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A platform for research: civil engineering, architecture and urbanism
Fire induced progressive collapse potential assessment of steel framed buildings using machine learning
https://orcid.org/0000-0002-9176-8159
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.
Highlights A new machine learning framework for fire induced progressive collapse assessment is developed. Failure patterns of structural elements are defined using critical temperature method. A method based on Monte Carlo simulation is developed for training data generation Prediction Accuracy of different classifiers is investigated.
Fire induced progressive collapse potential assessment of steel framed buildings using machine learning
https://orcid.org/0000-0002-9176-8159
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.
Highlights A new machine learning framework for fire induced progressive collapse assessment is developed. Failure patterns of structural elements are defined using critical temperature method. A method based on Monte Carlo simulation is developed for training data generation Prediction Accuracy of different classifiers is investigated.
Fire induced progressive collapse potential assessment of steel framed buildings using machine learning
https://orcid.org/0000-0002-9176-8159
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.
Highlights A new machine learning framework for fire induced progressive collapse assessment is developed. Failure patterns of structural elements are defined using critical temperature method. A method based on Monte Carlo simulation is developed for training data generation Prediction Accuracy of different classifiers is investigated.
Fire induced progressive collapse potential assessment of steel framed buildings using machine learning
Fu, Feng (author)
2019-12-23
Article (Journal)
Electronic Resource
English
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