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

Autor: Faezehossadat Khademi, Mahmoud Akbari, Sayed Mohammadmehdi Jamal, Mehdi Nikoo
Rok vydání: 2017
Předmět:
Zdroj: Frontiers of Structural and Civil Engineering. 11:90-99
ISSN: 2095-2449
2095-2430
DOI: 10.1007/s11709-016-0363-9
Popis: 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.
Databáze: OpenAIRE
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