^{1}. Department of Civil, Architectural and Environmental Engineering, Illinois Institute of Technology, Chicago IL 60616, USA ^{2}. Civil Engineering Department, University of Kashan, Kashan 8731753153, Iran ^{3}. Department of Civil Engineering, University of Hormozgan, Bandar Abbas 3995, IRAN ^{4}. Ahvaz Branch, Islamic Azad University, Ahvaz 6134937333, IRAN

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.

Tab.2 Mean and standard deviation of each concrete characteristic [13]

Fig.2 Architecture of artificial neural network

Fig.3 Schematic of ANFIS Architecture

Fig.4 Structure of the ANN model used in Matlab software

Fig.5 Structure of the ANFIS model used in Matlab software

Fig.6 Comparison between the “measured” and “predicted” parameters for “training” data in MLR model

Fig.7 The training state for the artificial neural network model

Fig.8 Best validation performance in artificial neural network model

Fig.9 Comparison between the “target” and “output” parameters for “training” data in ANN model

Fig.10 Comparison between the “target” and “output” parameters for “validation” data in ANN model

Fig.11 Comparison between the “target” and “output” parameters for “training” data in ANFIS model

Fig.12 Comparison between the “target” and “output” parameters for “validation” data in ANFIS model

Fig.13 Comparison between the “target” and “output” parameters for “test” data in ANFIS model

Fig.14 Comparison between the measured and predicted compressive strength by the MLR model for “test” data

Fig.15 Comparison between the measured and predicted compressive strength by the ANN model for “test” data

Fig.16 Comparison between the measured and predicted compressive strength by the ANFIS model for “test” data

prediction model name

coefficient of determination for set 1

coefficient of determination for set 2

MLR

0.7456

0.7311

ANN

0.9226

0.9010

ANFIS

0.8212

0.8053

Tab.3 Performing sensitivity analysis on MLR, ANN, and ANFIS models

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