Machine-learning networks to predict the ultimate axial load and displacement capacity of 3D printed concrete walls with different section geometries
Yazarlar (2)
Dr. Öğr. Üyesi İffet Gamze MÜTEVELLİ ÖZKAN Kastamonu Üniversitesi, Türkiye
Prof. Dr. Alper Aldemir Hacettepe Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Structures (Q1) | ||
| Dergi ISSN | 2352-0124 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 08-2024 |
| Cilt / Sayı / Sayfa | 66 / 1 / 106879–0 | DOI | 10.1016/j.istruc.2024.106879 |
| Makale Linki | http://dx.doi.org/10.1016/j.istruc.2024.106879 | ||
| UAK Araştırma Alanları |
Sayısal Modelleme
Yapı Mekaniği
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| Özet |
| This paper presents details on the machine learning (ML) models for predicting the ultimate axial load capacity and ultimate displacement capacity of 3D printed concrete (3DPC) walls. The large database required for training and testing procedures of ML models is generated using a validated finite element (FE) model, verified using experimental specimens from the literature. Then, a wide range of physical and mechanical properties is selected to form a large database of 3DPC walls. To this end, 61800 3DPC walls with five different cross-sections and with various geometries were analyzed. The ultimate axial load capacity and ultimate displacement capacity of each wall are determined using explicit dynamic analysis. In conclusion, ML algorithms provide accurate predictions with coefficient of determination values of 0.95 and above. It should be noted that the prediction of the maximum axial load capacities was … |
| Anahtar Kelimeler |
| 3D printed concrete walls | Cross-sectional areas | Machine-learning algorithms | Maximum axial load predictions | Parametric finite element analyses |
Science Direct
Machine-learning networks to predict the ultimate axial load and displacement capacity of 3D printed concrete walls with different section geometries
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 13 |
| Scopus | 13 |
| Google Scholar | 13 |