Inversion of thermal parameters of arch dam based on multi-strategy improved sand cat swarm optimization algorithm

The simulation accuracy of concrete temperature fields is limited by thermal parameters, which often differ significantly from real values due to environmental temperature fluctuations, cooling water variation, solar radiation, and surface insulation in actual construction. Traditional inversion methods may fall into a local optimum and have low convergence efficiency; it is difficult to meet the requirements of high-dimensional nonlinear optimization. Therefore, this paper proposes a Multi-Strategy Improved Sand Cat Swarm Optimization algorithm (MISCSO). The population is initialized using a cubic chaotic map to enhance randomness, and the triangle walk mechanism, Levy flight, and lens imaging reverse learning strategy are integrated to balance the global exploration and local development capabilities. The advantages of MISCSO performance are verified by twelve benchmark function tests and nonparametric tests. Key thermal parameters were screened by Sobol global sensitivity analysis. A finite element model considering the ambient temperature, boundary conditions, and cooling water was built through an engineering example, and the thermal parameters were obtained by using the optimization algorithm. After substituting the inversion parameter value into the numerical model, the absolute error between the calculated temperature and the monitored value is within 1.0 °C, which verifies the effectiveness of the inversion method.