Prediction of Bridge Fires Characteristics Using Machine Learning

Abstract

Bridges are an essential component of transportation systems for traffic passage across natural or man-made obstacles. Modern urban trends and growing travel patterns have resulted in a rise in the number of bridges to avoid road conflict. A major fire might cause irreversible structural deterioration or the bridge's failure. This research studies the critical factors of bridge fire incidents using a comprehensive database containing 171 bridges. Using an Artificial Neural Network (ANN) model, the vulnerability of bridges in fire is estimated, and the extent of damage is determined based on several key factors including bridge proximity to urban, suburban, or rural areas, structural system, construction materials, annual average daily traffic (AADT), ignition source, types of combustible, position of the fire against bridges, and the fire-caused damage level. The outcome of this research will help predict the risk of fire in bridges with various characteristics and the level of damage.