Analysis of the time-varying reliability of earth dam slopes considering multiparameter spatial variability

Reliability analysis is an effective method for comprehensively assessing earth-rock dam safety, considering both soil spatial variability and reservoir water level fluctuations. However, previous studies primarily focused on static analyses at specific moments, and were not applicable to problems involving multiple high-dimensional random variables. Therefore, a probabilistic framework integrating slice inverse regression (SIR), Gaussian process regression (GPR), and Monte Carlo simulation (MCS) is proposed for assessing the time-varying reliability of earth dam slopes. This framework consists of three key components: (1) The Karhunen–Loève (KL) expansion is adopted to discretize the random field (RF) of high-dimensional multivariate soil parameters, and the dimension of the random field variables is further reduced utilizing a SIR method; (2) An efficient GPR surrogate model is constructed based on the reduced-dimension variables; (3) The MCS method is employed to estimate failure probability (P_f) of earth dam slopes. The proposed framework is illustrated using the Ashigong earth dam case study. Results demonstrate that the SIR-GPR-MCS framework effectively characterizes multi-parameter spatial variability (SV) and estimates time-dependent P_f with acceptable computational efficiency. Parametric analyses investigating soil parameter statistics and the time-varying statistical distribution of safety factors (FS) are also conducted. These analyses provide valuable insights for the design and maintenance of dam slope stability under fluctuating reservoir water levels.