Efficiency-aware seismic fragility analysis of super-high arch dam using unsupervised ground motion clustering with probabilistic representation

• Fragility analysis of super-high arch dam is performed integrating ML-driven approaches and refined simulation. • Uncertain fragility intervals are derived through probabilistic prediction of NGBoost metamodel. • The smallest representative GMR subset are selected using PAM unsupervised clustering technology. • The time cost of computationally intensive simulations in fragility analysis is effectively reduced. Machine Learning (ML)-driven approaches have been employed to replace computationally intensive seismic simulations of hydraulic engineering structures. For the complex seismic responses of arch dams, constructing a metamodel that captures the nonlinear relationship between ground motion inputs and structural response outputs using a limited set of numerical simulations can significantly reduce the computational cost. However, conventional deterministic predictions and fragility analyses fail to account for the high aleatory and epistemic uncertainties inherent in the seismic response of arch dams. To this end, this paper proposes an efficient fragility analysis method for arch dams that integrates probabilistic ML algorithms with the traditional Incremental Dynamic Analysis. By constructing a Natural Gradient Boosting (NGBoost) metamodel for the arch dam dynamic response, not only can the predicted mean value of each response sample be obtained, but also its conditional probability distribution. Superimpose the simulation data with the response distribution predicted by NGBoost, and the binary parameters of the fragility function are estimated, thereby generating both the fragility curve and the uncertain fragility interval of arch dam. Additionally, representative Ground Motion Records (GMRs) for the arch dam are selected using the Partitioning Around Medoids (PAM) unsupervised clustering technique, determining the minimum subset proportion that effectively represents the whole GMR dataset. The effectiveness of the proposed method is validated in a super-high arch dam. The 40% GMR proportion is found to adequately reproduce the fragility curves of the whole dataset, with the reference curve falling within the derived uncertainty interval, achieving a 56.8% reduction in computational cost. The 60% GMR proportion ensured fragility curves with balanced accuracy and effectiveness, exhibiting maximum mean differences of 0.058 and maximum standard deviation differences of 0.031 from reference curves across all damage levels, while reducing computational cost by 39.7%. Comparative results demonstrate the superiority of NGBoost and PAM over existing deterministic metamodels and GMRs selection techniques, respectively. The efficient fragility analysis method proposed in this study ultimately enables the direct characterization of uncertainties in arch dam seismic responses.