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Journals / Celal Bayar Üniversitesi Fen Bilimleri Dergisi / 2022 / Cilt: 18 - Sayı: 4

ESTIMATING THE COMPRESSIVE STRENGTH OF FLY ASH ADDED CONCRETE USING ARTIFICIAL NEURAL NETWORKS

Zafer KURT
Talip ÇAKMAK
Ali GÜRBÜZ
İlker USTABAŞ
Pages
365–369
Publication Date
2022-12-26
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a result of the study, it has been observed that the predictions made using artificial neural networks are very close to the strength values obtained from the experiments.I

📌 3 Key Takeaways

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Objective & Hypothesis:The aim of this study is to develop an artificial intelligence that predicts the compressive strength of fly ash substituted concretes using material mixing ratios.
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Key Findings & Data:Within the scope of the study, 5 different fly ash mixed concrete samples were produced.
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Conclusion & Impact:As a result of the study, it has been observed that the predictions made using artificial neural networks are very close to the strength values obtained from the experiments.I
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Artificial Intelligence · artificial neural networks · fly ash substituted concretes

Abstract

The aim of this study is to develop an artificial intelligence that predicts the compressive strength of fly ash substituted concretes using material mixing ratios. Within the scope of the study, 5 different fly ash mixed concrete samples were produced. The strength values were estimated using artificial neural networks before the produced samples were subjected to the pressure test. In order to develop the artificial neural network, it is introduced as a dataset of 3000 different mixing ratios consisting of experimental results in the existing literature. In order to estimate the compressive strength, varying ratios of 8 different materials such as water, cement, fly ash entering the mixture are analyzed. As a result of the study, it has been observed that the predictions made using artificial neural networks are very close to the strength values obtained from the experiments.I

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Cite This Article

KURT, Z., ÇAKMAK, T., GÜRBÜZ, A., USTABAŞ, İ. (2022). ESTIMATING THE COMPRESSIVE STRENGTH OF FLY ASH ADDED CONCRETE USING ARTIFICIAL NEURAL NETWORKS. Celal Bayar Üniversitesi Fen Bilimleri Dergisi, Cilt: 18 - Sayı: 4, 365–369. https://ajindex.com/articles/866009

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References (1)

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