A machine learning approach to predict explosive spalling of heated concrete

Liu, Jin-Cheng; Zhang, Zhigang

doi:10.1007/s43452-020-00135-w

Artykuł - szczegóły

Tytuł artykułu

A machine learning approach to predict explosive spalling of heated concrete

Autorzy

Liu Jin-Cheng , Zhang Zhigang

Wybrane pełne teksty z tego czasopisma

http://www.springer.com/journal/43452

Identyfikatory

DOI

10.1007/s43452-020-00135-w

Warianty tytułu

Języki publikacji

Abstrakty

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.

Słowa kluczowe

concrete explosive spalling high temperature machine learning artificial neural network

beton temperatura wysoka sieć neuronowa

Wydawca

Springer
Wrocław University of Science and Technology

Czasopismo

Archives of Civil and Mechanical Engineering

Rocznik

2020

Tom

Vol. 20, no. 4

Strony

535--559

Opis fizyczny

Bibliogr. 57 poz., rys., wykr.

Twórcy

autor

Liu Jin-Cheng

jcliu@hku.hk

Department of Civil Engineering, The University of Hong Kong, Pokfulam, Hong Kong, China

autor

Zhang Zhigang

zhangzg@cqu.edu.cn

Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, Ministry of Education, Chongqing 400045, China

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021)

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-b8c3dbe3-5e7b-45c6-8572-057d0ccd1338