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Journals / Eskişehir Osmangazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi / 2009 / Cilt: 22 - Sayı: 3 özel sayı

PREDICTING THE STRENGTH DEVELOPMENT OF DIFFERENT POZZOLAN CEMENTS BY ARTIFICIAL NEURAL NETWORKS

FARKLI PUZOLANLARLA ÜRETİLMİŞ ÇİMENTOLARIN DAYANIM GELİŞİMİNİN YAPAY SİNİR AĞLARIYLA TAHMİNİ

İlker Bekir TOPÇU
Mustafa SARIDEMİR
Cenk KARAKURT
Pages
113–122
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

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

Experimental results were also obtained by building models according to artificial neural network (ANN) system.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study is based on the determination of optimum usage of mineral additives as supplementary cementing material for blended cement production.
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Key Findings & Data:For this purpose, blended cements were produced under laboratory conditions with natural zeolite, trass, volcanic tuff, fly ash and ground granulated blast furnace slag at 10, 20, 30, 40 and 45% clinker replacement ratios.
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Conclusion & Impact:Experimental results were also obtained by building models according to artificial neural network (ANN) system.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

This study is based on the determination of optimum usage of mineral additives as supplementary cementing material for blended cement production. For this purpose, blended cements were produced under laboratory conditions with natural zeolite, trass, volcanic tuff, fly ash and ground granulated blast furnace slag at 10, 20, 30, 40 and 45% clinker replacement ratios. Strength development of the cements was determined with compressive strength tests performed at 2, 7, 28 and 180 days. Experimental results were also obtained by building models according to artificial neural network (ANN) system.

Özet

Bu çalışma, çimento üretiminde kullanılan mineral katkılarda en uygun kullanım oranının belirlenmesi için yapılmıştır. Laboratuvarda farklı kaynaklardan sağlanan doğal zeolit, tras, volkanik tüf, uçucu kül ve yüksek fırın cürufu katkıları, çimento üretiminde klinker yerine %10, 20, 30, 35, 40 ve 45 oranlarında kullanılmıştır. Çimentolar üzerinde yapılan 2, 7, 28 ve 180 günlük basınç dayanımı deneyleriyle dayanım gelişimi belirlenmiştir. Daha sonra deneysel çalışmadan elde edilen veriler kullanılarak yapay sinir ağı (YSA) yönteminde model geliştirilmiştir.

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Cite This Article

TOPÇU, İ. B., SARIDEMİR, M., KARAKURT, C. (2022). PREDICTING THE STRENGTH DEVELOPMENT OF DIFFERENT POZZOLAN CEMENTS BY ARTIFICIAL NEURAL NETWORKS. Eskişehir Osmangazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, Cilt: 22 - Sayı: 3 özel sayı, 113–122. https://ajindex.com/articles/505838

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