Brandschaden und Instandsetzung der Wiehltalbrücke im Zuge der A4, Köln – Olpe

Eisel, Hans; Palm, Norbert; Prehn, Wolfgang; Sedlacek, Gerhard

doi:10.1002/stab.200710011

Cited by 17 publications

(5 citation statements)

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“…Following the fire accident, the bridge had to be closed for weeks for full inspection and repair. This fire incident costed $9.8 (€7.2) million, the most expensive traffic accident in German history [26].…”

Section: Validation Of the Proposed Approachmentioning

confidence: 99%

A probabilistic assessment for classification of bridges against fire hazard

Naser

Kodur

2015

Fire Safety Journal

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a b s t r a c tThis paper presents the development of a simplified approach for classification of bridges based on fire hazard. Statistical data from recent fires in bridges is utilized to quantify the probable risk of fire in bridges and also probability of fire-induced collapse of structural members in bridges. An importance factor is derived for identifying the vulnerability of bridges to fire hazard. The proposed importance factor, developed using weighted factor approach, takes into account the degree of vulnerability of different bridge components, critical nature of a bridge from traffic functionality point and fire mitigation strategies present in a specific bridge. The proposed importance factor for fire design, which is similar to the one currently used for evaluating wind, and snow loading in buildings, is validated against previous bridge fire incidents. It is shown through this validation that the proposed method for importance factor can be used as a practical tool for identifying critical bridges from the point of fire hazard.

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Section: Validation Of the Proposed Approachmentioning

confidence: 99%

A probabilistic assessment for classification of bridges against fire hazard

Naser

Kodur

2015

Fire Safety Journal

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“…Thus, a comprehensive literature review was first carried out to document notable bridge fire incidents. This review documented key and common factors that governing the response of bridges to fire as documented by the departments of transportation (DoTs) reports and from consultation with practicing engineers (Eisel et al, 2007;Quiel et al, 2015;NYDOT, 2008;Bocchini et al, 2014;Qiang et al, 2009;Davis & Tremel, 2008;Guthrie et al, 2009;Culliton, 2018). These documented factors include; bridges (structural) features, traffic flow patterns, and fire characteristics.…”

Section: Development Of Bridge Fire Databasementioning

confidence: 99%

“…Given the above, and noting that the bridges are often away from nearest fire department locations (to fight the fires), continuous exposure to the surrounding environment and their extended service life, implies that bridges become vulnerable to extreme events, especially fire hazard. Recent incidents have shown that fires on bridges can lead to the development of significant thermally-induced forces on connections, and result in collapse (NTSB, 2017;Eisel et al, 2007). Fortunately, bridge fires often extinguish quickly due to burning out of limited fuel present or firefighting activities.…”

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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“…The bridge that was damaged in the fire is very similar to the Wiehl valley viaduct near Cologne in Germany that was also damaged in a similar fire in August 2004. 3 However, the damages to the Mathild e Bridge were more extensive.…”

Section: The Mathilde Bridge Was Built Between 1977 and 1979 And Was mentioning

confidence: 99%