Ensuring the long-term, efficient, and safe operation of reservoir dams relies on the slope stability of embankment dams. Periodic fluctuations of the reservoir water level due to reservoir scheduling operations make the slope of the reservoir bank vulnerable to instability. To investigate the influence of various factors and their interactions with embankment dam slope stability under changing reservoir water levels, a global sensitivity analysis method is proposed that accounts for seepage–stress coupling. An embankment dam in Shaanxi Province, China, is studied as an example, with COMSOL Multiphysics software simulating the seepage and slope stability of the dam under fluctuating reservoir water level conditions and seepage–stress coupling. The global sensitivity analysis of factors affecting dam slope stability is accomplished by combining Plackett–Burman and Box–Behnken experimental designs, with ANOVA determining the sensitivity of each factor and interaction term. The results demonstrate that during the impoundment period of the reservoir, the saturation line is concave, and the overall stability safety of the dam slope increases first and then tends to be stable, according to the coefficient. The internal friction angle φ, cohesion c, and soil density ρ_s represent the three most sensitive factors affecting the stability and safety of the dam slope, while c × ρ_s is a second-order interaction term with significant sensitivity to the stability and safety coefficient of the dam slope. The reservoir drainage period infiltration line is convex, and dam slope stability first reduced and then increased. The magnitude of water level change H, internal friction angle φ, cohesion c, and soil density ρ_s are the four most sensitive factors for the coefficient of safety of dam slope stability, while c × ρ_s, H × ρ_s, and φ × ρ_s are the second-order interaction terms with significant sensitivity to the coefficient of safety of dam slope stability. These research findings and methods can offer valuable technical support and reference for the investigation and evaluation of the stability of embankment dam slopes.

Seepage is a main cause of dam failure, and its stability analysis is the focus of a dam’s design, construction, and management. Because a geological survey can only determine the range of a dam foundation’s hydraulic conductivity, hydraulic conductivity inversion is crucial in engineering. However, current inversion methods of dam hydraulic conductivity are either not accurate enough or too complex to be directly used in engineering. Therefore, this paper proposes a new method for the inversion of hydraulic conductivity with high application value in hydraulic engineering using an improved genetic algorithm coupled with an unsaturated equivalent continuum model (IGA–UECM). This method is implemented by a new code that fully considers engineering applicability. In addition to overcoming the premature convergence shortcomings of traditional genetic algorithms, it converges faster than Bayesian optimization and tree-structured Parzen estimator inversion algorithms. This method is verified by comparing the water head from drilling exploration and inversion. The results of the inversion are used to study the influence of a cement grouting curtain layout scheme on the seepage field of the Hami concrete-face rockfill dam in China, which is used as an engineering application case of the IGA–UECM. The law of the seepage field is reasonable, which verifies the validity of the IGA–UECM. The new inversion method of hydraulic conductivity and the proposed cement grouting curtain layout in this study offer possible strategies for the design, construction, and management of concrete-face rockfill dams.

Permeability characteristics in coarse-grained soil is pivotal for enhancing the understanding of its seepage behavior and effectively managing it, directly impacting the design, construction, and operational safety of embankment dams. Furthermore, these insights bridge diverse disciplines, including hydrogeology, civil engineering, and environmental science, broadening their application and relevance. In this novel research, we leverage a Convolutional Neural Network (CNN) model to achieve the accurate segmentation of coarse-grained soil CT images, surpassing traditional methods in precision and opening new avenues in soil granulometric analysis. The three-dimensional (3D) models reconstructed from the segmented images attest to the effectiveness of our CNN model, highlighting its potential for automation and precision in soil-particle analysis. Our study uncovers and validates new empirical formulae for the ideal particle size and the discount factor in coarse-grained soils. The robust linear correlation underlying these formulae deepens our understanding of soil granulometric characteristics and predicts their hydraulic behavior under varying gradients. This advancement holds immense value for soil-related engineering and hydraulic applications. Furthermore, our findings underscore the significant influence of granular composition, particularly the concentration of fine particles, on the tortuosity of water-flow paths and the discount factor. The practical implications extend to multiple fields, including water conservancy and geotechnical engineering. Altogether, our research represents a significant step in soil hydrodynamics research, where the CNN model’s application unveils key insights into soil granulometry and hydraulic conductivity, laying a strong foundation for future research and applications.

Particle shape is one of the critical parameter factors that affect gravel’s pore structure and permeability. However, few studies have considered its effects on engineering applications due to the difficulty of conducting laboratory tests. To overcome these difficulties, new methods of estimating the gravel pack shape that involve manual work and measuring the surface area of particles and pores based on support vector machine segmentation and the reconstruction of X-ray diffraction computed tomography (CT) images were proposed. Under the same conditions, CT tests were carried out on gravel packs and two other regular-shaped particle packs to investigate the influence of particle shape on the fractal dimension of gravel’s pore–particle interface and the specific surface area of the pore network. Additionally, permeability tests were performed to study the effect of particle shape on gravel’s hydraulic conductivity. The results showed that a gravel pack with a larger aspect ratio and a smaller roundness had a larger specific pore network surface area and a more complex pore structure, leading to lower permeability. This kind of gravel had a more significant length, quantity, and tortuosity of the seepage path when seepage occurred in a two-dimensional seepage field simulation. Therefore, we suggest that the filter materials of hydraulic projects should preferably use blasting gravel with a larger aspect ratio and smaller roundness to achieve better anti-seepage properties. In addition, projects can increase pores’ specific surface area using our method as a control factor in filter construction.

Cables have been widely used in long-span bridges as the main bearing structures. The safety of cables has great importance, and thus identification of the tension as accurately as possible is vital to reflect its bearing conditions and performance degradation of cables. To address this issue, this paper proposes an Implicit Function Method (IFM) to investigate the tension of cables using vibration-based methods. The method introduces three non-dimensional parameters to describe the relationship between tension and vibration frequency. Both inclined angle, sag, and bending stiffness are considered to create the frequency characteristic equation and the trust-region dogleg method is used to find the solution of implicit functions. The efficacy of the method is demonstrated by a field experiment in bridge engineering, and the results show that the proposed method improves on the string theory method with higher identification precision.