RAI: Rapid, Autonomous and Intelligent machine learning approach to identify fire-vulnerable bridges

Recent surveys have noted that the majority of bridges continue to serve for a prolonged period of time (+40 years) that far exceeds its intended operational lifespan. Given our limited resources to maintain and upkeep bridges, these structures become notoriously vulnerable to extreme events. Building upon the fact that bridges continue not to be specifically designed to withstand the adverse effects of fire, this study presents the development of a rapid, automated, and intelligent (RAI) approach that leverages machine learning to identify vulnerable bridges to fire hazard. This work also presents details on a comprehensive database comprising actual observations collected from 135 notable and international bridge fire incidents. This database was developed to train two machine learning techniques, deep learning and genetic algorithms, to quantify hidden patterns that govern the propensity of existing/new/historical bridges to undergo fire damage and/or fire-induced collapse via a multi-classification analysis. The proposed RAI approach also has the capability to pinpoint fire-vulnerable bridge components and to display its level of confidence in its predictions. As such, our approach can be of aid to bridge engineers and government officials (who may not be well-versed in fire design) with accuracy reaching 89.6%. This approach is implemented into a software (App) with optimized architecture and reduced computational complexity and hence is easily scalable and integratable into a user-friendly framework and handheld devices. The outcome of this study shows that the RAI approach can be deployed to arrive at an instantaneous assessment of fire vulnerable bridges. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

The study introduces a rapid, automated, and intelligent approach that leverages machine learning to identify vulnerable bridges to fire hazard, backed by a comprehensive database comprising actual observations. This method can assist engineers and government officials in swiftly assessing fire-vulnerable bridges with a high accuracy of 89.6%.