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Unsupervised Machine Learning for Fire Resistance Analysis

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Science, Engineering Management and Information Technology (SEMIT 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1808))

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Abstract

Due to its inert nature, concrete has good fire resistance properties. As such, concrete has often been favored for construction – especially where fire hazard is expected. However, this does not mean that reinforced concrete cannot catch fire. It can still be affected by heat and, if exposed to high temperatures, can eventually break down. Therefore, the fire resistance of the reinforced concrete (RC) columns is a critical concern. There are many ways for assessing the fire resistance of structures, but it is difficult to quantify the fire resistance in quantitative terms. The purpose of this work is to investigate the use of unsupervised machine learning by means of clustering to examine the fire resistance of RC columns. A database of over 144 RC columns subjected to standard fire conditions has been collected and then examined via the interpretable Fuzzy C-Means algorithm (FCM) and the Classification and Regression Tree (CART) model. Our results indicate that this clustering technique groups RC columns into four natural groups – each with specific properties and characteristics. Moreover, the CART model is used to analyze the variables used as the basis for the clustering of RC columns. Accordingly, when RC columns are separated into four natural clusters, the first split occurs due to restrictions, and the second separation is controlled by the compressive strength and reinforcement ratios of the columns. This research might be the first to attempt to leverage clustering analysis to investigate the fire response of RC columns. The findings of the study clearly show that unsupervised machine learning can provide valuable insights to fire engineers often missing from traditional supervised learning.

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Authors and Affiliations

  1. Department of Civil Engineering, Faculty of Engineering, Manisa Celal Bayar University, Manisa, Turkey

    Aybike Özyüksel Çiftçioğlu

  2. School of Civil and Environmental Engineering & Earth Sciences, AI Research Institute for Science and Engineering (AIRISE), Clemson, USA

    M. Z. Naser

Authors
  1. Aybike Özyüksel Çiftçioğlu
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  2. M. Z. Naser
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Correspondence to Aybike Özyüksel Çiftçioğlu .

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Editors and Affiliations

  1. Kharazmi University, Tehran, Iran

    Abolfazl Mirzazadeh

  2. Ankara Yıldırım Beyazıt University, Ankara, Türkiye

    Babek Erdebilli

  3. Istinye University, Istanbul, Türkiye

    Erfan Babaee Tirkolaee

  4. Poznań University of Technology, Poznań, Poland

    Gerhard-Wilhelm Weber

  5. Indian Institute of Technology Delhi, New Delhi, India

    Arpan Kumar Kar

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Çiftçioğlu, A.Ö., Naser, M.Z. (2023). Unsupervised Machine Learning for Fire Resistance Analysis. In: Mirzazadeh, A., Erdebilli, B., Babaee Tirkolaee, E., Weber, GW., Kar, A.K. (eds) Science, Engineering Management and Information Technology. SEMIT 2022. Communications in Computer and Information Science, vol 1808. Springer, Cham. https://doi.org/10.1007/978-3-031-40395-8_15

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  • DOI: https://doi.org/10.1007/978-3-031-40395-8_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-40394-1

  • Online ISBN: 978-3-031-40395-8

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