Estimation of compressive strength of waste andesite powder-added concrete using an artifical neural network

CEYLAN H., DAVRAZ M., SİVRİ M.

Tehnicki Vjesnik, cilt.28, sa.4, ss.1182-1186, 2021 (SCI-Expanded, Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 28 Sayı: 4
  • Basım Tarihi: 2021
  • Doi Numarası: 10.17559/tv-20200604232451
  • Dergi Adı: Tehnicki Vjesnik
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1182-1186
  • Anahtar Kelimeler: Artificial neural network, Concrete compressive strength, Waste andesite powder
  • Isparta Uygulamalı Bilimler Üniversitesi Adresli: Evet

Özet

In this study, the effects of using andesite powder wastes-produced from natural stone factories as mineral additives in concrete manufacturing-on the compressive strength of concrete were modeled using an Artificial Neural Network (ANN). To achieve this, cement mixtures were produced by using waste andesite powder (WAP) mixture at ratios of 0% (control), 10%, 15% and 20%. The effects of curing time were investigated by preparing specimens at 28 and 90 days. The training set was formed by using cement and the specified WAP mixtures and curing time parameters. It was observed that the results obtained from the training ANNs were consistent with the experimental data.