| Yazarlar |
Prof. Dr. Hasbi YAPRAK
Kastamonu Üniversitesi, Türkiye |
Abdulkadir Karacı
Kastamonu Üniversitesi, Türkiye |
|
İlhami Demir
Kırıkkale Üniversitesi, Türkiye |
| Özet |
| The present study aims at developing an artificial neural network (ANN) to predict the compressive strength of concrete. A data set containing a total of 72 concrete samples was used in the study. The following constituted the concrete mixture parameters: two distinct w/c ratios (0.63 and 0.70), three different types of cements and three different cure conditions. Measurement of compressive strengths was performed at 3, 7, 28 and 90 days. Two different ANN models were developed, one with 4 input and 1 output layers, 9 neurons and 1 hidden layer, and the other with 5, 6 neurons, 2 hidden layers. For the training of the developed models, 60 experimental data sets obtained prior to the process were used. The 12 experimental data not used in the training stage were utilized to test ANN models. The researchers have reached the conclusion that ANN provides a good alternative to the existing compressive strength prediction methods, where different cements, ages and cure conditions were used as input parameters. © 2011 Springer-Verlag London Limited. |
| Anahtar Kelimeler |
| Age | Artificial neural network | Cement | Compressive strength | Cure conditions |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | NEURAL COMPUTING & APPLICATIONS |
| Dergi ISSN | 0941-0643 |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Makale Dili | İngilizce |
| Basım Tarihi | 01-2013 |
| Cilt No | 22 |
| Sayı | 1 |
| Sayfalar | 133 / 141 |
| Doi Numarası | 10.1007/s00521-011-0671-x |
| Atıf Sayıları | |
| WoS | 44 |
| SCOPUS | 48 |
