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Journals / International Journal of Intelligent Systems and Applications in Engineering / 2019 / Cilt: 7 - Sayı: 1

Prediction of Bending Strength of Self-Leveling Glass Fiber Reinforced Concrete

Sadık Alper YILDIZEL
Mehmet BABOĞLU UZUN
Mustafa Tolga ÇÖĞÜRCÜ
Pages
7–12
DOI
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AI Summary & Key Takeaways

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

Results showed that the prediction results are in good agreement with the field data.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Many studies have been conducted on the prediction of fiber reinforced concrete strength; however, there are very rare data concerning the prediction of bending strength values of self-leveling glass fiber reinforced concrete.
📊
Key Findings & Data:An artificial neural network model (ANN) is designed with an extensive experimental data including 395 four-point bending tests, and input parameters as white cement amount, maximum aggregate size, glass fiber content, water cement ratio, superplasticizer and metakaolin content and slump test results.
💡
Conclusion & Impact:Results showed that the prediction results are in good agreement with the field data.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Özet

Many studies have been conducted on the prediction of fiber reinforced concrete strength; however, there are very rare data concerning the prediction of bending strength values of self-leveling glass fiber reinforced concrete. And there is no study for prediction of bending strength of self-leveling glass fiber reinforced concrete from mixture ingredients and slump values. In the present study, relationships between the bending strength and the mixture proportions are explored. An artificial neural network model (ANN) is designed with an extensive experimental data including 395 four-point bending tests, and input parameters as white cement amount, maximum aggregate size, glass fiber content, water cement ratio, superplasticizer and metakaolin content and slump test results. Effect of each parameter on the bending strength is investigated with the developed model. An empirical and user-friendly formula was obtained with the generalization capabilities of the ANN. Results showed that the prediction results are in good agreement with the field data. And these numerical results with high efficiency can make it possible to use the neural design for real-life self-leveling glass fiber reinforced concrete applications.

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Cite This Article

YILDIZEL, S. A., UZUN, M. B., ÇÖĞÜRCÜ, M. T. (2019). Prediction of Bending Strength of Self-Leveling Glass Fiber Reinforced Concrete. International Journal of Intelligent Systems and Applications in Engineering, Cilt: 7 - Sayı: 1, 7–12. https://ajindex.com/articles/160248

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