Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A machine learning approach to predict explosive spalling of heated concrete
https://orcid.org/http://orcid.org/0000-0002-7934-3027
Explosive spalling is an unfavorable phenomenon observed in concrete when exposed to heating load. It is a great potential threat to safety of concrete structures subjected to accidental thermal loads. Therefore, assessing explosive spalling risk of concrete is important for fire safety design of concrete structures. This paper proposed a popular machine learning approach, i.e., artificial neural network (ANN), to assess explosive spalling risk of concrete. Besides, the decision tree method was also used to execute the same mission for a comparison purpose. Twenty-eight groups of heating tests were conducted to validate the proposed ANN model. The ANN model behaved well in assessing explosive spalling of concrete, with a prediction accuracy of 82.1%. This study shows that ANN is a promising method for adequate classification of concrete as material resistant or not resistant to thermal explosive spalling.
A machine learning approach to predict explosive spalling of heated concrete
https://orcid.org/http://orcid.org/0000-0002-7934-3027
Explosive spalling is an unfavorable phenomenon observed in concrete when exposed to heating load. It is a great potential threat to safety of concrete structures subjected to accidental thermal loads. Therefore, assessing explosive spalling risk of concrete is important for fire safety design of concrete structures. This paper proposed a popular machine learning approach, i.e., artificial neural network (ANN), to assess explosive spalling risk of concrete. Besides, the decision tree method was also used to execute the same mission for a comparison purpose. Twenty-eight groups of heating tests were conducted to validate the proposed ANN model. The ANN model behaved well in assessing explosive spalling of concrete, with a prediction accuracy of 82.1%. This study shows that ANN is a promising method for adequate classification of concrete as material resistant or not resistant to thermal explosive spalling.
A machine learning approach to predict explosive spalling of heated concrete
https://orcid.org/http://orcid.org/0000-0002-7934-3027
Explosive spalling is an unfavorable phenomenon observed in concrete when exposed to heating load. It is a great potential threat to safety of concrete structures subjected to accidental thermal loads. Therefore, assessing explosive spalling risk of concrete is important for fire safety design of concrete structures. This paper proposed a popular machine learning approach, i.e., artificial neural network (ANN), to assess explosive spalling risk of concrete. Besides, the decision tree method was also used to execute the same mission for a comparison purpose. Twenty-eight groups of heating tests were conducted to validate the proposed ANN model. The ANN model behaved well in assessing explosive spalling of concrete, with a prediction accuracy of 82.1%. This study shows that ANN is a promising method for adequate classification of concrete as material resistant or not resistant to thermal explosive spalling.
A machine learning approach to predict explosive spalling of heated concrete
Archiv.Civ.Mech.Eng
Liu, Jin-Cheng (Autor:in) / Zhang, Zhigang (Autor:in)
24.10.2020
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
A machine learning approach to predict explosive spalling of heated concrete
| Springer Verlag | 2020
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Explosive spalling of concrete in fire
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