream YOLO, for accurate and efficient detection of cement cracks. Firstly, the model employs a dual backbone architecture, integrating edge and texture features in the frequency domain with semantic features in the spatial domain, to enhance the extraction of crack-related features. Furthermore, the Dynamic Inter-Domain Feature Fusion module (DIFF) is introduced, which uses large-kernel deep convolution and Hadamard to enable the adaptive fusion of features from different domains, thus addressing the problem of difficult feature fusion due to domain differences. Finally, the DIA-Head module has been proposed, which dynamically focuses on the texture and geometric deformation features of cracks by introducing the Deformable Interactive Attention Module (DIA Module) in Decoupled Head and utilizing its Deformable Interactive Attention. Extensive experiments on the RDD2022 dataset demonstrate that FD²-YOLO achieves state-of-the-art performance. Compared with existing YOLO-based models, it improves mAP50 by 1.3%, mAP50-95 by 1.1%, recall by 1.8%, and precision by 0.5%, validating its effectiveness in real-world object detection scenarios. In addition, evaluation on the UAV-PDD2023 dataset further confirms the robustness and generalization of our approach, where FD²-YOLO achieves a mAP50 of 67.9%, mAP50-95 of 35.9%, recall of 61.2%, and precision of 75.9%, consistently outperforming existing lightweight and Transformer-based detectors under more complex aerial imaging conditions.