About

Thomas Gernay is an associate professor in the Department of Civil and Systems Engineering at Johns Hopkins University. His research focuses on developing innovative methods to enhance the resilience of the built environment against fire and other hazards. He is known for pioneering computational modeling techniques and risk-based methodologies that are used worldwide to help structural engineers, architects, and decision-makers design buildings capable of withstanding fire and natural threats. Gernay founded and leads the Johns Hopkins Multi-Hazard Resilient Structures research group, which combines computational mechanics, experimental testing, performance-based design, and the development of numerical analysis software to model the performance of entire buildings under extreme hazards. His expertise includes studying the effects of fire on materials, structures, and systems, and he is the co-author of SAFIR®, a software used globally to model and predict structural responses to fire. Gernay's lab also investigates the impact of critical infrastructure damage on community resilience and develops methods to minimize damage and recovery time. He has contributed to advancing building codes and safety standards, including authoring a fire appendix for the American Iron and Steel Institute’s S100 standard and convening the Performance-based Fire Design working group for the International Federation for Structural Concrete. His work has been funded by prominent agencies such as the NSF, NIST, and NFPA. Recognized for his contributions, Gernay received multiple awards including the NSF Early CAREER Award, the Magnusson Award, and the American Institute of Steel Construction’s Terry Peshia Early Career Faculty Award. He has authored over 150 peer-reviewed articles, served as an associate editor for Fire Technology, and is actively involved in professional societies, contributing to standards and conferences related to fire safety and structural resilience.

Research topics

• Materials science
• Machine Learning
• Composite material
• Metallurgy
• Engineering
• Computer Science
• Structural engineering
• Mathematics
• Statistics
• Civil engineering
• Reliability engineering

Selected publications

• Physical Damage-Measured Response Correlation for Nonstructural Components Tested under Multi-Hazard (Earthquake and Fire) Demands in the CFS10 Building

  2026-04-27

  article

  This paper is the second of a set presented within the session: Findings from the CFS10 Multi-Hazard Test Program. The emphasis within this article is to highlight correlations between physical damage to nonstructural components and systems with measured response during a suite of 18 earthquake tests and 2 subsequent fire tests. This damage was documented within a 10-story cold-formed steel-framed building test specimen outfitted with various nonstructural components, including suspended ceilings, architectural finishes, a resilient stair system, windows and doors, pressurized fire sprinkler and gas piping systems, and roof-mounted mechanical equipment. The test building and multi-hazard protocol are described in the session companion paper. This paper provides test observations correlated with measured engineering demand parameters such as floor acceleration, building inter-story drift, or peak local temperature responses and offers related literature emerging from the project. Damage data and functionality checks will support development of fragility functions for these nonstructural systems for use in recovery-based frameworks.

  PublisherDOI

• Overview of the CFS10 Program: Full-Scale Shake Table Earthquake and Fire Testing of a 10-Story Cold-Formed Steel Building

  2026-04-27

  article

  This paper is the first in a pair presented within the session: Findings from the CFS10 Multi-Hazard Test Program. The emphasis within this article is to, in brevity, describe the scope of a landmark full-scale 10-story cold-formed steel (CFS) framed building tested under multi-hazard (earthquake and fire) scenarios at the UC San Diego 6-DOF Large High-Performance Outdoor Shake Table (LHPOST6). Coined CFS10, this unique building specimen is designed beyond current code height limits, adopting advances in cold-formed steel shear wall detailing, varied construction modalities, and enriched with nonstructural components and systems. The landmark CFS10 building specimen was subjected to extreme multi-hazard (earthquake and fire) loading conditions. This paper sets the framework for presentations to be shared at the Congress, while also aiding in ongoing documentation of findings from the program.

  PublisherDOI

• A review of experiments on cold-formed steel members at elevated temperatures

  Journal of Constructional Steel Research · 2025-03-11 · 7 citations

  reviewSenior authorCorresponding
  PublisherDOI

• Structural performance of modular buildings subjected to fire

  Engineering Structures · 2025-09-23 · 3 citations

  articleOpen access

  Modular construction is increasingly adopted for mid to high-rise buildings due to its cost, speed and sustainability benefits, making fire safety a critical concern. However, research on composite modular structures under fire remains limited. This study investigates the fire-induced structural performance of a composite modular building with concrete-filled steel tubular (CFST) columns. Validation was conducted at both the component and system levels. A full building model was developed and analysed using SAFIR, a finite element-based software for thermal-structural analysis. A parametric analysis was then performed to explore the effects of fire curves, fire locations, multi-compartment fire spread and vertical spread to the upper module’s floor beams. Results show that the modular building exhibited good overall fire resistance, primarily due to the presence of CFST columns and system redundancy. Corner fires trigger earlier failure due to reduced restraint, while lower-floor fire causes earlier failure due to higher loads. Multi-compartment and vertical fire spread increase vulnerability by raising force demands on fire-exposed and adjacent members. The findings underscore the need for system-level modelling, as isolated analyses miss complex redistribution and failure mechanisms. • Fire behaviour of a composite modular building is numerically investigated. • Validation is performed at both component and system levels. • Good overall fire resistance is achieved due to CFST columns and redundancy. • Corner and lower-floor fires cause earlier failure due to restraint and loads. • Multi-compartment fire spread significantly increases structural vulnerability.

  PublisherDOI

• Economic impact of performance-based fire design of composite steel frame structures

  Engineering Structures · 2025-05-20 · 3 citations

  articleSenior authorCorresponding
  PublisherDOI

• Thermo-mechanical analysis and validation of SAFIR for historic forms of construction

  Journal of Structural Fire Engineering · 2025-11-07

  articleSenior author

  Purpose The advanced finite element software SAFIR has been extensively validated and benchmarked against the cases set out in the German National Annex for EN 1991-1-2 (2010) (DIN EN, 1991-1-2/NA, 2010; Wald et al., 2012; Ferreira et al., 2018a; Zaharia and Gernay et al.; Ferreira et al., 2018b; Pintea and Franssen, 1997). The existing validation studies focus on recent forms of construction and are not necessarily relevant to historic structures. This paper explores the use of advanced numerical techniques to estimate the anticipated fire behaviour of existing floor systems used in historic buildings in the UK. The main objective is to benchmark the numerical model against standard fire resistance test data for historic floor constructions. Design/methodology/approach The methodology involves using SAFIR software to validate the thermo-mechanical behaviour of historic types of floors using standard fire resistance test evidence. The numerical analysis is based on two steps. In the first step, a heat transfer analysis is performed to determine the heat distribution through the floor section. In the second step, the mechanical analysis is performed based on the temperature output from the thermal analysis, which considers a reduction of the material properties at elevated temperatures. Findings This paper provides a thorough examination of standard fire resistance test data for filler joists and hollow pot floors, which were conducted as part of the Investigation of Building Fires study in the UK during the 1950s. The overarching objective of the paper is to validate the applicability of modern numerical tools such as SAFIR software in evaluating the thermo-mechanical performance of historic forms of construction. Through the thermo-mechanical analysis and validation exercises, this paper demonstrates the accuracy of advanced numerical techniques in evaluating the fire behaviour of historic forms of construction. Specifically, it aims to validate numerical methods by comparing them against fire resistance test data, focusing on two significant historical floor types: filler joists and hollow pot floors. The analysis presented in the paper shows that the formulation for the material properties (steel and concrete) from the Eurocodes can accurately capture the behaviour in fire of the two floor systems. Research limitations/implications One limitation is that these numerical methods cannot predict local integrity failure (e.g. cracking or spalling) or the effects of undetected defects. Originality/value The study presented in the paper highlights the potential of applying advanced numerical techniques to enhance the understanding and evaluation of the fire performance of historical elements of structure. This not only facilitates more accurate assessments of fire resistance but also offers invaluable insights for architects, engineers and preservationists involved in the conservation and refurbishment of historic buildings.

  PublisherDOI

• From Occurrence to Consequence: A Comprehensive Data-driven Analysis of Building Fire Risk

  ArXiv.org · 2025-03-11 · 1 citations

  preprintOpen accessSenior author

  Building fires pose a persistent threat to life, property, and infrastructure, emphasizing the need for advanced risk mitigation strategies. This study presents a data-driven framework analyzing U.S. fire risks by integrating over one million fire incident reports with diverse fire-relevant datasets, including social determinants, building inventories, weather conditions, and incident-specific factors. By adapting machine learning models, we identify key risk factors influencing fire occurrence and consequences. Our findings show that vulnerable communities, characterized by socioeconomic disparities or the prevalence of outdated or vacant buildings, face higher fire risks. Incident-specific factors, such as fire origins and safety features, strongly influence fire consequences. Buildings equipped with fire detectors and automatic extinguishing systems experience significantly lower fire spread and injury risks. By pinpointing high-risk areas and populations, this research supports targeted interventions, including mandating fire safety systems and providing subsidies for disadvantaged communities. These measures can enhance fire prevention, protect vulnerable groups, and promote safer, more equitable communities.

  PublisherOA PDFDOI

• Fragility curves for structural fire performance of various composite floor designs under natural fire

  Reliability Engineering & System Safety · 2025-01-11 · 4 citations

  articleSenior authorCorresponding
  PublisherDOI

• Structural behavior of timber columns in wood crib compartment fire tests

  Fire Safety Journal · 2025-05-13 · 4 citations

  articleSenior authorCorresponding
  PublisherDOI

• Strength of thin-walled steel beams under non-uniform temperature: Analytical and machine learning models

  Fire Safety Journal · 2025-02-19 · 2 citations

  articleOpen accessSenior author

  The resistance of thin-walled steel beams in fire is governed by a complex interaction between the buckling of the plates and the lateral-torsional buckling (LTB) of the member, combined with the temperature-induced reduction of steel properties. Besides, in many applications, steel beams are subjected to non-uniform thermal exposure which creates temperature gradients in the section. There is a lack of analytical design methods to capture the effects of temperature gradients on the structural response, which leads to overly conservative assumptions thwarting optimization efforts. This paper describes a study on the strength of thin-walled steel beams subjected to constant bending moment in the major-axis and thermal gradients through analytical and Machine Learning (ML) methods. A parametric heat transfer analysis is conducted to characterize the thermal gradients that develop under three-sided fire exposure. Nonlinear finite element simulations with shells are then used to generate the resistance dataset. Results show that the use of the Eurocode model with a uniform temperature taken as the hot flange temperature severely underestimate the moment strength with an R 2 of 0.61. The ML models, trained using physically defined features, are far superior to the Eurocode methods in predictive capacity. The ML-based models can be used to improve existing design methods for non-uniform temperature distributions. • Parametric thermal analyses reveal significant temperature gradients in the cross-sections. • Thermal gradients influence buckling behavior of thin-walled steel beams. • Machine Learning models predict steel beam strength under thermal gradients. • Analytical methods fail to capture beam response accurately under thermal gradients. • ANN models outperform others with R 2 of 0.998 on the testing dataset.

  PublisherDOI

Frequent coauthors

• Jean‐Marc Franssen

  University of Liège

  76 shared

• Negar Elhami Khorasani

  University at Buffalo, State University of New York

  46 shared

• Ruben Van Coile

  Ghent University Hospital

  34 shared

• Shuna Ni

  15 shared

• Danny Hopkin

  15 shared

• Yan Xia

  Shandong Academy of Sciences

  14 shared

• Maria Garlock

  Princeton University

  11 shared

• Fabienne Robert

  Centre d'Études et de Recherches de l'Industrie du Béton

  9 shared

Education

• PhD, Structural Engineering

  Université de Liège

  2012

• Master en Ingénieur Civil des Constructions, Sciences Appliquées

  Université de Liège

  2009

Awards & honors

• 2025 Outstanding Educator of the Year from the Maryland Sect…
• NSF Early CAREER Award
• International Association of Fire Safety Science’s Magnusson…
• American Institute of Steel Construction’s Terry Peshia Earl…
• HEMI Seed Grant from the Hopkins Extreme Materials Institute…