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.

Back analysis for seepage parameters is a classic issue in hydraulic engineering seepage calculations. Considering the characteristics of inversion problems, including high dimensionality, numerous local optimal values, poor convergence performance, and excessive calculation time, a biological immune mechanism-based quantum particle swarm optimization (IQPSO) algorithm was proposed to solve the inversion problem. By introducing a concentration regulation strategy to improve the population diversity and a vaccination strategy to accelerate the convergence rate, the modified algorithm overcame the shortcomings of traditional PSO which can easily fall into a local optimum. Furthermore, a simple multicore parallel computation strategy was applied to reduce computation time. The effectiveness and practicability of IQPSO were evaluated by numerical experiments. In this paper, taking one concrete face rock-fill dam (CFRD) as a case, a back analysis for seepage parameters was accomplished by utilizing the proposed optimization algorithm and the steady seepage field of the dam was analysed by the finite element method (FEM). Compared with immune PSO and quantum PSO, the proposed algorithm had better global search ability, convergence performance, and calculation rate. The optimized back analysis could obtain the permeability coefficient of CFRD with high accuracy.

During the long‐term service life of hydraulic structures, the calcium compounds in cement‐based materials decompose in the aqueous environment, leading to the continuous change of seepage characteristics. To study the influence of calcium leaching on the concrete dam seepage characteristics, we proposed a new mathematic model of the cement‐based material calcium leaching model under advection‐diffusion‐driven leaching. A solid‐liquid nonequilibrium model is adopted to model the decomposition of calcium hydroxide (CH) and calcium silicate hydrate gel (C‐S‐H). To calculate the porosity more accurately, the proposed model takes the effect of different calcium compound decomposition on the porosity increase in consideration, respectively. Shimantan dam is selected for the three‐dimensional (3D) calcium leaching analyses. The 3D finite element model of this dam is analyzed using COMSOL Multiphysics software that is based on the finite element method. Based on the proposed model, seepage characteristics evolutions of the Shimantan dam are studied. Good agreement between the numerical results and the monitored data indicates the accuracy of this simulation. The result shows that after 100 a leaching duration, the uplift pressure increases by 40.8%, and the leakage quantities of the dam body and foundation increase by 48 and 17 times. The rise of uplift pressure and leakage changes caused by curtain deterioration are the main influences of calcium leaching on the dam seepage. The parameter sensitivity results show that it is necessary to reduce CH content in cement‐based materials to obtain better calcium leaching durability. This model and simulation results can guide the operation of concrete dams under advection‐diffusion‐driven leaching.

There exist shear and seepage behaviors on the interface between clay core and concrete slab in clay core dams. In order to investigate the seepage characteristics of the clay–structure interface after shear deformation, a shear-seepage test system is proposed, in which the seepage direction is perpendicular to the shear direction. The shear test and shear-seepage test are performed on clay–metal and clay–mortar interfaces under different normal stresses (100, 200, 400, 800, and 1600 kPa). The shear stress-deformation curves of two clay–structure interfaces exhibit softening behavior and residual friction behavior. The interface roughness can enhance peak and residual shear strength and increase peak displacement. The shear-seepage test results show that specimen permeability decreases first and then increases to a stable value as shear deformation increases under low normal stress, while it decreases continuously and then retains stability under high normal stress. The interface roughness enhances specimen permeability under low normal stress, whereas it has a weak effect on specimen permeability under high normal stress. Compared with initial permeability, shear deformation reduces specimen permeability rather than raise it. The ratio of stabilized permeability coefficient to initial value ranges from 0.6 to 0.8. The clay–structure interface still has a good resistance to seepage failure after shear deformation. The shear dilation features and interface pore decrease caused by shear behavior are the internal attributions of clay–structure specimen permeability evolution.

Seepage is the primary cause of dam failures. Conducting regular seepage analysis for dams can effectively prevent accidents from occurring. Accurate and rapid determination of seepage parameters is a prerequisite for seepage calculation in hydraulic engineering. The Whale Optimization Algorithm (WOA) was combined with Support Vector Regression (SVR) to invert the hydraulic conductivity. The good point set initialization method, a cosine-based nonlinear convergence factor, the Levy flight strategy, and the Quasi-oppositional learning strategy were employed to improve WOA. The effectiveness and practicality of Improved Whale Optimization Algorithm (IWOA) were evaluated via numerical experiments. As a case study, the seepage parameters of the Dono Dam located on the Baishui River in China were inversed, adopting the proposed inversion model. The calculated seepage field was reasonable, and the relative error between the simulated head and the measured value at each monitoring point was within 2%. This new inversion method is more feasible and accurate than the existing hydraulic conductivity estimation methods.