Use of regression and machine learning approach in predicting the strength of GBFS‐based geopolymer mortars without experimentation
Yazarlar (4)
Doç. Dr. Murat ÇAVUŞ Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Doç. Dr. Sinan NACAR Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Doç. Dr. Şinasi BİNGÖL Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Doç. Dr. Şahin SÖZEN Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Structural Concrete (Q2) | ||
| Dergi ISSN | 1464-4177 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 10-2026 |
| Cilt / Sayı / Sayfa | 0 / 0 / – | DOI | 10.1002/suco.70369 |
| Makale Linki | https://doi.org/10.1002/suco.70369 | ||
| Özet |
| The determination of the compressive strength (CS) of mortar samples by lengthy experimental tests leads to time and economic losses. Therefore, the predictability of the CS of mortar specimens without conducting tests has gained importance. In this study, the potential for predicting the CS of geopolymer mortars based on granulated blast furnace slag (GBFS) was investigated without the need for experimental work. For this purpose, two model combinations with different independent variables were used. In the first model, molarity, sample age, and curing temperature were set as independent variables, while in the second model, ultrasonic pulse velocity, unit volume weight, porosity, and water absorption properties were included in addition to these variables. In the modeling studies, 108 data sets were used, which were obtained as a result of experimental studies. The prediction of CS values was performed … |
| Anahtar Kelimeler |
| ANNs | compressive strength | prediction modeling | regression analysis |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları |