Parameter inversion of rock RHT constitutive model using PAWN global sensitivity analysis and intelligent optimization algorithm

The Riedel-Hiermaier-Thoma (RHT) constitutive model has been widely applied in tunnel blasting, impact-resistant structural design, and underground protective engineering due to its strong capability to describe the mechanical behavior of brittle materials such as rock and concrete under high-strain-rate and high-pressure conditions. However, the model involves a large number of nonlinear parameters, some of which are difficult to determine experimentally because of the high cost of testing. These key parameters are often adjusted through trial-and-error methods, which limit both modeling efficiency and simulation accuracy. To overcome these limitations, a comprehensive parameter inversion framework was developed for 16 difficult-to-calibrate parameters of the RHT model. The framework integrated the PAWN (Pianosi-Wagener) global sensitivity analysis method with intelligent optimization algorithms and coupled MATLAB with the ANSYS/LS-DYNA simulation platform. The area difference of the stress-strain curve was introduced as the core evaluation metric, and a batch result-extraction and automated three-wave alignment technique was developed. Based on these developments, an efficient and reliable RHT parameter inversion system was established, achieving, for the first time, a global sensitivity analysis (GSA) and automated inversion of key parameters in the RHT model. The results show that, among the 16 parameters analyzed, only eight exert a significant influence on the model response. The intelligent optimization–based inversion achieved relative errors ranging from 0.23% to 9.28%, and the reliability of the calibrated parameters was verified through Semicircular Bend Split Hopkinson Pressure Bar (SCB-SHPB) tests and scaled blasting experiments. The proposed method significantly enhances both the efficiency and accuracy of RHT parameter calibration without the need to construct large sample datasets, and it is applicable to a wide range of loading conditions. Compared with traditional calibration approaches, the required inversion accuracy was achieved in fewer than 15 iterations, meeting the dual demands of computational efficiency and precision. Overall, the proposed framework provides a new and effective approach for sensitivity analysis and parameter calibration of dynamic constitutive models, demonstrating strong engineering applicability and practical value.