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Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete

Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days... Evaluating the in situ concrete compressive strength by means of cores cut from hardened concrete is acknowledged as the most ordinary method, however, it is very difficult to predict the compressive strength of concrete since it is affected by many factors such as different mix designs, methods of mixing, curing conditions, compaction, etc. In this paper, considering the experimental results, three different models of multiple linear regression model (MLR), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) are established, trained, and tested within the Matlab programming environment for predicting the 28 days compressive strength of concrete with 173 different mix designs. Finally, these three models are compared with each other and resulted in the fact that ANN and ANFIS models enables us to reliably evaluate the compressive strength of concrete with different mix designs, however, multiple linear regression model is not feasible enough in this area because of nonlinear relationship between the concrete mix parameters. Finally, the sensitivity analysis (SA) for two different sets of parameters on the concrete compressive strength prediction are carried out. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Frontiers of Structural and Civil Engineering Springer Journals

Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete

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References (31)

Publisher
Springer Journals
Copyright
Copyright © Higher Education Press and Springer-Verlag Berlin Heidelberg 2017
ISSN
2095-2430
eISSN
2095-2449
DOI
10.1007/s11709-016-0363-9
Publisher site
See Article on Publisher Site

Abstract

Evaluating the in situ concrete compressive strength by means of cores cut from hardened concrete is acknowledged as the most ordinary method, however, it is very difficult to predict the compressive strength of concrete since it is affected by many factors such as different mix designs, methods of mixing, curing conditions, compaction, etc. In this paper, considering the experimental results, three different models of multiple linear regression model (MLR), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) are established, trained, and tested within the Matlab programming environment for predicting the 28 days compressive strength of concrete with 173 different mix designs. Finally, these three models are compared with each other and resulted in the fact that ANN and ANFIS models enables us to reliably evaluate the compressive strength of concrete with different mix designs, however, multiple linear regression model is not feasible enough in this area because of nonlinear relationship between the concrete mix parameters. Finally, the sensitivity analysis (SA) for two different sets of parameters on the concrete compressive strength prediction are carried out.

Journal

Frontiers of Structural and Civil EngineeringSpringer Journals

Published: Mar 1, 2017

Keywords: concrete; 28 days compressive strength; multiple linear regression; artificial neural network; ANFIS; sensitivity analysis (SA)

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