An efficient and accurate method for concrete thermal parameter inversion is essential to guarantee the reliable and prompt thermal analysis results of dams. Traditional inversion methods either suffer from low analysis efficiency or are limited in accuracy. Thus, this paper presents a method for multiple thermal parameter inversion based on an integrated surrogate model (ISM) and the Jaya algorithm. This method replaces finite element analysis with an ISM incorporating three machine learning algorithms, Kriging, support vector regression (SVR), and radial basis function (RBF), to describe the mapping relationship between thermal parameters and structure temperature responses. The input datasets for model training and testing are generated by a uniform design approach. Subsequently, a simple and efficient global optimization algorithm, Jaya, is used to identify the thermal parameters by minimizing the error between calculated and monitored temperatures. The effectiveness and practicality of this method are verified by applying monitored data of two strength grades of concrete in a dam. The verification results indicate that the proposed approach can obtain more accurate inversion results than the above individual models. Compared with these models, the inversion errors using ISM are reduced by 8.45%, 3.93% and 20.85%, respectively for C35 concrete, and by 6.53%, 23.82% and 44.43%, respectively for C40 concrete. Additionally, this approach maintains the powerful computational efficiency of surrogate-based optimization, and compared to the methods that directly invert using swarm intelligence algorithms, the analysis efficiency is improved by about 111.7 times.

Dynamic dam‐foundation interaction is great important in the design and safety assessment of the dam structures. Two classic boundary conditions, i.e., the viscous‐spring boundary and the viscous boundary, are employed to consider the radiation damping of the unbounded rock foundation. The input models of seismic excitation of the viscous‐spring boundary and the viscous boundary are derived. The accuracy of the two boundary conditions in the dynamic analysis of the dam foundation is verified through the foundation analysis using an impulsive load. The influences of two boundary conditions and their earthquake input models on the seismic analysis of the Pine Flat and Jin’anqiao gravity dam‐foundation‐reservoir systems are then investigated. The results of displacements, hydrodynamic pressure, and principal stresses show that the agreement between the results of the viscous‐spring boundary and viscous boundary is good. The relative errors of the two models in the Pine Flat and Jin’anqiao gravity dams are both less than 5%. They are both acceptable from an engineering point of view.

In the simulation of concrete thermal stress fields, thermal parameters are crucial for calculating the concrete temperature field. In actual construction, due to the adjustment of the concrete mixing ratio and the changing external environment (temperature fluctuations, cooling conditions, solar radiation, thermal insulation measures, etc.), there are significant differences between the thermal parameters obtained in tests and the actual working conditions, which affect the simulation accuracy. Therefore, the inverse analysis of concrete thermal parameters under real working conditions can be carried out based on the measured temperature data. A method for inverse analysis of thermal parameters of arch dams using the walrus optimization algorithm (WaOA) is proposed. To verify the accuracy of the inversion parameters, twelve classical test functions are used to compare the three algorithms to evaluate their fitness. The efficiency difference is analyzed by nonparametric methods such as Fredman and Wilcoxon rank sum test. The results consistently indicate that the walrus optimization algorithm performs better. Furthermore, the WaOA is utilized for the parameter inversion of an arch dam in the downstream area of the Jinsha River. We bring the inversion results into different dam sections to calculate the temperature field during construction, which effectively verifies the efficient solution ability of the WaOA for the inverse analysis of concrete thermal parameters under complex engineering backgrounds.

For arch dams, the joint surface has a certain bond strength after grouting the contraction joints, which can withstand the arch-wise tensile stress to a certain extent and influence the stress distribution of the dam blocks on both sides. The seismic response analysis of arch dams needs to consider the influence of the initial tensile and shear strengths generated by the contraction joint grouting. Thus, a modified B-differentiable equation method is proposed by introducing the initial tensile strength and the initial shear strength of contraction joints the into traditional B-differentiable equation method. In the proposed method, the shear strength varies with the contraction joint opening. The modified B-differentiable equation method can still be solved by the B-differentiable damped Newton method with theoretical guarantee of convergence. Then, a seismic calculation model for the dam–reservoir–foundation system is developed based on the modified B-differentiable equation method, the Westergaard additional mass method, and multiple transmission boundary conditions. The influence of the initial tensile and shear strength of the contraction joints on the arch dam seismic response is discussed. The results show that the initial tensile and shear strength of the contraction joints have an influence on the opening and distribution range of the contraction joints and the maximum values of the principal tensile and compressive stress in the dam body. The initial tensile and compressive strength of the contraction joints should be considered when carrying out seismic response analysis of arch dams.