Optimization of foam concrete characteristics using response surface methodology and artificial neural networks
Yazarlar (6)
Doç. Dr. Bilal Kurşuncu Bartin Üniversitesi, Türkiye
Prof. Dr. Osman Gençel Bartın Üniversitesi, Türkiye
Doç. Dr. Oğuzhan Yavuz BAYRAKTAR Kastamonu Üniversitesi, Türkiye
Jinyan Shi
Central South University, Çin
Central South University, Çin
Mahdi Nematzadeh
University Of Mazandaran, İran
University Of Mazandaran, İran
Doç. Dr. Gökhan Kaplan Atatürk Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Construction and Building Materials (Q1) | ||
| Dergi ISSN | 0950-0618 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 06-2022 |
| Cilt / Sayı / Sayfa | 337 / 1 / 127575–0 | DOI | 10.1016/j.conbuildmat.2022.127575 |
| Makale Linki | http://dx.doi.org/10.1016/j.conbuildmat.2022.127575 | ||
| UAK Araştırma Alanları |
Yapı Malzemeleri
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| Özet |
| In this study, influences of waste marble powder (WMP) and rice husk ash (RHA) partially replaced instead of fine aggregate and cement into foam concrete (FC) on compressive and flexural strength, porosity, and thermal conductivity coefficient were investigated using Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) methods. The foam parameter was determined as two levels in the experimental design, and the WMP and RHA parameters were determined as three levels. With the RSM analysis, the most influential parameters for compressive and flexural strength were determined as Foam WMP and RHA, respectively. Likewise, the order of effective parameters for porosity and thermal conductivity coefficient was found as foam WMP and RHA. With the RSM method, R2 values were obtained as 0.9492 for compressive strength, 0.9312 for flexural strength, 0.9609 for porosity, and 0.9778 … |
| Anahtar Kelimeler |
| ANN | Foam concrete | Optimization | Rice husk ash | RSM | Waste marble powder |
Science Direct
Optimization of foam concrete characteristics using response surface methodology and artificial neural networks
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 69 |
| Scopus | 80 |
| Google Scholar | 89 |