Browsing by Author "Paultre, Patrick"

Now showing 1 - 4 of 4
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    Accounting for Uncertainties in the Safety Assessment of Concrete Gravity Dams: A Probabilistic Approach with Sample Optimization
    (MDPI, 2021) Segura, Rocio L.; Miquel, Benjamin; Paultre, Patrick; Padgett, Jamie E.
    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.
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    Expected seismic performance of gravity dams using machine learning techniques
    (New Zealand Society for Earthquake Engineering, 2021) Segura, Rocio; Padgett, Jamie; Paultre, Patrick
    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.
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    Metamodel-Based Seismic Fragility Analysis of Concrete Gravity Dams
    (ASCE, 2020) Segura, Rocio; Padgett, Jamie E.; Paultre, Patrick
    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 O4) 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.
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    Seismic Performance Assessment of a Retrofitted Bridge with Natural Rubber Isolators in Cold Weather Environments Using Fragility Surfaces
    (ASCE, 2022) Bandini, Pedro Alexandre Conde; Siqueira, Gustavo Henrique; Padgett, Jamie Ellen; Paultre, Patrick
    Rubber-based seismic isolation has been demonstrated to be one of the most effective measures to protect structural elements from damage during earthquakes and a viable option to retrofit existing structures with poor seismic detailing. The main constituent of these isolation units is rubber, a material that is subject to stiffening when exposed to low air temperatures. In the case of isolated highway bridges, thermal stiffening might reduce the efficiency of isolators, transferring higher forces to the substructure. Assessment of the seismic response of retrofitted structures using rubber isolators in cold regions is thus necessary. Accordingly, in this study, the effect of low temperatures on the seismic performance of a highway bridge retrofitted with natural rubber (NR) isolators is quantified using a probabilistic framework based on fragility surfaces. From the component- and system-level surfaces, it is revealed that the effects of cold temperatures on highway bridges retrofitted with elastomeric isolators may be negligible, depending on the configuration of lateral restraining structures. However, when isolators are able to perform their function without impediment, their thermal stiffening might be significantly detrimental to the bridge’s substructure, mainly affecting bent columns.