Browsing by Author "Padgett, Jamie Ellen"

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    Ground Motion Intensity Measure Selection for Probabilistic Seismic Risk Assessment of Multi-response Structural Systems
    (2020-05-01) Du, Ao; Padgett, Jamie Ellen
    Seismic hazards can pose devastating regional impacts and lead to significant structural damages, economic losses and casualties, as observed in the past major earthquake events. Moreover, with rapid urbanization and population growth, especially in earthquake-prone zones, exposures to seismic threats are also heightening. In the future, seismic hazards will continue existing as major threats to the human built environment. Therefore, confident probabilistic seismic risk assessment (PSRA) of the built environment, is critical to informing decision-making such as retrofit prioritization, pre-event planning and risk mitigation, post-event response, as well as insurance underwriting or risk financing. However, the current PSRA framework largely relies on scalar conditioning metrics with an underlying conditional independence assumption, which is often times violated in reality. As a result, different conditioning metric selection can lead to drastically different seismic risk estimates. In this regard, the overarching goal of this thesis is to facilitate more robust and confident PSRA of general multi-response structural systems, with particular focus on intensity measure (IM) selection and uncertainty propagation. First of all, this thesis addresses a long standing question in PSRA, which is the lack of multivariate hazard consistent ground motion selection for use in probabilistic seismic demand estimation of structures. Specifically, a novel multivariate return period (MRP)-based ground motion selection methodology is proposed. MRP generalizes the return period concept by incorporating the joint rate of exceedance of a vector of IMs, thereby providing more holistic characterization of the seismic hazard. By leveraging MRP in linking the level of seismic hazard to a vector of IMs, the proposed MRP-based methodology for the first time achieves multivariate hazard consistency over a vector of IMs, and outperforms all the state-of-the-art ground motion selection alternatives. This thesis also proposes new approaches for surrogate demand modeling of complex multi-response structural systems under earthquake excitation. By leveraging advanced multivariate statistical and machine learning techniques, multivariate surrogate demand models (MvSDMs) are developed to facilitate more unified and joint demand estimation and uncertainty propagation. The formulation of the MvSDMs consists of two major components including a systematic trend model to characterize the mean response hypersurface, and an error covariance model to quantify the correlated model errors. The efficacy of different MvSDMs is thoroughly examined in terms of both predictive performance and system fragility curves, and promising MvSDM alternatives are identified. A preliminary general IM comparative study is then carried out to examine the explanatory power potential and the applicability of different IM formulations, including those conventional IMs, recently proposed advanced IMs, and other potential IM candidates not yet studied. The IM comparative study is based on general hysteretic single-degree-of-freedom (SDOF) systems with a wide range of structural parameters. The underlying mechanisms of different IM formulations are explored and promising IM formulations are identified. Finally, enabled by the above-mentioned study and information theory, an entropy-based IM selection methodology is proposed. This is the first IM selection approach able to holistically consider multiple sources of uncertainties, all the way from seismic hazards to demand modeling. Practical heuristics and workflow are developed to enable entropy-based IM selection in both site-specific and regional-level PSRA. The efficacy of the proposed IM selection method and the influence of IM selection on PSRA of individual structures as well as spatially distributed structural portfolios is thoroughly evaluated. Moreover, the influence of vector-IM record updating on uncertainty reduction of spatial ground motion random field as well as on risk estimates in post-event regional-level PSRA is examined. Overall, this thesis provides powerful tools and methodologies for ground motion selection, multivariate surrogate demand modeling, and IM selection in PSRA, which collectively contribute to more confident seismic risk estimates of general multi-response structural systems, and better inform decision making under earthquake hazards.
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    Seismic fragility assessment of typical bridges in Northeastern Brazil
    (SciELO, 2022) Cavalcante, Gustavo Henrique Ferreira; Pereira, Eduardo Marques Vieira; Rodrigues, Isabela Durci; Vieira Júnior, Luiz Carlos Marcos; Padgett, Jamie Ellen; Siqueira, Gustavo Henrique
    This paper presents a seismic fragility assessment of bridges commonly found in Northeastern Brazil. A generic three-dimensional nonlinear finite-element model is generated in OpenSees to enable variation of geometric features and component modeling. A parametric analysis is performed to evaluate the impact of the geometric and physical variations of the bridge inventory on the seismic behavior of the structures. Nonlinear time-history analyses using four sets of natural earthquake records are performed to obtain the Probabilistic Seismic Demand Model for the bridges. Capacity models are adopted according to previous studies to be combined with demand models to generate fragility functions. This article helps the decision markers to predict the seismic behavior of typical bridges in Northeastern Brazil, which enables the evaluation of risk mitigation methods.
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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.
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    Seismic Resilience of Rail-Truck Intermodal Freight Transportation Networks
    (2020-08-14) Misra, Sushreyo; Padgett, Jamie Ellen
    Railway and highway networks constitute the backbone of the US freight transportation network, and the rail-truck intermodal combination constitutes a popular emerging mode of freight transport. Although components of the intermodal network, namely railway bridges, highway bridges, roadways, railway tracks and intermodal terminals have suffered damage in past earthquakes with potentially significant economic consequences, a framework for assessing intermodal network resilience incorporating key component level input models is lacking in the literature. This study introduces a framework for quantifying the time evolving functionality and consequently resilience of rail-truck intermodal freight transportation networks subjected to seismic hazard, incorporating input datasets and models that support the framework. The intermodal network is modeled as an integrated multi-scale network, enabling explicit modeling of network component disruptions on a high-resolution local scale near the site of the disruption event, as well as modeling resulting network throughput on a nationwide scale. In addition to formulating the overarching framework for resilience modeling of intermodal transportation networks, this thesis addresses pressing gaps in modeling the fragility and restoration of constituent components of these systems. Fragility models offer conditional probabilities of physical damage given the intensity of the hazard as well as other structural parameters, offering key input to overall resilience assessment of these networks. A new fragility modeling approach is proposed leveraging elastic nets regularization and logistic regression, and given that they are altogether lacking in the literature, this method is applied to derive new fragility models for typical railway bridge classes subjected to seismic hazards. Restoration models used in the resilience modeling framework, providing estimates of closure decisions and durations given damage states of intermodal network components, are scarce in the literature. Those that exist suffer from the use of limited expert opinion data and lack sufficient insights to relate practical estimates of closure to functionality. To this end, new restoration models are proposed for network components leveraging decision trees and clustered random forests, estimating both decisions and durations of complete closure as well as partial closures (e.g. speed restriction and load restriction). In addition to this, a fault tree model is proposed to model intermodal terminal functionality, enabling integrated assessment of rail and highway networks including explicit modeling of the performance of the nodes of freight transfer. Finally, a restoration scheduling strategy is proposed for optimal allocation of repair crew and corresponding network flows under limited resources, aiming to minimize costs from various stakeholders’ perspectives while ensuring shipment demands are satisfied as far as possible. The framework and input models are tested using a case study analysis on the intermodal network of Memphis, TN subjected to an earthquake originating in the nearby New Madrid Seismic Zone. Overall, this thesis provides a framework for estimating the resilience of intermodal freight networks, while addressing gaps in key input models required to support the framework. The proposed framework and input models will be integrated within Interdependent Networked Community Resilience Environment (INCORE), an open source tool for community resilience modeling currently in development within National Institute of Standard and Technology (NIST) funded Center of Excellence in Community Resilience Planning. As illustrated in the case study application, this framework allows exploration of central questions in infrastructure resilience assessment, such as the spatial distribution of damage and relative impact of various hazard events; the temporal evolution of component and network level performance; the probability distribution of alternative resilience metrics specific to intermodal freight networks; or the impact of different approaches to restoration scheduling and post-event resource deployment. Furthermore, the models posed herein form a basis for probing broader questions in community resilience planning and decision-making, where the resilience of intermodal transportation infrastructure can have major implications on economic or social systems modeling given their role in goods transport, business activity and employment, and recovery of a community.
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    Seismic Risk Assessment of Vertical Concrete Dry Casks
    (2018-04-20) Ebad Sichani, Majid; Padgett, Jamie Ellen
    An appreciable portion of the electricity generated in the United States (U.S.) is provided by nuclear power plants, making the U.S. the largest producer of nuclear power in the world. In order to ensure public safety and effective plant operation, the spent nuclear fuel (SNF) removed from nuclear reactors needs to be stored safely and efficiently. Due to the lack of long-term storage options, the operational period of dry storage structures, originally designed as interim storage solutions, is being extended beyond their intended design life. Among various dry storage structures, this study focuses on vertical concrete dry casks due to their susceptibility to lateral loads, and specifically explores the response of dry casks to seismic and seismically-induced impact events given their alarming performance in past earthquakes, such as the August 23, 2011, earthquake in Virginia. Owing to their design and orientation, dry casks are susceptible to sliding or rocking during seismic events, potentially resulting in collision between adjacent casks or impact damage from tip-over, with concerns regarding structural damage and release of radioactive material. This study explores the seismic behavior of dry casks and their response to tip-over and collision loads by using a probabilistic method that leverages validated finite element models and introduces surrogate modeling of cask behavior in seismic and impact events. Specifically, these surrogate models of key structural responses in the seismic, tip-over and collision events render fragility, sensitivity, and risk analyses for dry casks computationally feasible. By covering a broad parameter space, the models derived afford the opportunity to explore the relative vulnerability of alternative designs to seismic loads, or to consider, for the first time, the influence of temporal parameter variations and aging effects on the risk of cask damage. The probabilistic approach adopted herein enables propagating the uncertainty due to geometric, material and structural parameters as well as the seismic hazard, enhancing seismic risk analysis of this key storage component in the nuclear industry. Along the way to evaluating fragility and risk to dry casks, this thesis uncovers viable surrogate models for estimating the behavior of dry casks under extreme loads, including polynomial response surface models, multivariate adaptive regression splines, regression trees, and support vector machines for regression. Since the duration and response parameters of interest differ significantly in seismic response and impact scenarios, these insights can offer a valuable foundation for future studies that take advantage of surrogate models for structural multi-hazard reliability and risk problems. In the seismic analysis of casks, a new methodology for probabilistic seismic demand modeling is proposed, referred to as a two-layer surrogate modeling strategy. The two-layer approach proposed offers an advance over traditional probabilistic seismic demand modeling methods, by introducing estimation of intermediate predictors with key links to the problem physics, thereby improving the performance of the developed surrogate models for critical response parameters. Although applied in this thesis to dry casks, the approach has potential widespread application to studying the probabilistic seismic response of other rigid-body-type structures, such as bridges with rocking foundations, rigid blocks, statues or laboratory equipment. The models that emerge from this study provide key insights into the important parameters that affect cask response and fragility. Furthermore, they offer an estimate of the risk of exceeding key cask response limits, indicative of potential damage or design concerns. For example, sensitivity analyses on seismic fragility models of the dry casks are conducted to reveal the relative importance of such parameters as the frequency content of the earthquake, friction coefficient, and key geometric parameters on the likelihood of exceeding sliding and rocking limits. Comparative risk estimates across the U.S. integrate the seismic hazard potential with the derived parameterized fragility models to reveal the annual risks of limit state exceedance for alternative designs and cask locations. Probabilistic analysis of impact scenarios explore the role of cask aging and temporal temperature variations in affecting such responses as canister strains or cask accelerations under tip-over and collision events. Given the concern for release of radioactive material to the environment in the event of cask impact, the models provide valuable information for engineers, plant owners and decision makers in the nuclear industry.