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Fire induced Progressive Collapse Potential assessment of Steel Framed Buildings using machine learning

Fu, F. ORCID: 0000-0002-9176-8159 (2020). Fire induced Progressive Collapse Potential assessment of Steel Framed Buildings using machine learning. Journal of Constructional Steel Research, 166, 105918.. doi: 10.1016/j.jcsr.2019.105918

Abstract

In this paper, a new Machine Learning framework is developed for fast prediction of the failure patterns of simple steel framed buildings in fire and subsequent progressive collapse potential assessment. This pilot study provides a new tool of fire safety assessment for engineers in an efficient and effective way in the future. The concept of Critical Temperature Method is used to define the failure patterns for each structural member which is incorporated into a systematic methodology employing both Monte Carlo Simulation and Random Sampling to generate a robust and sufficient large dataset for training and testing, hence guarantees the accurate prediction. A comparative study for different machine learning classifiers is made. Three classifiers are chosen for failure patterns prediction of buildings under fire: Decision Tree, KNN and Neural Network using Google Keras with TensorFlow which is specially used for Google Brain Team. The Machine Learning framework is implemented using codes programmed by the author in VBA and Python language. A case study of a 2 story by 2 bay steel framed building was made. Two different fire scenarios were chosen. The procedure gives satisfactory prediction of the failure pattern and collapse potential of the building under fire.

Publication Type: Article
Additional Information: © 2019 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords: Fire; Decision Tree; KNN; Neural Network; Critical Temperature Method; TensorFlow; Monte Carlo Simulation; Random Sampling
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Departments: School of Mathematics, Computer Science & Engineering > Engineering > Civil Engineering
Date Deposited: 23 Dec 2019 15:03
URI: https://openaccess.city.ac.uk/id/eprint/23386
[img] Text - Accepted Version
This document is not freely accessible until 9 January 2021 due to copyright restrictions.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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