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 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. ; separate discussions must be submitted for individual papers. This paper is part of the Journal of Structural Engineering, © ASCE, ISSN 0733-9445. © ASCE 04020121-1 J. Struct. Eng. J. Struct. Eng., 2020, 146(7): 04020121 Downloaded from ascelibrary.org by 44.224.250.200 on 07/05/20. Copyright ASCE. For personal use only; all rights reserved. © ASCE 04020121-2 J. Struct. Eng. J. Struct. Eng., 2020, 146(7): 04020121 Downloaded from ascelibrary.org by 44.224.250.200 on 07/05/20. Copyright ASCE. For personal use only; all rights reserved. © ASCE 04020121-6 J. Struct. Eng. J. Struct. Eng., 2020, 146(7): 04020121 Downloaded from ascelibrary.org by 44.224.250.200 on 07/05/20. Copyright ASCE. For personal use only; all rights reserved. © ASCE 04020121-7 J. Struct. Eng. J. Struct. Eng., 2020, 146(7): 04020121 Downloaded from ascelibrary.org by 44.224.250.200 on 07/05/20. Copyright ASCE. For personal use only; all rights reserved. © ASCE 04020121-10 J. Struct. Eng. J. Struct. Eng., 2020, 146(7): 04020121 Downloaded from ascelibrary.org by 44.224.250.200 on 07/05/20. Copyright ASCE. For personal use only; all rights reserved. © ASCE 04020121-11 J. Struct. Eng. J. Struct. Eng., 2020, 146(7): 04020121 Downloaded from ascelibrary.org by 44.224.250.200 on 07/05/20. Copyright ASCE. For personal use only; all rights reserved. © ASCE 04020121-12 J. Struct. Eng. J. Struct. Eng., 2020, 146(7): 04020121 Downloaded from ascelibrary.org by 44.224.250.200 on 07/05/20. Copyright ASCE. For personal use only; all rights reserved. © ASCE 04020121-13 J. Struct. Eng. J. Struct. Eng., 2020, 146(7): 04020121 Downloaded from ascelibrary.org by 44.224.250.200 on 07/05/20. Copyright ASCE. For personal use onl...

In recent years, probabilistic methods, such as fragility analysis, have emerged as reliable tools for the seismic assessment of dam-type structures. These methods require the selection of a representative suite of ground motion records, resulting in the need for a ground motion selection method that includes all the relevant ground motion parameters in the fragility analysis of this type of structure. This article presents the development of up-to-date fragility curves for the sliding limit states of gravity dams in Eastern Canada using a record selection method based on the generalized conditional intensity measure approach. These fragility functions are then combined with the recently developed regional hazard data to evaluate the annual risk, which is measured in terms of the unconditional probability of limit state exceedance. The proposed methodology is applied to a case study dam in northeastern Canada, whose fragility is assessed through comparison with previous studies and current safety guidelines. It is observed that the more accurate procedure proposed herein produces less conservative fragility estimates for the case study dam.

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.

Methods for the seismic analysis of dams have improved extensively in the last several decades. Advanced numerical models have become more feasible and constitute the basis of improved procedures for design and assessment. A probabilistic framework is required to manage the various sources of uncertainty that may impact system performance and fragility analysis is a promising approach for depicting conditional probabilities of limit state exceedance under such uncertainties. However, the effect of model parameter variation on the seismic fragility analysis of structures with complex numerical models, such as dams, is frequently overlooked due to the costly and time-consuming revaluation of the numerical model. To improve the seismic assessment of such structures by jointly reducing the computational burden, this study proposes the implementation of a polynomial response surface metamodel to emulate the response of the system. The latter will be computationally and visually validated and used to predict the continuous relative maximum base sliding of the dam in order to build fragility functions and show the effect of modelling parameter variation. The resulting fragility functions are used to assess the seismic performance of the dam and formulate recommendations with respect to the model parameters. To establish admissible ranges of the model parameters in line with the current guidelines for seismic safety, load cases corresponding to return periods for the dam classification are used to attain target performance limit states.

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.