R-3 Repository :: Browsing by Author "Padgett, Jamie E."

Browsing by Author "Padgett, Jamie E."

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A Markov chain-based model for structural vulnerability assessmentof corrosion-damaged reinforced concrete bridges
(The Royal Society, 2021) Dizaj, Ebrahim Afsar; Padgett, Jamie E.; Kashani, Mohammad M.
The deterioration and cracking of reinforced concrete (RC) bridges due to the chloride-induced corrosion of steel reinforcement is an inherently time-dependent stochastic phenomenon. In the current practice of bridge management systems, however, the determination of the condition states of deteriorated bridges is highly dependent on the opinion of experienced inspectors. Taking such complexity into account, the current paper presents a new stochastic predictive methodology using a non-homogeneous Markov process, which directly relates the visual inspection data (corrosion rate and crack widths) to the structural vulnerability of deteriorated concrete bridges. This methodology predicts the future condition of corrosion-induced damage (concrete cracking) by linking structural vulnerability analysis and a discrete-time Markov chain model. The application of the proposed methodology is demonstrated through a case-study corrosion-damaged RC bridge pier.This article is part of a discussion meeting issue ‘A cracking approach to inventing new tough materials: fracture stranger than friction’.
A new mutually reinforcing network node and link ranking algorithm
(Macmillan Publishers Limited, 2015) Wang, Zhenghua; Dueñas-Osorio, Leonardo; Padgett, Jamie E.
This study proposes a novel Normalized Wide network Ranking algorithm (NWRank) that has the advantage of ranking nodes and links of a network simultaneously. This algorithm combines the mutual reinforcement feature of Hypertext Induced Topic Selection (HITS) and the weight normalization feature of PageRank. Relative weights are assigned to links based on the degree of the adjacent neighbors and the Betweenness Centrality instead of assigning the same weight to every link as assumed in PageRank. Numerical experiment results show that NWRank performs consistently better than HITS, PageRank, eigenvector centrality, and edge betweenness from the perspective of network connectivity and approximate network flow, which is also supported by comparisons with the expensive N-1 benchmark removal criteria based on network efficiency. Furthermore, it can avoid some problems, such as the Tightly Knit Community effect, which exists in HITS. NWRank provides a new inexpensive way to rank nodes and links of a network, which has practical applications, particularly to prioritize resource allocation for upgrade of hierarchical and distributed networks, as well as to support decision making in the design of networks, where node and link importance depend on a balance of local and global integrity.
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.
Application of Compact, Geometrically Complex Shape Memory Alloy Devices for Seismic Enhancement of Highway Bridge Expansion Joints
(2014-04-25) McCarthy, Emily Ruth; Padgett, Jamie E.; Stanciulescu, Ilinca; Lou, Jun; DesRoches, Reginald
Highway bridges are an important part of transportation networks. They provide connectivity across waterways, ravines and other roadways, reducing commuting times and facilitating social community. The disruption of their effective operation caused by earthquake damage has lasting effects based on repair costs, road closure times, traffic rerouting causing extended commute times and additional CO2 emissions, and the potential prevention of emergency responders being able to reach affected regions. Bridge expansion joints have historically been recognized as the most vulnerable component in the bridge system during these seismic events, causing dramatic disruption to bridge functionality because of their location in bridges (points of discontinuity in deck systems). Expansion joint systems are placed in these locations of discontinuity and accommodate bridge movements from thermal effects while facilitating safe driving surfaces across large gaps in the roadway. Commonly installed systems are not designed to survive seismic events, instead failure is assumed and replacement necessary to return the bridge to its functional state. When damaged, the large gaps they span can be un-crossable without external intervention, resulting in non-functioning bridges even when the structural system remains sound. Expensive and complex expansion systems exist, which prevent seismic damage, however they are used mostly in highly seismic regions and limitedly elsewhere. This dissertation provides an expansion joint design that is economical and superior in seismic performance to the commonly installed service level expansion joints so that more bridges in moderate seismic regions can be equipped with expansion systems able to accommodate large longitudinal displacement demands from earthquakes. The use of innovative shape memory alloy (SMA) springs enables a single support bar modular bridge expansion joint (one type of large capacity expansion joint) to accommodate seismic level longitudinal displacements while maintaining existing performance behavior for service level thermal expansion demands. Through limited alteration of the existing configuration, costs are minimized. The resulting design is experimentally and analytically shown to be superior in performance and able to prevent expansion joint system failure during dynamic loading. The use of fragility curves, which are probabilistic statements of demand exceeding capacity, offers a means of measuring performance over a range of earthquake intensities. Convolution with seismic hazard curves for some moderate seismic zones in the US over a range of time intervals provide information on lifetime seismic risk, valuable information for a cost benefit analysis that concludes investment in SMA springs for enhancement of modular bridge expansion joints is worthwhile for the cost reduction they offer over the life of the bridge.
Assessing the accessibility of petrochemical facilities during storm surge events
(Elsevier, 2019) Bernier, Carl; Gidaris, Ioannis; Balomenos, Georgios P.; Padgett, Jamie E.
Recent hurricane events have exposed the susceptibility of petrochemical facilities to severe transportation network disruptions due to flooding or storm surge. Network disruptions can result in cascading impacts or amplify the consequences of damage to petrochemical infrastructure due to delayed emergency response and limited access to the site. This study presents a scenario-based framework to assess the accessibility of petrochemical facilities by emergency responders and workers during storm surge events. First, the framework couples storm surge modeling with aboveground storage tank fragility models to determine the locations where natural hazard-triggered technological (NaTech) events could occur. Then, storm surge modeling is coupled with bridge fragility models and geographic system analysis to evaluate the potential for network disruptions such as bridge failures and road inundations. Finally, probabilistic network analyses are performed to evaluate the time-evolving accessibility of NaTech sites to emergency responders and facility workers. As a proof of concept, the framework is applied to a case study area. Results for the case study area demonstrate that the proposed framework is a powerful tool to quantify the accessibility of potential NaTech events, facilitate mitigation and emergency activities, and improve the management of critical resources and personnel during and after a storm.
Buckling of aboveground storage tanks subjected to storm surge and wave loads
(Elsevier, 2019) Bernier, Carl; Padgett, Jamie E.
This study investigates the buckling behavior of aboveground storage tanks (ASTs) subjected to storm surge and wave loads during hurricane events and explores the importance of dynamic effects on the buckling behavior. First, a computational fluid dynamics model is developed to estimate water pressures on ASTs subjected to wave loads. The modeling assumptions of this model are also validated with experimental data. Next, a methodology is presented to perform dynamic buckling analysis of ASTs subjected to surge and wave loads by adapting procedures commonly used for ASTs subjected to wind or seismic loads; the methodology is illustrated with a case study AST located in the Houston Ship Channel. For comparison, static buckling analyses are also performed to determine the significance of dynamic effects. Lastly, design of experiments principles and regression analyses are employed to investigate the effects of varying AST and loading parameters on the buckling behavior and the relative importance of dynamic effects. Results indicate that wave loads can significantly affect the buckling behavior of ASTs subjected to storm surge and need to be considered, while the dynamic effects induced by waves have a negligible influence on the buckling strength of ASTs. Simpler and computationally inexpensive static buckling analysis provides reasonable estimates for ASTs subjected to surge and waves. However, dynamic buckling analysis might still be required if the objective is to assess the post-buckling behavior of ASTs subjected to waves, rather than only to estimate the critical load.
Case studies of multihazard damage: Investigation of the interaction of Hurricane Maria and the January 2020 earthquake sequence in Puerto Rico
(Frontiers, 2023) Hain, Alexandra; Zaghi, Arash E.; Padgett, Jamie E.; Tafur, Anibal
This paper is motivated by the unique findings and observations from reconnaissance visits after the earthquake series in Puerto Rico in January 2020. It aims to discuss the potential interactions of Hurricane Maria and 2020 earthquake series and the considerations they underscore for future field reconnaissance missions. Traditionally, post-disaster damage assessment activities focus on one hazard and overlook the potentially cascading effects of multiple hazards on structures and infrastructure. This paper provides case studies showing the possible interaction of multiple hazards and their cascading effects observed in Puerto Rico. Infrastructure surveyed includes port facilities, buildings (particularly historical structures), and bridge structures. The data collected during the reconnaissance missions reveal how the impacts of Hurricane Maria, along with infrastructure aging and delayed repair and recovery activities, may have influenced the damage level and failure modes observed during the earthquake sequence a few years after. These case studies illustrate the nature of multihazard interactions and how these effects should be documented during post-disaster assessments. Beyond the insights gained from the case studies illustrated in this paper, the field survey instrument is provided as a basis for future reconnaissance studies, and the full set of reconnaissance data collected are published on the NSF funded NHERI DesignSafe cyberinfrastructure. As a result, this work not only provides data from Puerto Rico that can inform future damage and recovery modeling efforts, but also offers survey instruments and a field data collection process that is particularly tailored to cases where multihazard effects are at play.
Developing Innovative Designs with Manufacturing Capability Using the Level Set Method
(2012-09-05) Baradaran Nakhjavani, Omid; Meade, Andrew J., Jr.; Akin, John Edward.; Padgett, Jamie E.; Dick, Andrew J.
This thesis discusses how to use topology and shape optimization, specifically the level set method, for innovative design. The level set method is a numerical algorithm that simulates the expansion of dynamic implicit surfaces. In this research, the equations for manufacturability are generated and solved through use of the level set method joined with the COMSOL multi-physics package. Specific constraints are added to make the optimization practical for engineering design. The resulting method was applied to design the best underlying support structure, conforming to both curvature and manufacturability constraints, for the longerons used with the International Space Station solar panels.
Dynamic stability of elastomeric seismic isolation bearings and seismic protection using enhanced adaptive negative stiffness system
(2013-09-16) Vemuru, Venkata; Nagarajaiah, Satish; Padgett, Jamie E.; Dick, Andrew J.
In this study the issue of stability of elastomeric bearings subjected to extreme dynamic loading is studied in detail and response reduction strategies in structures both fixed base and base-isolated structures using a negative stiffness device (NSD) are evaluated. For response reduction, the composite response spectrum approach is utilized to provide a comprehensive representation of the trade-offs associated with each choice of design strategies. A comprehensive database of strong ground motion is used to study the suitability of NSD for seismic protection. The spectral characteristics of all the ground motions selected are identified using a mathematical model and the characteristics of the ground motion that affect the structural assembly and the response reduction strategies considered are evaluated. The results of the study are organized based on the identified characteristics of ground motion to demonstrate the effectiveness of the negative stiffness device for response reduction. The analytical study takes into account both linear fixed base structures as well as base-isolated structures equipped with linear and nonlinear dampers. For stability of elastomeric bearings, detailed analytical models that capture the nonlinear behavior of the bearings under extreme axial loads and horizontal displacements are developed, calibrated and verified using experimental data. A new analytical model is developed to capture the highly nonlinear horizontal behavior of the bearings under dynamic loading. This includes both the response under normal operating conditions as well as the instability experienced by the bearings under large axial loads and extreme horizontal displacements. The analytical model developed for the horizontal behavior of bearings is enhanced to include the coupled horizontal-vertical behavior of the bearings with the main objective of understanding the actual behavior of the bearings at the instant it experiences loss of stability. An important distinction is made between the geometrical effects and deformation of the bearing that contributes to its vertical response at this instant. The important effect the increased vertical reaction has on the bearing stability is demonstrated and an empirical analytical model is developed to capture this effect. By using the analytical models developed above for elastomeric bearings the response of a six storeyed structure isolated using elastomeric bearings is studied for different ground motions. The effectiveness of the NSD in reducing the response of the structure for different ground motions is demonstrated based on this study and the findings are organized using the identified characteristics of the ground motion. By identifying completely the limitations of the isolation system and subjecting it to actual ground motions the behavior of the system under extreme ground motions is studied. For instances where the bearings fail due to severe ground motions, addition of an NSD not only reduces the response of the superstructure but also protects the bearings by retaining their stability. Stability retention is mainly achieved due to a decrease in the imposed axial loads in the system with an NSD. A fail-safe NSD with mechanical displacement feedback is proposed to prevent accidental failure of isolation systems and its effectiveness is demonstrated for severe ground motion using analytical study.
Enabling efficient regional seismic fragility assessment of multi-component bridge portfolios through Gaussian process regression and active learning
(Wiley, 2024) Ning, Chunxiao; Xie, Yazhou; Burton, Henry; Padgett, Jamie E.
Regional seismic fragility assessment of bridge portfolios must address the embedded uncertainties and variations stemming from both the earthquake hazard and bridge attributes (e.g., geometry, material, design detail). To achieve bridge-specific fragility assessment, multivariate probabilistic seismic demand models (PSDM) have recently been developed that use both the ground motion intensity measure and bridge parameters as inputs. However, explicitly utilizing bridge parameters as inputs requires numerous nonlinear response history analyses (NRHAs). In this situation, the associated computational cost increases exponentially for high-fidelity bridge models with complex component connectivity and sophisticated material constitutive laws. Moreover, it remains unclear how many analyses are sufficient for the response data and the resulting demand model to cover the entire solution space without overfitting. To deal with these issues, this study integrates Gaussian process regression (GPR) and active learning (AL) into a multistep workflow to achieve efficient regional seismic fragility assessment of bridge portfolios. The GPR relaxes the probability distribution assumptions made in typical cloud analysis-based PSDMs to enable heteroskedastic nonparametric seismic demand modeling. The AL leverages the varying standard deviation to select the least but most representative bridge-model-ground-motion sample pairs to conduct NRHA with much-improved efficiency. Both independent and correlated multi-output GPRs are proposed to deal with bridge portfolios with seismic demand correlations among multiple components (column, bearing, shear key, abutment, unseating, and joint seal). Considering a single benchmark highway bridge class in California as the case study, the AL-GPR framework and the associated component-level fragility results are investigated in terms of their efficiency, accuracy, and robustness. The fragility results show that 70 AL-selected samples would enable the GPR to derive bridge-specific fragility models comparable to the ones using the multiple stripes analysis approach with 1950 ground motions considered for each individual bridge. The AL-GPR model also successfully captures the physics of how bridge span length, deck area, column slenderness, and steel reinforcement ratio would change the damage state exceedance probabilities of different bridge components. The efficiency of AL stems from the fact that, with the multi-output independent GPR, a stable and reliable fragility model can be achieved using 50 AL-selected samples compared to at least 270 randomly chosen samples. The proposed methodology advances the state of the art in enabling more efficient and reliable regional seismic fragility assessment of multi-component bridge portfolios.
Enhancing Research in Natural Hazards Engineering Through the DesignSafe Cyberinfrastructure
(Frontiers Media S.A., 2020) Rathje, Ellen M.; Dawson, Clint; Padgett, Jamie E.; Pinelli, Jean-Paul; Stanzione, Dan; Arduino, Pedro; Brandenberg, Scott J.; Cockerill, Tim; Esteva, Maria; Haan, Fred L. Jr.; Kareem, Ahsan; Lowes, Laura; Mosqueda, Gilberto
The DesignSafe cyberinfrastructure (www.designsafe-ci.org) is part of the NSF-funded Natural Hazard Engineering Research Infrastructure (NHERI) and provides cloud-based tools to manage, analyze, understand, and publish critical data for research to understand the impacts of natural hazards. The DesignSafe Data Depot provides private and public disk space to support research collaboration and data publishing through a web interface. The DesignSafe Reconnaissance Portal uses a map interface to provide easy access to data collected to investigate the effects of natural hazards, and the DesignSafe Workspace provides cloud-based tools for simulation, data analytics, and visualization; as well as access to high performance computing (HPC). This paper provides an overview of the DesignSafe cyberinfrastructure and describes specific examples of the use of DesignSafe in research for natural hazards. These examples include electronic data reports that use Jupyter notebooks to allow researchers to interrogate data interactively within the web portal, computational workflows that integrate ensembles of HPC-based simulations and surrogate modeling, and the publication of field research data after natural hazard events that utilize a variety of DesignSafe tools. The paper also provides an overall assessment of current DesignSafe impact and usage, demonstrating how DesignSafe is enhancing research in natural hazards.
Fragility and risk assessment of aboveground storage tanks subjected to concurrent surge, wave, and wind loads
(Elsevier, 2019) Bernier, Carl; Padgett, Jamie E.
Comprehensive tools to assess the performance of aboveground storage tanks (ASTs) under multi-hazard storm conditions are currently lacking, despite the severe damage suffered by ASTs in past storms resulting in the release of hazardous substances. This paper presents a rigorous yet efficient methodology to develop fragility models and perform risk assessments of ASTs subjected to combined surge, wave, and wind loads. Parametrized fragility models are derived for buckling and dislocation from the ground. The buckling strength of ASTs is assessed using finite element analysis, while the stability against dislocation is evaluated using analytical limit state functions with surrogate modeling-based load models. Scenario and probabilistic risk assessments are then performed for a case study region by convolving the fragility models with hazard models. Results demonstrate that the derived fragility models are efficient tools to evaluate the performance of ASTs in industrial regions. Insights obtained from the fragility and risk assessments reveal that neglecting the multi-hazard nature of storms, as existing studies have done, can lead to a significant underestimation of vulnerability and risks. This paper also highlights how using surrogate model techniques can facilitate and reduce the computational complexity of fragility and risk assessments, particularly in multi-hazard settings.
A global analysis of coastal flood risk to the petrochemical distribution network in a changing climate
(Elsevier, 2022) Capshaw, Kendall M.; Padgett, Jamie E.
The global petroleum distribution network already faces a significant threat of disruption due to annual coastal flooding of major refining centers, which is expected to further increase with the effects of climate change. This study considers the impacts that sea level rise projections might have on the annual flood risk to coastal refineries, and how regional disruptions propagate across the network. Both the annual regional risk in terms of expected production disruption under a range of climate scenarios, as well as the expected production disruption due to a major flood event impacting refining hubs of high importance are assessed throughout the 21st century. These risks are propagated across the network to model the global impact of coastal flood-induced refining disruptions. This analysis provides insights on the relative risks that different climate scenarios and flood events pose globally, informing potential mitigation and adaptation needs of critical facilities. Due to the highly interconnected nature of the global petroleum product distribution network, these results highlight the need for mitigation considerations for even regions with low domestic production disruption risk due to coastal flood hazards, as disruptions in remote regions can have cascading consequences resulting in significant disruption to petroleum product supply around the world. Furthermore, such results can inform decisions regarding technology transitions or energy diversification in light of the new understanding of climate risks to coastal refineries and the global petroleum distribution network.
Hurricane Risk Assessment of Petroleum Infrastructure in a Changing Climate
(Frontiers, 2020) Sichani, Majid Ebad; Anarde, Katherine A.; Capshaw, Kendall M.; Padgett, Jamie E.; Meidl, Rachel A.; Hassanzadeh, Pedram; Loch-Temzelides, Ted P.; Bedient, Philip B.
Hurricanes threaten the petroleum industry in the United States and are expected to be influenced by climate change. This study presents an integrated framework for hurricane risk assessment of petroleum infrastructure under changing climatic conditions, calculating risk in terms of monetary loss. Variants of two synthetic probabilistic storms and one historical storm (Hurricane Ike) are simulated using the SWAN+ADCIRC model, representing a range of potential scenarios of impacts of a changing climate on hurricane forward speed and sea-level rise given uncertainties in climate projections. Model outputs inform an infrastructure impact and cascading economic loss analysis that incorporates various sources of uncertainty to estimate five types of losses sustained by petroleum facilities in surge events: land value loss, process-unit damage loss, cost of spill clean-up and repair of aboveground storage tanks, productivity loss, and civil fines. The proposed risk assessment framework is applied as a case study to seven refineries along the Houston Ship Channel (HSC), a densely-industrialized corridor in Texas. The results reveal that either an increase in mean sea level or a decrease in storm forward speed increases the maximum water elevations in the HSC for storms that produce maximum wind setup in Galveston Bay (FEMA 33 and FEMA 36), resulting in larger economic loss estimates. The role of refinery features such as storage capacity and average elevation of the refinery and its critical equipment in the refinery response to hurricane hazards is studied, and the probability distribution of refinery total loss and the loss risk profile in different hurricane scenarios are discussed. Loss estimates are presented, demonstrating the effects of hurricane forward speed and sea level on the losses for the refineries as well as the HSC. Such a framework can enable hurricane risk assessment and loss estimation for petroleum infrastructure to inform future policies and risk mitigation strategies. Potential policy implications for a region like the HSC are highlighted herein as an illustration.
Improved Seismic Risk Assessment of Non-ductile Reinforced Concrete Buildings
(2014-02-28) Fuselier, Blaine; Padgett, Jamie E.; Duenas-Osorio, Leonardo; Nagarajaiah, Satish
Existing reinforced concrete (RC) buildings built to non-ductile specifications are highly susceptible to damage given lateral loads induced from earthquake ground motions. To explore the effects of these ground motions, non-linear finite element analyses are being used in research and practice to model representations of non-ductile RC buildings as well as conduct probabilistic analyses of their seismic fragility in as-built and retrofitted conditions. This study examines the influence of modeling fidelity on the response and fragility of non-ductile RC buildings, testing the role of explicitly capturing local failure in the finite element model as well as providing new insight into the probability of component damage levels given system level failure. Also, a survey is presented to assess the tagging decisions made during post-earthquake rapid evaluations of reinforced concrete buildings and compare these results to empirical data from past earthquake reconnaissance reports. The results of this study will provide insight into several key issues in seismic performance assessment for RC buildings.
Influence of Vertical Ground Motions on the Seismic Fragility Modeling of a Bridge-Soil-Foundation System
(Earthquake Engineering Research Institute, 2013-08) Wang, Zhenghua; Padgett, Jamie E.; Dueñas-Osorio, Leonardo
This paper explores the effects of vertical ground motions (VGMs) on the component fragility of a coupled bridged-soil-foundation (CBSF) system with liquefaction potential, and highlights the unique considerations on the demand and capacity model required for fragility analysis under VGMs. Optimal intensity measures (IMs) that account for VGMs are identified. Moreover, fragility curves that consider capacity change with fluctuating axial force are derived. Results show that the presence of VGMs has a minor effect on the failure probabilities of piles and expansion bearings, while it has a great influence on fixed bearings. Whether VGMs have an impact on column fragilities depends on the design axial load ratio. Finally, more accurate fragility surfaces are derived, which are compared with results of conventional fragility curves. This study highlights the important role that VGMs play in the selection of optimal IMs, and the capacity and fragility representation of certain components of CBSF systems.
Investigation of mitigation strategies to reduce storm surge impacts associated with oil infrastructures
(IASSAR, 2017) Bernier, Carl; Padgett, Jamie E.; Elliott, James R.; Bedient, Philip B.
This paper evaluates different mitigation strategies to reduce the risks posed by aboveground storage tanks and the vulnerability of nearby communities. A framework integrating natural hazard exposure, structural vulnerability, and social vulnerability is proposed to investigate the effects and the viability of different mitigation strategies.
Life-cycle cost-benefit (LCC-B) analysis for bridge seismic retrofits
(2010) Dennemann, Kristina L.; Padgett, Jamie E.
Bridges constitute key elements of the nation's infrastructure and are subjected to considerable threats from natural hazards including seismic events. A range of potential bridge retrofit measures may be used to limit seismic damage in deficient bridges, and help mitigate associated social and economic losses. However, since resources are often limited for investment in seismic upgrade, particularly in regions of intense but infrequent events, a risk-based approach for evaluating and comparing the cost effectiveness of different mitigation strategies is warranted. This thesis illustrates a method for evaluating the best retrofits for non-seismically designed bridges based on seismic life-cycle costs and cost-benefit analysis. The approach integrates probabilistic seismic hazard models, fragility of as-built and retrofitted bridges for a range of damage states, and associated costs of damage and retrofit. The emphasis on life-time performance and benefits, as opposed to initial retrofit cost alone, not only permits risk-wise investment, but also helps to align upgrade actions with highway agency missions for sustainable infrastructure.
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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Methods and Tools for Risk-informed Resilience Enhancement of Coastal Intermodal Freight Networks
(2024-12-06) Tafur Gutierrez, Anibal; Padgett, Jamie E.
Coastal intermodal freight networks (CIFNs)—comprising maritime, roadway, and railway corridors—are vital to the economies of coastal communities. These networks facilitate efficient cargo transfers, thereby underpinning supply chains. However, they are increasingly vulnerable to disruptions caused by extreme coastal hazards, which are being intensified by climate change and population growth. Such disruptions, as evidenced by past hurricane events, can have devastating effects, with impacts rippling through communities and leading to both short- and long-term socio-economic consequences. Given these challenges, it is essential to model the recovery and resilience of CIFNs to better assess their capacity to restore cargo-transfer functionality and to inform planning efforts aimed at enhancing this capability. Despite the importance of this task, current modeling methods have significant limitations. These include a lack of consideration for multiple commodity types, high computational complexity, limited ability to accommodate stakeholders at various scales, and a failure to effectively incorporate resource-related constraints into the recovery modeling process. This study aims to address these gaps by proposing a multi-scale probabilistic resilience modeling framework tailored to the intricacies and complexity of CIFNs. Specifically, this framework enables a comprehensive characterization of the intermodal network at the component, link, and network levels. It integrates both roadway and railway corridors with port terminals through specially adapted network flow models, enabling the estimation of network-level functionality while accounting for multiple shipments, diverse commodities, and various stakeholders. In addition to posing the overarching framework for resilience modeling of CIFNs, this study addresses a critical gap in modeling flow-based port functionality under disrupted conditions—a key component in simulating the intermodal recovery of post-disaster satisfied demands between maritime and inland corridors. Specifically, this model incorporates the probabilistic availability of port structural and handling components following a disaster, enabling more detailed, physics-based estimations of flow capacity at the terminal level, extending beyond just the handling capacity of cargo at berth areas. Another critical gap this study aims to bridge is the lack of efficient methods for incorporating resource constraints into the modeling of CIFN recovery following extreme coastal hazards. To address this, the study introduces a resource-aware framework that not only considers the available resources for restoration—both monetary and labor—but also how these resources are allocated by decision-makers under post-disaster strained conditions. Specifically, two allocation algorithms are proposed: an optimization-based approach and a heuristics-based approach. These algorithms are designed to explore the influence of the interplay between different conditions of resource availability and allocation efficiency on network-level resilience outcomes. The proposed models and methodologies are demonstrated through three real-world case studies in Mobile and Baldwin counties, Alabama, involving a multi-commodity port site, a railway network, and a fully integrated CIFN that incorporates these systems along with roadway corridors. These case studies assess post-disaster functionality at both local scales (e.g., recovery of port flow capacity) and regional scales (e.g., restoration of satisfied demands across the intermodal network). These resilience analyses are conducted under various hurricane and resource constraint scenarios. Ultimately, the risk-informed, resource-sensitive resilience assessments developed and demonstrated in this study are aimed at supporting broader community resilience enhancement efforts, which have gained critical importance in light of escalating climate-driven disasters. Notably, this work is part of a multidisciplinary effort supported by the NIST Center for Risk-Based Community Resilience Planning. IN-CORE—a key outcome of this initiative—is an open-source suite of tools for community resilience modeling, which will incorporate the data compiled and methodologies proposed in this work, thereby expanding its potential to contribute towards bolstering community resilience in the face of extreme coastal events.

Browsing by Author "Padgett, Jamie E."

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