Compressive strength prediction of hollow concrete masonry blocks using artificial intelligence algorithms
Authors:
- Pouyan FakharianPouyan Fakharian
- Danial Rezazadeh EidgaheeDEDanial Rezazadeh Eidgahee
- Mahdi AkbariMAMahdi Akbari
- Hashem JahangirHJHashem Jahangir
- Amir Ali TaebATAmir Ali Taeb
- 14 December 2022
- journal article
- research article
- Published by Elsevier in Structures
- Vol. 47, 1790-1802
Abstract
No abstract availableKeywords
This publication has 42 references indexed in Scilit:
- Estimation of the axial behaviour of masonry walls based on Artificial Neural NetworksComputers & Structures, 2013
- Using artificial neural networks for predicting the elastic modulus of recycled aggregate concreteConstruction and Building Materials, 2013
- Artificial neural network for predicting drying shrinkage of concreteConstruction and Building Materials, 2013
- Prediction of the concrete compressive strength by means of core testing using GMDH-type neural network and ANFIS modelsComputational Materials Science, 2012
- Neural networks for predicting compressive strength of structural light weight concreteConstruction and Building Materials, 2009
- On the compressive strength prediction for concrete masonry prismsMaterials and Structures, 2009
- Mechanics of hollow concrete block masonry prisms under compression: Review and prospectsCement and Concrete Composites, 2007
- Prediction of concrete elastic modulus using adaptive neuro-fuzzy inference systemCivil Engineering and Environmental Systems, 2006
- Compression Behavior and Failure Mechanisms of Concrete Masonry PrismsJournal of Materials in Civil Engineering, 2005
- Factors influencing compressive strength of concrete masonry prismsMagazine of Concrete Research, 1996