Performance-based design of bridge structures under vehicle-induced fire accidents: Basic framework and a case study

Ma, Rujin; Cui, Chuanjie; Ma, Minglei; Chen, Airong

doi:10.1016/j.engstruct.2019.109390

Cited by 33 publications

(9 citation statements)

References 26 publications

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“…Unlike other case studies, Rujin et al . attempted to fill a rather large hole in the existing literature by considering elevated temperatures' influence on bridges’ structure (Ma et al ., 2019). While fire is not the most common method for causing failure in a bridge, vehicle-induced fire is a threat that continues to grow with the ever-increasing amount of transport in the world.…”

Section: Notable Success Stories and Case Studiesmentioning

confidence: 99%

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

Craig

Naser

2023

JSFE

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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need, the fire design of structures still relies on expensive fire tests, complex finite element simulations and outdated procedures with little room for innovation.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.

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Section: Notable Success Stories and Case Studiesmentioning

confidence: 99%

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

Craig

Naser

2023

JSFE

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“…Therefore, a new formula for the design of a cold-formed beam having sigma sections was proposed under non-uniform bending. Ma et al (2019) presented a performance-based design approach for bridges under vehicle induced fires. The authors illustrated the approach with a case study.…”

Section: Introductionmentioning

confidence: 99%

Simulation of bolted connections under fire: optimization and model validation

Saglik

Chen

2023

JSFE

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PurposeBeginners and even experienced ones have difficulties in completing the structural fire analysis due to numerical difficulties such as convergence errors and singularity and have to spend a lot of time making many repetitive changes on the model. The aim of this article is to highlight the advantages of explicit solver which can eliminate the mentioned difficulties in finite element analysis containing highly nonlinear contacts, clearance between modeled parts at the beginning and large deflections because of high temperature. This article provides important information, especially for researchers and engineers who are new to structural fire analysis.Design/methodology/approachThe finite element method is utilized to achieve mentioned purposes. First, a comparative study is conducted between implicit and explicit solvers by using Abaqus. Then, a validation process is carried out to illustrate the explicit process by using sequentially coupled heat transfer and structural analysis.FindingsExplicit analysis offers an easier solution than implicit analysis for modeling multi-bolted connections under high temperatures. An optimum mesh density for bolted connections is presented to reflect the realistic structural behavior. Presented explicit process with the offered mesh density is used in the validation of an experimental study on multi-bolted splice connection under ISO 834 standard fire curve. A good agreement is achieved.Originality/valueWhat makes the study valuable is that the points to be considered in the structural fire analysis are examined and it is a guide that future researchers can benefit from. This is especially true for modeling and analysis of multi-bolted connections in finite element software under high temperatures. The article can help to shorten and even eliminate the iterative debugging phases, which is a problematic and very time-consuming process for many researchers.

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“…+ 660,000 and + 878,000 operational bridges in the US and China, respectively) infers that identifying vulnerable bridges to fire can be challenging (Statista, 2020;LTBP, 2020). It is due to such challenges that little research has been directed towards identifying fire-vulnerable bridges (Giuliani et al, 2012;Quiel et al, 2015;Aziz & Kodur, 2013;Kodur et al, 2017;Kodur & Naser, 2019;Alos-Moya et al, 2017;Ma et al, 2019). Of the existing limited works, the majority applied similar methods to that adopted in identifying vulnerable bridges to wind and seismic hazard (i.e.…”

Section: Introductionmentioning

confidence: 99%

Classifying bridges for the risk of fire hazard via competitive machine learning

Kodur

Naser

2021

ABEN

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This study presents a machine learning (ML) approach to identify vulnerability of bridges to fire hazard. For developing this ML approach, data on a series of bridge fires was first collected and then analyzed through three algorithms; Random forest (RF), Support vector machine (SVM) and Generalize additive model (GAM), competing to yield the highest accuracy. As part of this analysis, 80 steel bridges and 38 concrete bridges were assessed. The outcome of this analysis shows that the ML based proposed approach can be effectively applied to arrive at the risk based classification of bridges from a fire hazard point of view. In addition, the developed ML algorithms are also capable of identifying the most critical features that govern bridges vulnerability to fire hazard. In parallel, this study showcases the potential of integrating ML into structural engineering applications as a supporting tool for analysis (i.e. in lieu of experimental tests, advanced simulations, and analytical approaches). This work emphasizes the need to compile data on bridge fires from around the world into a centralized and open source database to accelerate the integration of ML in to fire hazard evaluation.

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Performance-based design of bridge structures under vehicle-induced fire accidents: Basic framework and a case study

Cited by 33 publications

References 26 publications

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

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

Simulation of bolted connections under fire: optimization and model validation

Classifying bridges for the risk of fire hazard via competitive machine learning

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