Çakmak T., Ustabaş İ.
4th International Civil Engineering & Architecture Conference (ICEARC'25), Trabzon, Türkiye, 17 - 19 Mayıs 2025, ss.1-8, (Tam Metin Bildiri)
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Yayın Türü:
Bildiri / Tam Metin Bildiri
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Basıldığı Şehir:
Trabzon
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Basıldığı Ülke:
Türkiye
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Sayfa Sayıları:
ss.1-8
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Recep Tayyip Erdoğan Üniversitesi Adresli:
Evet
Özet
Concrete is one of the best commonly applied
building materials due to its various advantages as such high strength,
durability, resistance to high temperatures, etc. However, due to the CO2
emissions abput the production and consumption of the raw material cement,
research has long been conducted into various alternatives. Various
supplementary cementitious materials with pozzolanic properties are at the
leading of such options. In addition to reducing the CO2 emissions
of these materials, it is possible to improve various properties, especially
strength and durability. However, obtaining the properties that occur when
various materials are used together with concrete results from long laboratory
processes and labour. Therefore, ML algorithms allow us to reduce this time and
effort. In this study, a variety of ML algorithms such as RF, ET and GBR were
used to predict the properties of concretes with different mix parameters such
as different proportions of FA, GGBS, aggregate quantity and curing time. In
addition, feature importance analyses based on different algorithms were
performed to elucidate the background functioning of these algorithms. As a
result of the analysis, the GPR algorithm showed the highest prediction and
generalisation performance with an R2 value of 0.871. The ET and RF
algorithms follow this performance. In addition, as a result of the feature
importance analyses, the most important mixing parameters for all algorithms
were found to be cement ratio and setting time. The values obtained as a result
of the study show the suitability of using ML algorithms to detect different
properties of concretes.