Structural Fire Engineering Research Articles

Reliability-based safety format for structural fire engineering – Derivation based on the most likely failure point

Designing structures for burnout resistance ensures stability during evacuation and search and rescue operations, limits collateral damage, and enhances post-fire repairability. This represents a significant shift from traditional prescriptive designs that do not evaluate performance under realistic fire conditions. However, given the variability in fire exposure and structural response, it is unclear which input values should be used to ensure a high level of reliability for burnout calculations. This paper introduces a safety format for burnout resistance compatible with the Eurocode and its reliability principles. The format allows users to specify desired reliability levels and prescribes equations for determining design values for load effects and fire load density using predetermined sensitivity weights. A method for calculating default sensitivity weights is outlined, proposing tentative values: 0.65 for resistance effect, -0.40 for load effect, and -0.80 for fire load density, with a default coefficient of variation of 0.30 for resistance effect when case-specific information is lacking. The safety format's performance is verified through case studies of a concrete slab and a numerical evaluation of a steel column, showing satisfactory and conservatively assessed results. Inherent conservatism in the design format may, however, occasionally lead to the undue rejection of designs. Further investigations are necessary to confirm the safety format's conceptualization, default sensitivity weights, and the influence of the adopted compartment fire model.

The methodology for evaluating the fire resistance performance of concrete-filled steel tube columns by integrating conditional tabular generative adversarial networks and random oversampling

Artificial Intelligence (AI) technology has emerged as a powerful tool for addressing various complex issues within engineering structures. Accurately assessing the fire resistance performance of Concrete-Filled Steel Tube (CFST) columns poses a significant challenge for researchers. The scarcity of CFST column fire resistance test data presents a hurdle in the development of Machine Learning (ML) models. To tackle this issue, this study proposes a novel ML approach that integrates Conditional Tabular Generative Adversarial Networks (CTGAN) and Random Oversampling (ROS) for evaluating CFST column fire resistance performance. CTGAN technology is used to generate synthetic datasets to alleviate data scarcity issues, while ROS technique is used to balance the dataset. To validate the effectiveness of this method, detailed experimental tests using eight ML algorithms were conducted on both real and synthetic datasets, with results indicating superior performance on the synthetic dataset compared to the real dataset. The optimal model in the synthetic dataset achieves an Accuracy, Precision, Recall, and F1-Score of 0.972, 0.973, 0.972, and 0.972, respectively. Furthermore, extensive comparative analyses and practical applications of the proposed method were conducted, revealing its exceptional performance in evaluating the fire resistance of CFST columns. To facilitate ease of use and operation in practical engineering, a visual interface was developed. The introduction of this technique offers a novel solution for researchers and engineers in the field of structural fire engineering.

Probabilistic Performance-based Fire Design of Structures: A Hazard-Centred and Consequence-Oriented Perspective

Risk-based design and assessment methods are gaining popularity in performance-based structural fire engineering. These methods usually start by defining a set of hazard scenarios to use as analysis inputs. This approach, proven highly effective for other hazard types such as earthquakes, may not be optimal for fire safety design. Indeed, the strong coupling between the fire phenomenon and structural features enables an ad-hoc design variable selection (and/or optimisation) to reduce fire intensity, making fire scenarios additional design outputs. In addition, such a coupling effect implies that fire scenarios maximising consequences are structure specific. Building on these considerations, this paper discusses the limitations that arise at different analysis steps (i.e., fire-scenario and intensity treatment, identifying fire intensity measures, probabilistic fire hazard analysis, developing fire fragility models, and risk calculation) when using conventional risk-based approaches for design purposes. Furthermore, it compares such approaches with a fire safety design methodology (the Consequence-oriented Fire intensity Optimisation, CFO, approach) that addresses the identified limitations. The potential benefits of integrating the two approaches are also discussed. Finally, the fire design of a simplified steel-girder bridge is introduced as an illustrative example, comparing the consequence metrics and design updating strategies resulting from the two approaches.

Impact of the variability of material constitutive models on the thermal response of reinforced concrete walls

PurposeThis study examines the effect of temperature-dependent material models for normal-strength (NSC) and high-strength concrete (HSC) on the thermal analysis of reinforced concrete (RC) walls.Design/methodology/approachThe study performs an one-at-a-time (OAT) sensitivity analysis to assess the impact of variables defining the constitutive and parametric fire models on the wall's thermal response. Moreover, it extends the sensitivity analysis to a variance-based analysis to assess the effect of constitutive model type, fire model type and constitutive model uncertainty on the RC wall's thermal response variance. The study determines the wall’s thermal behaviour reliability considering the different constitutive models and their uncertainty.FindingsIt is found that the impact of the variability in concrete’s conductivity is determined by its temperature-dependent model, which differs for NSC and HSC. Therefore, more testing and improving material modelling are needed. Furthermore, the heating rate of the fire scenario is the dominant factor in deciding fire-resistance performance because it is a causal factor for spalling in HSC walls. And finally the reliability of wall's performance decreased sharply for HSC walls due to the expected spalling of the concrete and loss of cross-section integrity.Originality/valueLimited studies in the current open literature quantified the impact of constitutive models on the behaviour of RC walls. No studies have examined the effect of material models' uncertainty on wall’s response reliability under fire. Furthermore, the study's results contribute to the ongoing attempts to shape performance-based structural fire engineering.

The effects of pre-loading on structural behavior of reinforced concrete beams under fire condition: a review

PurposeThis review paper seeks to enhance knowledge of how pre-loading affects reinforced concrete (RC) beams under fire. It investigates key factors like deflection and load capacity to understand pre-loading's role in replicating RC beams' actual responses to fire, aiming to improve fire testing protocols and structural fire engineering design.Design/methodology/approachThis review systematically aggregates data from existing literature on the fire response of RC beams, comparing scenarios with (WP) and without pre-loading (WOP). Through statistical tools like the two-tailed t-test and Mann–Whitney U-test, it assesses deflection extremes. The study further examines structural responses, including flexural and shear behavior, ultimate load capacity, post-yield behavior, stiffness degradation and failure modes. The approach concludes with a statistical forecast of ideal pre-load levels to elevate experimental precision and enhance fire safety standards.FindingsThe review concludes that pre-loading profoundly affects the fire response of RC beams, suggesting a 35%–65% structural capacity range for realistic simulations. The review also recommended the initial crack load as an alternative metric for determining the pre-loading impact. Crucially, it highlights that pre-loading not only influences the fire response but also significantly alters the overall structural behavior of the RC beams.Originality/valueThe review advances structural fire engineering with an in-depth analysis of pre-loading's impact on RC beams during fire exposure, establishing a validated pre-load range through thorough statistical analysis and examination of previous research. It refines experimental methodologies and structural design accuracy, ultimately bolstering fire safety protocols.

The Impact of Various Heating Rates on Real-Time Degree of Hybrid Fire Simulation

Global fire performance of structures in fire is proven to be more advantageous in many cases of engineering practice than the prescriptive fire resistance based on isolated structural member testing. Hybrid fire simulation (HFS) is a novel well-suited method trending in recent years for analysis of global performance of structures in fire. In the principles of this method, the part of a structure which has unknown behavior or is uncertain to be numerically modeled (subjected to fire) would be physically tested, while the rest of the structure is numerically simulated. HFS method enables capturing the beneficial interaction mechanisms evolving between fire-exposed structural members and the adjacent cooler substructure. Due to the continuous temperature increase in a fire test and the existing thermal inertia as well as the rate- and temperature-dependent material behavior of structures exposed to fire, a real-time performance in hybrid fire simulation counts as a necessity. This challenge is more critical for hybrid fire simulations with higher applied heating rates relevant to structural fire engineering. Within scope of this paper, (a) a robust and rigorous approach for real-time HFS is presented; (b) a series of proof-of-concept studies of different hybrid fire simulations with various applied heating rates are carried out for a thermomechanical benchmark problem; (c) the important results of four representative hybrid fire simulations with relevant heating rates to structural fire engineering are discussed; (d) the importance of an appropriate calculation method for stiffness update of the fire-exposed structural member over HFS procedure is highlighted, and e) the precision and accuracy of the applied HFS approach with respect to interface error and real-time degree are evidenced.

Validation of a New Analytical Formula to Predict the Steel Temperature of Heavily Insulated Cross-Sections

Fire protection is a popular solution to slow down the temperature increase in steel elements subjected to fire, and simple equations, such as the mass lumped formula proposed in EN1993-1-2, may be employed to estimate the steel temperature in the cross-section. The EN1993-1-2 formula assumes that the temperature of the exposed insulation surface and the surrounding gas are equal. This simplification may provide inaccurate results for heavily insulated steel sections. Therefore, a new mass lumped formula was derived, accounting for more accurate boundary conditions considering the heat flux impinging the insulation. On these premises, this work evaluates how the new simple formula fares with respect to the EN1993-1-2 formula. In this respect, a comprehensive comparison with the results of 1-D and 2-D analyses considering several insulation materials and thicknesses of insulation and steel is thoroughly presented. The proposal results in being always safe and better estimates steel temperatures relevant in the structural fire engineering context. Its use is particularly recommended for heavily insulated sections, where the ratio between the insulation and the steel heat capacities is higher than 14, and the EN1993-1-2 formula gives unsafe predictions.

Framework to Incorporate Sprinkler System in Structural Fire Engineering

Framework to Incorporate Sprinkler System in Structural Fire Engineering

A primer and success stories on performance-based fire design of structures

PurposeThe extreme nature of fire makes structural fire engineering unique in that the load actions dictating design are intense and neither geographically nor seasonally bound. Simply, fire can break out anywhere, at any time and for any number of reasons. Despite the apparent need, the fire design of structures still relies on expensive fire tests, complex finite element simulations and outdated procedures with little room for innovation. This paper aims to discuss the aforementioned issues.Design/methodology/approachThis primer highlights the latest state of the art in this area with regard to performance-based design in fire structural engineering. In addition, this short review also presents a series of examples of successful implementation of performance-based fire design of structures from around the world.FindingsA comparison between global efforts clearly shows the advances put forth by European and Oceanian efforts as opposed to the rest of the world. In addition, it can be clearly seen that most performance-based fire designs are related to steel and composite structures.Originality/valueIn one study, this paper presents a concise and global view to performance-based fire design of structures from success stories from around the world.

Double-machine-learning-based data-driven stochastic flow stress model for aluminium alloys at elevated temperatures

As a widely used metal, aluminium alloys take an important role in many engineering fields. Quantifying the stochastic flow behaviour of aluminium alloys at high temperatures is inevitable for performing the reliability design in structural fire engineering. This work proposes a double-machine-learning (DML) framework for modelling the stochastic flow stress of aluminium alloys at elevated temperatures directly from the dataset. The proposed framework extracts the mean and standard deviation of the flow stress from the dataset and describes them separately by two machine learning (ML) models. The experimental data of the 6061-T651 aluminium alloys is used to validate the DML-based stochastic flow stress model. By comparison between four popular ML models, the artificial neural network (ANN) model and the Gaussian process regression (GPR) model are the most appropriate choices for predicting the mean and the standard deviation, respectively, in the DML framework. The stochastic distribution of the flow stress estimated by the DML framework, which consists of the ANN model and GPR model, is in high agreement with the experimental results. The good agreement between predictions and experimental results demonstrates that the DML framework can accurately describe the stochastic flow stress of aluminium alloys at elevated temperatures and can be used in the reliability design processes of aluminium structures. • Double-machine-learning (DML) framework is proposed for stochastic flow stress at elevated temperatures • Established stochastic flow stress model is validated by experimental data of aluminium alloys • DML framework with ANN and GPR model is the most suitable choice for aluminium alloys • Probability distribution of aluminium alloys’ flow stress is accurately reproduced by DML framework

An approach for developing probabilistic models for temperature‐dependent properties of construction materials from fire tests and small data

AbstractProbabilistic approaches provide a more realistic look into assessing structures under fire conditions and overcome some limitations observed in the more traditional (deterministic) approaches. These approaches have also been introduced to the fire engineering domain, for example, fire probabilistic risk analysis and probabilistic structural fire engineering. In order to perform probabilistic‐based analysis, temperature‐dependent probabilistic models for material properties are needed. This paper presents a methodology to develop temperature‐dependent probabilistic models for the thermal and mechanical properties for commonly used construction materials, including normal‐strength, high‐strength, and high‐performance concrete and mild, high‐strength, and cold‐formed steels. The presented approach analyzes a comprehensive list of surveyed experimental data at different temperature groups, tests the goodness of fit for a number of distributions, and derives a continuous function to quantify temperature‐dependent parameters of the distribution. In addition, the newly derived models are also compared against those adopted by fire codes, and standards and others derived using machine learning. The newly developed models will complement existing efforts to facilitate probabilistic performance‐based structural fire engineering.

The finite element method for evaluating the fire behavior of steel structures

PurposeThe purpose of this study is to contribute toward providing the main aspects of numerical modeling the fire behavior of steel structures with finite elements (FEs). The application of the method is presented for a characteristic case study comprising the series of large-scale fire door tests performed at the Danish Institute of Fire and Security Technology.Design/methodology/approachFollowing a general overview of current practices in structural fire engineering, the FE method is used to simulate the large-scale furnace tests on steel doors with thermal insulation exposed to standard fire.FindingsThe FE model is compared with the fire test results, achieving good agreement in terms of developed temperatures and deformations.Originality/valueThe numerical methodology and recommended practices for modeling the fire behavior of steel structures are presented, which can be used in support of performance-based fire design standards.

A numerical investigation of 3D structural behaviour for steel-composite structures under various travelling fire scenarios

In performance-based structural fire engineering, “travelling fires” is being gradually accepted as an important fire boundary condition. However, its application is still limited by uncertainties in the selection of different design travelling fire parameters, resulting from the lack of relevant experimental data and corresponding validated structural finite element models which can be used with advanced travelling fire methodologies, e.g. the Extended Travelling Fire Methodology (ETFM) framework. This paper aims to fill this gap through modelling a prototype steel-composite floor structure (representing a “slice” of a large open-plan office), to investigate its true structural response under a wide range of travelling fire scenarios, with an emphasis on considering the effect of concrete slab in a 3D finite element model, using LS-DYNA. To ensure the credibility of this numerical study, the model was first validated against the experimental data of the structural response from the Veselí Travelling Fire Test. In the parametric studies, 32 cases were examined to investigate the thermal and structural response, related to the selection of key design parameters for travelling fires (i.e. fire spread rates, fuel load densities and inverse opening factors (IOF)); fire protection (i.e. different fire protection schemes and required fire resistance rating (FRR)), and the effect of slab specification (i.e. thicknesses and steel reinforcements).It was found that solely satisfying the critical temperature and deflection criteria for the structural members might not guarantee a sufficient structural design for travelling fire scenarios, and it is suggested that the steel stress utilisation should also be examined. Compared with the IOF, it appears that the selection of fire spread rates and fuel load densities are likely to be more critical in identifying the worst travelling fire scenario for the structural response with fire protection. Moreover, the global structural response under travelling fire is also affected by the combination of fire protection (i.e. equivalent FRR in this paper) and fire spread rate. Under a “slow” travelling fire (e.g. 0.5 mm/s) with increasing FRR, the failure of structural elements during the cooling phase was prevented effectively; however, under a relatively “fast” travelling fire (e.g. 2.5 mm/s, 12.5 mm/s), increasing FRR may not always improve the fire performance of the structure. This work also indicates that steel reinforcement ratio has a greater influence on structural response than slab thickness under travelling fires. Furthermore, the 3D finite element model is very important for structural fire analysis, not only due to the more conservative internal force captured by the 3D model (i.e. reduced by over 80 % on the 2D model in our case) thereby reproducing the collapse triggered by the failure of the connection under fire in general, but also the 3D model was able to better represent the deflection and the “internal force reversal” caused by travelling fires.

Performance-Based Structural Fire Engineering of Steel Building Structures: Traveling Fires

Many studies and observations of large building fires show that in large compartments, fires do not burn uniformly. Rather, fires will burn locally and move across a floor plate as fuel is consumed. Currently fire engineering practice assumes uniformly burning fires within a compartment and the fire design methodologies are based upon small compartment fire scenarios. Traveling fire modeling that accounts for fire dynamics is essential for performance-based fire engineering. This paper reviews two traveling fire models presented in literature and implements both models on a steel-frame building. An advanced analysis of the building is performed and the fire performance of the building in these travel fire scenarios are compared to the fire performance of the building when subjected to conventional fire exposure scenarios (full story fires or compartment fires). The authors then illustrate the use of structural fire engineering methodologies to improve the fire resistance of the building.

Hiding in plain sight: What can interpretable unsupervised machine learning and clustering analysis tell us about the fire behavior of reinforced concrete columns?

The role of machine learning (ML) continues to rise in the structural fire engineering area. Noting the widespread of supervised ML approaches, such methods are being heavily utilized nowadays. On the other hand, little interest has been dedicated to unsupervised ML. Unlike supervised learning, unsupervised learning algorithms are trained using data that is neither classified nor labeled, thus, allowing the algorithm, and without guidance, to identify unseen patterns and decode hidden structures of complex phenomena residing in data. In this pursuit, this study presents findings obtained from one of the earliest investigations aimed to explore the potential of unsupervised and interpretable machine learning (ML) clustering analysis to investigate the response of reinforced concrete (RC) columns under fire conditions. We used four algorithms (namely, K-Means, Hierarchical, Fuzzy C-Means, and DBSCAN) to cluster over 140 fire-exposed RC columns. Results from our clustering analysis show that the performance of such columns can be distinctly grouped into unique clusters. Our findings allow structural fire engineers to 1) identify and avoid RC columns with poor fire performance and, when possible, 2) upgrade the features/characteristics of columns of poor fire response to achieve an improved behavior. Overall, our analysis indicates that fire-exposed RC columns naturally fit into four clusters – each with unique properties and response – that are governed by the material, geometric and loading features.

Structural fire engineering considerations for cross-laminated timber walls

The current understanding of the thermo-mechanical response of cross-laminated timber (CLT) walls to fire is insufficiently developed. This paper presents results obtained using a novel experimental methodology on fire-exposed CLT walls under sustained loads. The findings demonstrate that global instability is likely to be the dominant failure mode for CLT walls in fire. Use of a polyurethane adhesive resulted in earlier structural failure than use of a melamine urea formaldehyde adhesive. Three-ply walls failed significantly earlier than those with five plies. The combination of these two factors caused a halving of failure time between different walls under identical heating conditions. In addition, CLT walls were found to collapse during artificially induced cooling phases. It is concluded that these findings are centrally relevant considerations for fire design of CLT.

Limit Carrying Capacity Calculation of Two-Way Slabs with Three Simply Supported Edges and One Clamped Edge under Fire

Fire-resistance experiments were conducted on two full-scale two-way reinforced concrete slabs with three simply supported edges and one clamped edge. This paper presents the design of the furnace, specimens and clamped-end device, the test plan, and the measuring contents and methods. The cracking and failure characteristics of the tested two-way reinforced concrete slabs were introduced. The temperature field distribution of the concrete and steel in the direction of the section thickness, out-of-plane deflections, in-plane deflections, and angle of the slab edges of the two-way concrete slab under fire conditions were analyzed. Cracks form on the slab surface, which is shaped similar to the bottom of a shallow bowl, with two transverse cracks in the mid-area of the medially clamped side and intensively annular diagonal cracks on the corner side. There were several main transverse cracks in the central area of the slab surface. The results indicate that there was a significant decrease in frequency under fire conditions. Relations between the frequency and the central vertical deflection of the two slabs were analyzed by the regression method. This research generates valuable test data that can be used to validate the numerical models developed by fellow researchers in the field of structural fire engineering. Based on the plate balance method and energy method, calculation formula of the limit carrying capacity calculation formula of reinforced concrete two-way slabs with four different boundaries under fire are given, which are simply supported on three sides and clamped on one side. The influence of membrane effect under large deflection is considered in the formula, and the calculated results are in good agreement with the experimental results.

Steel Structures Research Update: Structural Fire Engineering

Across the United States, researchers are making exciting discoveries and advances in structural fire engineering. Eleven of the leading scholars in the field are featured. Brief research highlights are organized by the topic areas of behavior and design of steel and composite structures for fire, fire following earthquakes, and performance-based fire engineering. For each individual, related steel and fire research is also noted. Meanwhile, due to timing and circumstances, there are structural fire engineering researchers who do not appear in this article. You may find some of their structural steel research in past articles or perhaps a future research update.

Ductile connection to improve the fire performance of bare-steel and composite frames

PurposeIn order to improve the robustness of bare-steel and composite structures in fire, a novel axially and rotationally ductile connection has been proposed in this paper.Design/methodology/approachThe component-based models of the bare-steel ductile connection and composite ductile connection have been proposed and incorporated into the software Vulcan to facilitate global frame analysis for performance-based structural fire engineering design. These component-based models are validated against detailed Abaqus FE models and experiments. A series of 2-D bare-steel frame models and 3-D composite frame models with ductile connections, idealised rigid and pinned connections, have been created using Vulcan to compare the fire performance of ductile connection with other connection types in bare-steel and composite structures.FindingsThe comparison results show that the proposed ductile connection can provide excellent ductility to accommodate the axial deformation of connected beam under fire conditions, thus reducing the axial forces generated in the connection and potentially preventing the premature brittle failure of the connection.Originality/valueCompared with conventional connection types, the proposed ductile connection exhibits considerable deformability, and can potentially enhance the robustness of structures in fire.

Fire Experiment Inside a Very Large and Open-Plan Compartment: x-ONE

The traditional design fires commonly considered in structural fire engineering, like the standard fire and Eurocode parametric fires, were developed several decades ago based on experimental compartments smaller than 100 m2 in floor area. These experiments led to the inherent assumption of flashover in design fires and that the temperatures and burning conditions are uniform in the whole of the compartment, regardless of its size. However, modern office buildings often have much larger open-plan floor areas (e.g. the Shard in London has a floor area of 1600 m2) where non-uniform fire conditions are likely to occur. This paper presents observations from a large-scale fire experiment x-ONE conducted inside a concrete farm building in Poland. The objective of x-ONE was to capture experimentally a natural fire inside a large and open plan compartment. With an open-plan floor area of 380 m2, x-ONE is the largest compartment fire experiment carried out to date. The fire was ignited at one end of the compartment and allowed to spread across a continuous wood crib (fuel load ~ 370 MJ/m2). A travelling fire with clear leading and trailing edges was observed spreading along 29 m of the compartment length. The flame spread rate was not constant but accelerated with time from 3 mm/s to 167 mm/s resulting in a gradually changing fire size. The fire travelled across the compartment and burned out at the far end 25 min after ignition. Flashover was not observed. The thermocouples and cameras installed along the fire path show clear near-field and far-field regions, indicating highly non-uniform spatial temperatures and burning within the compartment. The fire dynamics observed during this experiment are completely different to the fire dynamics reported in small scale compartments in previous literature and to the assumptions made in traditional design fires for structural design. This highlights the need for further research and experiments in large compartments to understand the fire dynamics and continue improving the safe design of modern buildings.