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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Vydáno v:	Computers & structures Ročník 247; s. 106479
Hlavní autoři:	Shishegaran, Aydin, Varaee, Hesam, Rabczuk, Timon, Shishegaran, Gholamreza
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Elsevier Ltd 15.04.2021 Elsevier BV
Témata:	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
On-line přístup:	Získat plný text
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