BETON DAYANIM ÖZELLİKLERİNİN YÜZEY TEPKİ YÖNTEMİ, GENETİK ALGORİTMA VE YAPAY SİNİR AĞLARI İLE TAHMİNİ
Öz
Anahtar Kelimeler
Destekleyen Kurum
Proje Numarası
Teşekkür
Kaynakça
- American Concrete Institute (ACI) Committee 211, 1993. Guide for Selecting Proportions for High Strength Concrete with Portland Cement and Fly Ash. ACI211.4R-93, Detroit, MI.
- Armaghani, D.J., Asteris, P.G., 2021. A Comparative Study of ANN and ANFIS Models for the Prediction of Cement-Based Mortar Materials Compressive Strength. Neural Computing and Applications, 33(9), 4501-4532.
- ASTM C597, 1994. Standard Test Method for Pulse Velocity Through Concrete. Annual book of ASTM Standards, Vol. 04.02. West Conshohocken, PA: American Society for Testing and Materials.
- Asutkar, P., Shinde, S.B., Patel, R., 2017. Study on the Behaviour of Rubber Aggregates Concrete Beams Using Analytical Approach. Engineering Science and Technology, an International Journal, 20(1), 151-159.
- Cihan, M.T., Güner, A., Yüzer, N., 2013. Response Surfaces for Compressive Strength of Concrete. Construction and Building Materials, 40, 763-774.
- Deng, F., He, Y., Zhou, S., Yu, Y., Cheng, H., Wu, X., 2018. Compressive Strength Prediction of Recycled Concrete Based on Deep Learning. Construction and Building Materials, 175, 562-569.
- Duan, Z.H., Kou, S.C., Poon, C.S., 2013. Prediction of Compressive Strength of Recycled Aggregate Concrete Using Artificial Neural Networks. Construction and Building Materials, 40, 1200-1206.
- Feng, D.C., Liu, Z.T., Wang, X.D., Chen, Y., Chang, J.Q., Wei, D.F., Jiang, Z.M., 2020. Machine learning-Based Compressive Strength Prediction for Concrete: An Adaptive Boosting Approach. Construction and Building Materials, 230, 117000.
Ayrıntılar
Birincil Dil
Türkçe
Konular
Yer Bilimleri ve Jeoloji Mühendisliği (Diğer)
Bölüm
Araştırma Makalesi
Yazarlar
Ekin Köken
0000-0003-0178-329X
Türkiye
Yayımlanma Tarihi
30 Haziran 2022
Gönderilme Tarihi
22 Ekim 2021
Kabul Tarihi
13 Şubat 2022
Yayımlandığı Sayı
Yıl 2022 Cilt: 10 Sayı: 2