A new approach for mechanical parameter inversion analysis of roller compacted concrete dams using modified PSO and RBFNN.

The mechanical parameter inversion model is an essential part of ensuring dam health; it provides a parametric basis for assessing the safe operational behavior of dams using numerical simulation techniques. Due to the complicated nonlinear mapping relationship between the roller compacted concrete (RCC) dam's mechanical parameters and various environmental quantities, as well as conventional statistical models, machine learning methods, and neural networks fail to consider the inputs of fuzzy uncertainty factors. Therefore, the accuracy, efficiency, and stability of inversion models are usually affected by their modeling methods. In this paper, a novel hybrid model for mechanical parameter inversion of an RCC dam is proposed, which uses a radial basis function neural network (RBFNN) to establish the nonlinear mapping relationship between the dam mechanical parameters and the environmental quantities, and the modified particle swarm optimization (PSO) algorithm is used to find the optimal parameters of the model. The modified PSO algorithm makes the inertia weight ω dynamically adjust with the number of iterations to improve the randomness and diversity of the particle population, and population crossover and mutation are introduced to improve the global search ability and convergence speed of the algorithm. The proposed hybrid model is verified and comparatively analyzed by four typical mathematical test functions, and the results show that the proposed model exhibits good performance in parameter inversion accuracy, convergence speed, stability and robustness. Finally, the model is applied to the mechanical parameter inversion analysis of an RCC gravity dam in Henan Province in China. The results show that the proposed model is feasible and reasonable for practical engineering applications, and the relative error between the results obtained by inputting the inverted parameters into the numerical model and monitoring data was within 10%. The methodology derived from this study can provide technical support and a reference for the mechanical parameter inversion analysis of similar dam projects. [ABSTRACT FROM AUTHOR]