About

Jamie E. Padgett is the Stanley C. Moore Professor and Department Chair of Civil and Environmental Engineering at Rice University in Houston, TX. She is a structural engineer whose research focuses on multi-hazard risk and resilience modeling of structures and infrastructure systems, with an emphasis on understanding their impacts on communities. Her work develops new methods to quantify and improve the performance of infrastructure exposed to natural hazards such as earthquakes, hurricanes, and flooding. Padgett's research has applications to a range of systems, including bridges, tank farms, energy and industrial facilities, and intermodal transportation systems. She has published over 250 articles in journals or archived conference proceedings in the general area of structural fragility, life-cycle assessment, and infrastructure resilience. Dr. Padgett was the founding Chair of the ASCE/SEI technical committee on Multiple Hazard Mitigation and is an active member of several national technical committees within ASCE and TRB. Her research focuses on the application of probabilistic methods for risk assessment of infrastructure, including the subsequent quantification of resilience and sustainability. Her work emphasizes structural portfolios such as regional portfolios of bridges or oil storage tanks exposed to multiple hazards, including earthquakes, hurricanes, or aging and deterioration.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Political Science
• Machine Learning
• Sociology
• Computer Security
• Geography
• Data science
• Environmental economics
• Statistics
• Systems engineering
• Econometrics
• Environmental planning
• Environmental resource management
• Economics
• Operations research
• Risk analysis (engineering)
• Environmental science
• Knowledge management
• Construction engineering
• Mathematics
• Algorithm
• Transport engineering

Selected publications

• Identifying Efficient Submodel Fidelity Combinations

  Texas Advanced Computing Center · 2026-01-20

  otherOpen accessSenior author

  Not all submodels that encompass infrastructure system performance assessment achieve the same level of fidelity. Even if all reach high-fidelity levels, the required information for accurate regional-scale analysis may not be available. Therefore, this code determines whether a combination of lower-fidelity submodels yields a more efficient Monte Carlo (MC) estimate than using the Most Accurate But Expensive (MABE) model. Rather than leveraging multifidelity MC methods, our approach identifies whether submodels can be completely replaced by reduced-fidelity models without affecting the quality of outcomes. The Python code for identifying ‘efficient submodel fidelity combinations’ in multiscale models is 'Efficient_submodel_fidelity_combinations.py'. Additionally, we have added a model generator function, 'Funct.py'. The latter is used to exemplify the Python class `Efficient_model_selection` (found in the main script). Also, a Jupyter Notebook accompanies the illustrative example and illustrates the convergence plots of the function. Researchers, modelers, and decision-makers can benefit from procedures that objectively guide model selection. By using this selection criterion, we advocate for multiscale models defined at a level of fidelity that suits the intended purpose of the analysis. This project is based on the following manuscript: Rincon R, Padgett JE. (2026). “Identifying submodel fidelity combinations that yield efficient multiscale models for infrastructure performance estimation.” Reliability Engineering and System Safety, In Review, January 2026.

  PublisherDOI

• Tropical Cyclone Wind Fragility Assessment for Industrial Specialty Structures: The Case of Field-Erected Cooling Towers

  Journal of Performance of Constructed Facilities · 2026-04-01

  articleSenior author

  This study presents a parameterized fragility model for understudied industrial specialty structures subjected to tropical cyclone extreme winds using the case of field-erected cooling towers. First, a probabilistic wind demand model is adopted to determine tropical cyclone wind pressures for the envelope components as well as the structural system (main wind force resisting system). The capacities of envelope components are characterized by probability density functions. The structural capacity is modeled using parametric OpenSees models. Monte Carlo simulations for the limit state evaluations are leveraged to train surrogate models that predict conditional damage state probabilities for multiple functionality-based targets. Fragility models parameterized on significant predictors across the hazard intensity and structural portfolio parameter space afford ready application to risk assessment in coastal industrial regions. The derived fragility models are leveraged to obtain the class-average fragility curves and assess the fragilities of the Houston Ship Channel industrial portfolio. Overall, this work addresses gaps in the literature by posing a state-of-the-art method for hurricane wind fragility analysis that parametrically addresses heterogeneous portfolios of specialty structures to allow efficient but tailored damage estimation of industrial structures under tropical cyclone winds.

  PublisherDOI

• A Report on Validation and Mesh Sensitivity Analysis of Computational Fluid Dynamics (CFD) Models

  Texas Advanced Computing Center · 2026-03-04

  datasetOpen accessSenior author

  This document details the validation and mesh sensitivity analyses of computational fluid dynamics (CFD) models used to estimate hydraulic pressure around aboveground storage tanks (ASTs) subjected to storm surge and wave loads. The validation study is based on experimental results from Bernier et al. (2020) on representative aboveground storage tanks subjected to surge and wave loads at the O. H. Hinsdale Wave Research Laboratory (HWRL) at Oregon State University. Results of the validation process showed good agreement between the pressure time history from developed CFD models and the experimental results. Mesh convergence analysis was also performed for full-scale CFD models to identify a computationally efficient mesh size. All CFD analyses performed in this study were run in parallel, leveraging high-performance computing (HPC) resources from TACC (Texas Advanced Computing Center) and the NOTS cluster (operated by Rice University's Center for Research Computing). The validated CFD models were developed to assess load-modification effects from neighboring structures. A three-structure layout configuration is selected (with two ASTs on the upstream row and one on the downstream row) to capture the load-modification effects when structural layout follows a lattice pattern. The feasibility of this configuration for capturing the demand-modification effects of neighboring structures was also tested and presented in the document. The three-structure layout was found to sufficiently preserve hydraulic demand without compromising reliability in load estimation. Hence, these CFD models were used to propose a framework for deriving a parameterized fragility model for a coastal structural portfolio, accounting for the effects of neighboring structures.

  PublisherDOI

• Phase Transitions in Collective Damage of Civil Structures under Natural Hazards

  ArXiv.org · 2026-02-18

  articleOpen access

  The fate of cities under natural hazards depends not only on hazard intensity but also on the coupling of structural damage, a collective process that remains poorly understood. Here we show that urban structural damage exhibits phase-transition phenomena. As hazard intensity increases, the system can shift abruptly from a largely safe to a largely damaged state, analogous to a first-order phase transition in statistical physics. Higher diversity in the building portfolio smooths this transition, but multiscale damage clustering traps the system in an extended critical-like regime (analogous to a Griffiths phase), suppressing the emergence of a more predictable disordered (Gaussian) phase. These phenomenological patterns are characterized by a random-field Ising model, with the external field, disorder strength, and temperature interpreted as the effective hazard demand, structural diversity, and modeling uncertainty, respectively. Applying this framework to real urban inventories reveals that widely used engineering modeling practices can shift urban damage patterns between synchronized and volatile regimes, systematically biasing exceedance-based risk metrics by up to 50% under moderate earthquakes ($M_w \approx 5.5$--$6.0$), equivalent to a several-fold gap in repair costs. This phase-aware description turns the collective behavior of civil infrastructure damage into actionable diagnostics for urban risk assessment and planning.

  PublisherOA PDF

• Phase Transitions in Collective Damage of Civil Structures under Natural Hazards

  Open MIND · 2026-02-18

  preprint

  The fate of cities under natural hazards depends not only on hazard intensity but also on the coupling of structural damage, a collective process that remains poorly understood. Here we show that urban structural damage exhibits phase-transition phenomena. As hazard intensity increases, the system can shift abruptly from a largely safe to a largely damaged state, analogous to a first-order phase transition in statistical physics. Higher diversity in the building portfolio smooths this transition, but multiscale damage clustering traps the system in an extended critical-like regime (analogous to a Griffiths phase), suppressing the emergence of a more predictable disordered (Gaussian) phase. These phenomenological patterns are characterized by a random-field Ising model, with the external field, disorder strength, and temperature interpreted as the effective hazard demand, structural diversity, and modeling uncertainty, respectively. Applying this framework to real urban inventories reveals that widely used engineering modeling practices can shift urban damage patterns between synchronized and volatile regimes, systematically biasing exceedance-based risk metrics by up to 50% under moderate earthquakes ($M_w \approx 5.5$--$6.0$), equivalent to a several-fold gap in repair costs. This phase-aware description turns the collective behavior of civil infrastructure damage into actionable diagnostics for urban risk assessment and planning.

  DOI

• Multi-hazard stochastic time-dependent tropical cyclone fragilities for coastal structures

  2025-01-01

  articleOpen accessSenior author

  This paper presents a reliability-based multilattice topology optimization framework for structural design under uncertainty in material properties.Many natural and engineered materials-such as bone, wood, and polymers-possess hierarchical microstructures and spatially varying mechanical properties, enabling multifunctionality and adaptability.Inspired by these systems, architected lattice materials (also known as metamaterials) are engineered to achieve tunable mechanical performance through controlled porosity and microstructural orientation.These attributes enable the design of heterogeneous, high-performance systems that surpass the capabilities of their individual constituents.Topology optimization (TO) serves as a powerful tool for designing structures with spatially tailored material distributions and hierarchical configurations.However, computing the effective properties of lattice unit cells via homogenization at every optimization step is computationally intensive.To address this challenge, we propose a density-based TO framework that incorporates multiple lattice structures while accounting for material uncertainty.A Kriging-based surrogate modeling approach is employed to efficiently estimate the effective behavior of lattice structures across a range of relative densities, significantly reducing computational effort.Numerical examples involving multiscale lattice configurations demonstrate the feasibility and effectiveness of the proposed framework in generating lightweight, structurally robust designs under probabilistic constraints.The approach provides a scalable solution for engineering applications demanding high performance, adaptability, and resilience under uncertainty. INTRODUCTIONTopology optimization (TO) is a computational method used to determine the optimal material distribution within a given design domain, typically by minimizing structural compliance subject to constraints such as material volume, stress, or stability [1].TO has been widely applied across engineering fields, including mechanical [2], aerospace [3], biomedical [4], and civil engineering [5,6].Recent developments have extended TO to multi-material and lattice-based designs, enabling optimized spatial allocation of materials for multifunctional performance such as stiffness, energy absorption, or negative Poisson's ratio [7,8].Lattice structures, in particular, offer high stiffness-to-weight ratios and are well-suited for additive manufacturing.Their integration into TO has been explored through homogenization-based modeling [9] and more recently, through data-driven and surrogate

  PublisherOA PDFDOI

• Bridging data gaps in fragility modeling for coastal infrastructure resilience

  2025-01-01

  articleOpen accessSenior author

  Coastal infrastructure is highly exposed to hurricanes and severe storms, underscoring the need for reliable fragility models that predict structural performance under extreme hazards.Over the past fifty years, methodologies for generating and validating building fragility curves have advanced considerably, but calibration and validation continue to lag due to scarce damage records and domain-specific assumptions.While numerical and simulation-based frameworks exist for singleand multi-hazard scenarios, they may lack transferability and robust empirical verification.Machine learning techniques present opportunities to overcome these limitations by enabling adaptive, continually evolving fragility functions.We propose a methodology integrating conventional transfer learning and hierarchical Bayesian transfer learning to enhance model flexibility and precision, especially in data-scarce coastal regions.This approach refines existing simulation-or data-rich region-derived models with limited labeled damage data from new target areas, improving predictive accuracy and closing the gap between theoretical simulations and observed outcomes.We demonstrate its feasibility through residential building case studies impacted by Hurricane Ian.By leveraging both physical simulations and sparse field observations, the framework supports ongoing model learning and adaptation.By delivering more accurate, region-specific fragility assessments, this framework advances vulnerability analysis and supports resilient coastal infrastructure planning.

  PublisherOA PDFDOI

• Regional Seismic Damage Assessment of the Bridge Network in Los Angeles

  Lecture notes in civil engineering · 2025-01-01

  book-chapterSenior author
  PublisherDOI

• The pursuit of multi-hazard life-cycle resilience in an era of smart and objective modeling

  2025-07-14

  book-chapterOpen access1st authorCorresponding

  Reliable, effective functioning of structures and infrastructure systems during and following hazard events, like earthquakes, hurricanes, and floods, is essential to public safety, economic vitality and quality of life. Risk-informed decisions that promote infrastructure resilience (or its ability to withstand, adapt and recover) require confident predictions of system performance when exposed to such stressors throughout its lifetime. However, this future brings uncertainties regarding dynamic, evolving conditions; challenges with respect to a legacy of disparate impacts of natural hazards and infrastructure (under)investment; and opportunities related to smart systems and emerging data and algorithms. This paper discusses a paradigm shift toward smart and objective life-cycle resilience modeling of infrastructure exposed to multiple hazards. We discuss the characteristics and dimensions of such a modeling framework intended to infuse intelligence and promote confident, unbiased predictions with respect to the algorithms used for infrastructure resilience pursuits. Case studies across hazards, systems and scales are leveraged to highlight recent advances in risk and resilience modeling from the structure to infrastructure to community scale.

  PublisherOA PDFDOI

• Hurricane Risk Assessment of Petroleum Infrastructure in a Changing Climate

  UNC Libraries · 2025-07-11

  articleOpen access1st authorCorresponding

  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.

  PublisherDOI

Recent grants

• CAREER: A Risk-Based Model to Achieve Sustainable Solutions for Bridge Infrastructure Subjected to Multiple Threats

  NSF · $450k · 2011–2018

• Collaborative Research: Novel Fractional Order Ground Motion Intensity Measures for High Confidence Risk Assessment of Distributed Infrastructures

  NSF · $222k · 2015–2019

• BRITE Fellow: A New Paradigm of Equitable and Smart Multi-Hazard Resilience Modeling (ENSURE)

  NSF · $1000k · 2023–2028

• IT-Enabled Continuous Risk Assessment of Bridge Networks for Customized and Actionable Multi-Hazard Interventions

  NSF · $381k · 2009–2012

• SCC-PG: Toward Smart Resilience: Smart Systems for Situational Awareness of Flood Impacts and Transportation Access (SSSAFT) in Communities

  NSF · $150k · 2020–2022

Frequent coauthors

• Reginald DesRoches

  Rice University

  85 shared

• Leonardo Dueñas‐Osorio

  30 shared

• Jayadipta Ghosh

  Indian Institute of Technology Bombay

  28 shared

• Sabarethinam Kameshwar

  Louisiana State University

  27 shared

• Bryant G. Nielson

  Clemson University

  27 shared

• Mark Yashinsky

  California Department of Transportation

  25 shared

• Ed Tavera

  25 shared

• Oh‐Sung Kwon

  University of Toronto

  20 shared

Labs

• Padgett Research GroupPI

Awards & honors

• 2017 ASCE Walter L. Huber Civil Engineering Research Prize
• 2017 (T+R) 2 Award at Rice University for excellence in rese…
• 2011 National Science Foundation Faculty Early Career Develo…
• 2016 IALCCE Junior Award for contributions to life-cycle ana…