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Fire events due to tanker truck accidents have caused significant damage to numerous steel girder overpass bridges over the last several decades. Due to the limited availability of funding for public infrastructure, engineers must tailor hazard mitigation according to vulnerability and consequence, thereby maximising the cost/benefit of these investments. The authors have developed a fast-running analytical framework to evaluate steel-supported bridges for fire exposure. In this article, that computationally efficient tool is leveraged to conduct a large set of analyses over a complex highway interchange for a realistic and randomised series of fire scenarios. Fire hazard input parameters (e.g. combustion properties, spill geometry, etc) as well as the fire location within the interchange are randomly selected based on user-defined probability functions. Traffic data are used to determine which bridge spans may be most susceptible to fire and which pose the greatest consequences if they fail or lose functionality. The results of these analyses are used to develop a performance envelope that indicates the risk of fire-induced damage, as well as the effectiveness of potential fire protection strategies.
Zhu et al. (Mon,) studied this question.