Prediction of Bridge Fires Characteristics Using Machine Learning

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.

Name thesis:degree_name

Master of Applied Science (M.App.Sc.)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Engineering, Civil

Grantor dc:publisher

Carleton University

Year dc:date.issued

2022

Author dc:creator

• Mehrpour, Fatemeh

Statement dc:rights

• Copyright © 2022 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.

Language dc:language.iso

en

DOI

10.22215/etd/2022-15127

OAI identifier oai:identifier

oai:carleton.scholaris.ca:20.500.14718/42711