Polymer anti-seepage walls have been widely used in the anti-seepage reinforcement projects of earth dams. Dam crest is always supposed to meet the requirements of traffic load which has significant influence on the dam body and anti-seepage wall. In order to analyze the coordination characteristics of the deformation between polymer anti-seepage wall and dam under traffic loads, a 3D finite element model of an earth dam that considers the coupling effect of seepage field and stress field was established. Besides, the influence of load amplitude, vehicle speed and driving position on the stress and deformation characteristics of polymer anti-seepage wall and dam was analyzed, with the displacement difference between dam and wall, wall Mises stress and wall subsidence as indicators. The results show that, compared with vehicle speed, the load amplitude and vehicle speed of traffic load exerted a greater impact on the coordination characteristics of the deformation of the dam. The variation range of the displacement difference caused by axial load change reached 87.1%, while that resulted from driving position change reached 90.3%. That is, when the passing vehicle has a light axle load and passes quickly over the anti-seepage wall, it has less impact on the dam.

Cement–Soil Mixing (CSM) Pile is an important technology for soft ground reinforcement, and its as-formed compressive strength directly affects engineering design and construction quality. To address the significant discrepancy between laboratory-tested strength and field as-formed strength arising from differing environmental conditions, this study conducted modified laboratory experiments simulating key field formation characteristics. A cement–soil preparation system considering actual immersion conditions was established, based on controlling the initial water content state of the foundation soil before pile formation and applying submerged conditions post-formation. Utilizing data mining on 84 sets of experimental data with various preparation parameter combinations, a prediction model for the as-formed strength of CSM Pile was developed based on the Particle Swarm Optimization-Extreme Gradient Boosting (PSO-XGBoost) algorithm. Engineering validation demonstrated that the model achieved an RMSE of 0.138, an MAE of 0.112, and an R² of 0.961. It effectively addresses the issue of large prediction deviations caused by insufficient environmental simulation in traditional mix proportion tests. The research findings establish a quantitative relationship between as-formed strength and preparation parameters, providing an effective experimental improvement and strength prediction method for the engineering design of CSM Pile.

The Ground Penetrating Radar (GPR) method is a commonly used method for earth dam disease detection. However, the major challenge is that the obtained GPR image data of earth dam disease mainly relies on human judgment, especially in long-distance earth dam disease detection, which may lead to misjudgments and omissions. The You Only Look Once v5s (YOLOv5s) algorithm is innovatively employed for GPR image recognition to address the current challenge. The YOLOv5s neural network model has advantages over the traditional convolutional neural network in terms of object detection speed and accuracy. In this study, an earth dam disease detection model was established based on YOLOv5s. Raw images from actual earth dam disease detection and GPR forward simulation images were used as the initial dataset. Data augmentation techniques were applied to expand the original dataset. The LabelImg annotation tool was employed to classify and label earth dam disease, thereby creating an object detection dataset that includes earth dam disease features. The model was trained within this dataset. The results indicate that the total loss function of the model trained on the custom dataset initially decreases and then stabilizes, showing no signs of overfitting and demonstrating good generalizability. The earth dam disease detection model based on YOLOv5s achieved average precision rates of 96.0%, 95.5%, and 93.9% for voids, seepage, and loosening disease, respectively. It can be concluded that the earth dam disease detection model based on YOLOv5s may be an effective tool for intelligent GPR image recognition in identifying earth dam disease.

High-pressure jet grouting is widely used in the construction of cut-off wall in hydraulic engineering. Compared to the high-pressure jet grouting used for foundation improvement, the high-pressure jet grouting cut-off wall of hydraulic engineering has lower requirements for the advance consolidation and provision of bearing capacity, and more focus on providing an anti-seepage effect and ensuring the continuous integrity of the wall. In the past, the research on high-pressure jet grouting focused more on the application to foundation treatments, and the research on the construction efficiency of cut-off wall is relatively insufficient. Therefore, in this study, a self-developed ipsilateral multi-nozzle jet grouting machine with a large drill diameter and construction method are proposed according to the construction characteristics of the embankment cut-off wall in hydraulic engineering. Based on the dyke protection projects, Jinggangshan navigation hydropower and Xingan navigation hydropower, the large-diameter, multi-nozzle, high-pressure jet cut-off wall test is carried out, and the wall forming effect of the cut-off wall is verified through the combination of indoor and outdoor tests. The results show that the proposed construction method can adapt to the formation conditions in which it is difficult to implement conventional high-pressure jet grouting, and obtains good construction efficiency. A favorable wall quality can be obtained by using more efficient large-diameter and ipsilateral multi-nozzle jet grouting in the construction of high-pressure jet cut-off wall.