# Dynamic Inversion Method for Concrete Gravity Dam on Soft Rock Foundation  
[PERSON]  
1 College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210024, China; [EMAIL_ADDRESS] (G.Y. ); [EMAIL_ADDRESS] (W.X. ); [PERSON]_ADDRESS] (T.L.) 2  
[PERSON]  
1 College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210024, China; [EMAIL_ADDRESS] (G.Y. ); [EMAIL_ADDRESS] (W.X. ); [PERSON]_ADDRESS] (T.L.) 2  
[PERSON]  
3 Center for Big Data and Smart Water, Nanjing Hydraulic Research Institute, Nanjing 210029, China; [EMAIL_ADDRESS]  
2  
[PERSON]  
1 College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210024, China; [EMAIL_ADDRESS] (G.Y. ); [EMAIL_ADDRESS] (W.X. ); [PERSON]_ADDRESS] (T.L.) 2  
[PERSON]  
1 College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210024, China; [EMAIL_ADDRESS] (G.Y. ); [EMAIL_ADDRESS] (W.X. ); [PERSON]_ADDRESS] (T.L.) 2  
[PERSON]  
4 Dam Safety Management Department, Nanjing Hydraulic Research Institute, Nanjing 210029, China 45  
external loads [3; 4]. However, for the rock mass forming the foundation of a gravity dam, mechanical parameters obtained from laboratory tests on small specimens or limited field samples often lack representativeness and reliability due to the inherent heterogeneity of the rock mass (e.g., the influence of joints and fissures) [5]. Consequently, the inversion of mechanical parameters through feedback analysis of field monitoring data has become a crucial approach [6]. Among traditional optimization-based inversion methods, [PERSON] et al. [7] employed the simplex method to invert the comprehensive mechanical parameters of surrounding rock in a hydropower station, while [PERSON] et al. [8] used the simplex method to determine the creep characteristics of a milled concrete dam. With advancements in artificial intelligence, various intelligent optimization algorithms have been introduced into inversion analysis. For instance, [PERSON] et al. [9] applied a chaotic genetic algorithm to invert the transverse isotropic deformation and asymptotic parameters of a milled concrete dam. [PERSON] et al. [10] integrated an optimization method based on homogeneous design with a BP neural network to address the complex multi-parameter inversion problem for both the dam and its foundation. [PERSON] et al. [11] compared the performance of different machine learning models and proposed the XGBoost algorithm for inverting stockpile model parameters using dam deformation monitoring data. However, current research on dam parameter inversion has largely focused on static analysis methods. During the operational period, the dam is subjected to multiple multi-physics interactions, including hydraulic pressure, thermal cycling, and other environmental factors. These coupled loading conditions induce progressive degradation of foundation rock properties, particularly affecting strength and deformation parameters [12; 13]. This time-dependent material deterioration is especially significant in the soft rock foundation due to the inherent structural vulnerability and higher susceptibility to environmental factors [14]. Recent investigations on time-dependent behaviors of geotechnical structures, such as bridge pile foundations in soft soil, have demonstrated the necessity of accounting for creep mechanisms and consolidation effects in long-term performance evaluations [15]. Dynamic inversion refers to an iterative updating process where structural parameters are continuously adjusted based on time-dependent monitoring data, thereby capturing the evolutionary trends of structural characteristics. Compared to static inversion, dynamic inversion offers superior capability in tracking time-varying structural parameters through continuous data assimilation, but requires higher computational costs and more sophisticated algorithms.