The construction of an interval prediction model capable of explaining deformation uncertainties is crucial for the long-term safe operation of dams. High effective coverage and narrow interval coverage widths are two key benchmarks to ensure that the prediction interval (PI) can accurately quantify deformation uncertainties. The vast majority of existing models neglect to control the interval coverage width, and overly wide PIs can cause decision confusion when operators are developing safety plans for hydraulic structures. To address this problem, this paper proposes a novel interval prediction model combining bidirectional long-short-term memory network (Bi-LSTM) and split conformal quantile prediction (SCQP) for dam deformation prediction. The model uses Bi-LSTM as a benchmark regressor to extract and explain the nonlinear feature of dam deformation in the continuous time domain. SCQP is used to quantify the uncertainties in dam deformation prediction to ensure that the constructed PI can achieve high effective coverage while further improving the accuracy of the quantification of deformation uncertainties. The effectiveness of the proposed model is validated using deformation monitoring data collected from an arch dam in China. The results show that the average prediction interval effective coverage (PICP) of the proposed model is as high as 0.951 while the mean prediction interval width (MPIW) and coverage width-based criterion (CWC) are both only 5.815 mm. Compared with other models, the proposed method can construct higher-quality PIs, thus providing a better service for the safety assessment of dams.

The monitoring data can effectively reflect the safety status of the project during the construction of deep foundation pit, and the risks existing in the project can be discovered in time and the development trend can be reasonably predicted through the processing and analysis of the existing monitoring data. In this paper, a deep foundation pit in compound soil area of a coastal city was taken as an example, the BP neural network was taken to predict the monitoring data in the next stage, the entropy method was utilized to determine the weight of the evaluation index according to the predicted value, and the fuzzy comprehensive evaluation method was used to quantitatively describe the future safety status, so as to formulate targeted countermeasures and improve the construction safety.

Surface cracks are critical indicators of deterioration in flood-control infrastructure, yet automated detection from inspection imagery remains challenging due to complex backgrounds, elongated geometries, and variations in apparent scale. This study aims to develop a lightweight detector for accurate dike crack detection while maintaining computational efficiency. A hybrid CNN–Transformer detector, termed YOLO-Defender, was built on YOLOv8n. The proposed framework incorporates a C2F-FTB block in the backbone for local–global feature representation, a BiFF-PAN neck for enhanced multi-scale feature fusion, and coordinate attention for improved spatial feature localization. The model was evaluated on an in-house dike crack dataset containing 1217 images collected from flood-defense structures and related crack imaging scenarios using a unified training and testing protocol. YOLO-Defender achieved 89.6% mAP@0.5 and 69.2% mAP@0.5:0.95, improving YOLOv8n by 3.0 and 5.1 percentage points, respectively. Compared with YOLOv8n, the proposed model reduced parameters and GFLOPs by 41.2% and 18.5%, respectively, while achieving an inference speed of 153 FPS on a desktop GPU. The results indicate that task-oriented architectural design can improve crack detection accuracy and localization performance while preserving lightweight characteristics. These findings support efficient screening of hydraulic infrastructure inspection imagery.

The stability of bank slopes in pumped storage power stations is crucial, particularly in regions where frequent water level fluctuations occur. This study aims to investigate the degradation mechanism of bank slope under such fluctuating conditions, focusing on granite residual soil from the pumped storage power stations in Fujian, China. To explore the effects of drying-wetting cycles and matric suction on soil shear strength, drying and wetting cycles were conducted with unsaturated triaxial shear tests. The results revealed that the shear parameter strengthening effect occurs when the matric suction increases from 50 kPa to 200 kPa. Moreover, during the first five drying-wetting cycles, soil shear strength decreased sharply, with cohesion and internal friction angle decreasing by approximately 15.4% and 11.2%, respectively. This degradation trend stabilized in the later cycles. Scanning Electron Microscopy (SEM) analysis of the soil microstructure showed an evolution from a dense structure to a penetrating cavity during the cycles. This change reflects that the strength degradation characteristics of granite residual soils are controlled by the synergistic effects of structural and frictional mechanisms, manifesting as initial degradation followed by stabilization. Additionally, by fitting the nonlinear characteristics of the experimental data, shear strength evolution functions for matric suction and drying-wetting cycles were established, revealing the effect of these factors on strength degradation. These findings provide a theoretical basis for the stability analysis of bank slopes in pumped storage power stations, offering insights into soil behavior under fluctuating water levels.

Long-term chlorophyll-a (Chl-a) phenology and recurrent high-Chl-a conditions provide important evidence for identifying spatially persistent water-quality concerns in coastal bays. However, seasonally normalized screening of high-Chl-a recurrence at the sampling-cell scale remains limited for subtropical multi-bay coastlines, where regional monsoon forcing, hydrodynamic retention, riverine inputs, aquaculture, and coastal development jointly shape phytoplankton variability. Based on Copernicus Marine ocean-colour records from 2003 to 2024, this study developed a reproducible ~4 km sampling-cell framework for seven coastal bays in Fujian, China. Monthly geometric-mean climatologies, phenological metrics, P90-based high-Chl-a exceedance frequencies, monitoring-priority classes, and exploratory machine-learning diagnostics were derived. Bay-scale phenology showed clear divergence: Sansha and Xinghua Bays exhibited winter or year-end Chl-a enhancement, Xiamen Bay peaked in summer, and Quanzhou Bay reached an early-autumn maximum. Four seasonal phenological regimes further revealed cell-scale heterogeneity, with C2 representing winter-enhanced, high-amplitude cycles and C3 identifying cells with the most frequent seasonally normalized high-Chl-a exceedances. High- and moderate-priority cells were concentrated mainly in Sansha and Xinghua Bays, whereas Dongshan and Quanzhou Bays showed only localized priority cells. Exploratory diagnostics indicated that distance to the bay mouth was the most important correlate of recurrent high-Chl-a susceptibility, suggesting the role of bay-scale exchange and retention gradients. This framework converts long-term ocean-colour archives into spatially explicit phenological and anomaly-screening evidence to support targeted coastal water-quality monitoring and ecosystem management.