High correlated variables creator machine: Prediction of the compressive strength of concrete

•High correlated creator variables (HCVCM) uses functions to create stronger inputs.•A case study with two inputs and one output was solved.•The effect of HCVCM on the accuracy of known models was evaluated.•HCVCM improves the accuracy of models significantly. In this paper, we introduce a novel hyb...

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Bibliographic Details
Published in:	Computers & structures Vol. 247; p. 106479
Main Authors:	Shishegaran, Aydin, Varaee, Hesam, Rabczuk, Timon, Shishegaran, Gholamreza
Format:	Journal Article
Language:	English
Published:	New York Elsevier Ltd 15.04.2021 Elsevier BV
Subjects:	Adaptive Neuro-Fuzzy Inference System (ANFIS) Adaptive systems Artificial neural networks Compressive strength Concrete Correlation Fuzzy logic Gene expression Gene Expression Programming (GEP) Mean square errors Model accuracy Prediction models Rebound Hammer (RH) Rebound Number (RN) Root-mean-square errors Statistical analysis Step-By-Step Regression (SBSR) Ultrasonic Pulse Velocity (UPV) Ultrasonic Pulse Velocity (UPV) Step-By-Step Regression (SBSR) Adaptive Neuro-Fuzzy Inference System (ANFIS) Rebound Hammer (RH) Gene Expression Programming (GEP) Rebound Number (RN)
ISSN:	0045-7949, 1879-2243
Online Access:	Get full text
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