Probabilistic methods, such as fragility analysis, have been developed as a promising alternative for the seismic assessment of dam-type structures. However, given the costly reevaluation of the numerical model simulations, the effect of the model parameters likely to affect the seismic fragility of the system is frequently overlooked. Acknowledging the lack of the thorough exploration of different machine learning techniques to develop surrogates or metamodels that efficiently approximate the seismic response of dams, this study provides insight on viable metamodels for the seismic assessment of gravity dams for use in fragility analysis. The proposed methodology to generate multivariate fragility functions offers efficiency while accounting for the most critical model parameter variation influencing the dam seismic fragility. From the analysis of these models, practical design recommendations can be formulated. The procedure presented herein is applied to a case study dam in northeastern Canada, where the polynomial response surface of order 4 (PRS ${\mathcal{O}}^4$ ) came up as the most viable metamodel among those considered. Its fragility is assessed through comparison with the current safety guidelines to establish a range of usable model parameter values in terms of the concrete-rock angle of friction, drain efficiency, and concrete-rock cohesion.

Most gravity dams have been designed and built during the past century with methods of analysis that are now considered inadequate. In recent decades, knowledge of seismology, structural dynamics and earthquake engineering has greatly evolved, leading to the evaluation of existing dams to ensure public safety. This study proposes a methodology for the proper modelling and characterisation of the uncertainties to assess the seismic vulnerability of a dam-type structure. This study also includes all the required analyses and verifications of the numerical model prior to performing a seismic fragility analysis and generating the corresponding fragility curves. The procedure presented herein also makes it possible to account for the uncertainties associated with the modelling parameters as well as the randomness in the seismic solicitation. The methodology was applied to a case study dam in Eastern Canada, whose vulnerability was assessed against seismic events with characteristics established by the current safety guidelines.

This study addresses the vital issue of the variability associated with modeling decisions in dam seismic analysis. Traditionally, structural modeling and simulations employ a progressive approach, where more complex models are gradually incorporated. For example, if previous levels indicate insufficient seismic safety margins, a more advanced analysis is then undertaken. Recognizing the constraints and evaluating the influence of various methods is essential for improving the comprehension and effectiveness of dam safety assessments. To this end, an extensive parametric study is carried out to evaluate the seismic response variability of the Koyna and Pine Flat dams using various solution approaches and model complexities. Numerical simulations are conducted in a 2D framework across three software programs, encompassing different dam system configurations. Additional complexity is introduced by simulating reservoir dynamics with Westergaard-added mass or acoustic elements. Linear and nonlinear analyses are performed, incorporating pertinent material properties, employing the concrete damage plasticity model in the latter. Modal parameters and crest displacement time histories are used to highlight variability among the selected solution procedures and model complexities. Finally, recommendations are made regarding the adequacy and robustness of each method, specifying the scenarios in which they are most effectively applied.

Important advances have been made in the methodologies for assessing the safety of dams, resulting in the review and modification of design guidelines. Many existing dams fail to meet these revised criteria, and structural rehabilitation to achieve the updated standards may be costly and difficult. To this end, probabilistic methods have emerged as a promising alternative and constitute the basis of more adequate procedures of design and assessment. However, such methods, in addition to being computationally expensive, can produce very different solutions, depending on the input parameters, which can greatly influence the final results. Addressing the existing challenges of these procedures to analyze the stability of concrete dams, this study proposes a probabilistic-based methodology for assessing the safety of dams under usual, unusual, and extreme loading conditions. The proposed procedure allows the analysis to be updated while avoiding unnecessary simulation runs by classifying the load cases according to the annual probability of exceedance and by using an efficient progressive sampling strategy. In addition, a variance-based global sensitivity analysis is performed to identify the parameters most affecting the dam stability, and the parameter ranges that meet the safety guidelines are formulated. It is observed that the proposed methodology is more robust, more computationally efficient, and more easily interpretable than conventional methods.

This paper provides a comprehensive review of the dynamic tensile behavior of concrete, focusing on its implications for seismic-resistant and impact-prone structures such as dams. The present work distinguishes itself in the following ways: providing the first comprehensive synthesis explicitly focused on large-aggregate dam concrete behavior across the seismic strain rate range (${10}^{-4}$ to ${10}^{-2}$ s⁻¹), which is critical yet underrepresented in the existing literature; integrating recent experimental and numerical advances regarding moisture effects, load history, and cyclic loading—factors that are essential for dam safety assessments; and critically evaluating current design guidelines for concrete dams against state-of-the-art research to identify gaps between engineering practice and scientific evidence. Through the extensive synthesis of experimental data, numerical simulations, and existing guidelines, the study examines key factors influencing dynamic tensile strength, including strain rate effects, crack evolution, testing techniques, and material variables such as moisture content, load history, and aggregate size. Experimental results from spall tests, split Hopkinson pressure bar configurations, and cyclic loading protocols are analyzed, revealing dynamic increase factors ranging from 1.1 to over 12, depending on the strain rates, saturation levels, and preloading conditions. The roles of inertial effects, free water (via the Stefan effect), and microstructural heterogeneity in enhancing or diminishing tensile performance are critically evaluated. Numerical models, including finite element, discrete element, and peridynamic approaches, are discussed for their ability to simulate crack propagation, inertia-dominated responses, and moisture interactions. The review identifies and analyzes current design guidelines. Key conclusions emphasize the necessity of integrating moisture content, load history, and mesoscale heterogeneity into dynamic constitutive models, alongside standardized testing protocols to bridge gaps between laboratory data and real-world applications. The findings advocate for updated engineering guidelines that reflect recent advances in rate-dependent fracture mechanics and multi-scale modeling, ensuring safer and more resilient concrete infrastructure under extreme dynamic loads.

When many repetitions of an expensive or time‐consuming analysis are needed, simplified models are usually adopted to reduce the cost. This is often the case with gravity dams under seismic load, especially if geometry variation needs to be considered. Deterministic analysis of dams is an important part of preliminary analyses but generally leads to overconservative designs. In recent years, many researchers have studied the potential of machine learning techniques to reduce the computational burden of dam assessment. However, generating the training dataset for a surrogate model based on high‐fidelity (HF) data can be expensive when a large set of uncertain parameters is considered. To address this issue, this study proposes the use of multifidelity surrogate (MFS) models. In this method, datasets with different levels of fidelity are combined to generate a highly accurate surrogate model at a lower cost. To illustrate this, the seismic behavior of a gravity dam is assessed by means of a HF nonlinear finite element model that considers geometric, material, and seismic uncertainties. In addition, five lower fidelity (LF) models are combined with HF samples to generate multifidelity models. The goodness of fit of the models and the computational time to produce the dataset are used to identify the combination that optimizes the MFS model performance. The results show that including medium‐ or low‐fidelity samples improves the predictive performance of a surrogate model and reduces its computational burden. The results also show that the data generation and the selection of the best LF model depend on the size of the HF dataset.