Unsupervised Machine Learning for Fire Resistance Analysis

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dc.contributor.authorÇiftçioğlu A.Ö.
dc.contributor.authorNaser M.Z.
dc.date.accessioned2024-07-22T08:03:23Z
dc.date.available2024-07-22T08:03:23Z
dc.date.issued2023
dc.description.abstractDue 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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
dc.identifier.DOI-ID10.1007/978-3-031-40395-8_15
dc.identifier.issn18650929
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/12267
dc.language.isoEnglish
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.subjectClassification (of information)
dc.subjectClustering algorithms
dc.subjectCompressive strength
dc.subjectFire resistance
dc.subjectFuzzy clustering
dc.subjectReinforced concrete
dc.subjectTrees (mathematics)
dc.subjectClassification and regression tree models
dc.subjectClusterings
dc.subjectConcrete
dc.subjectExposed to
dc.subjectFire resistance analysis
dc.subjectFire resistance properties
dc.subjectHighest temperature
dc.subjectMachine-learning
dc.subjectReinforced concrete column
dc.subjectUnsupervised machine learning
dc.subjectMachine learning
dc.titleUnsupervised Machine Learning for Fire Resistance Analysis
dc.typeConference paper

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