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Frontiers of Structural and Civil Engineering

Front. Struct. Civ. Eng.    2017, Vol. 11 Issue (1) : 90-99     https://doi.org/10.1007/s11709-016-0363-9
RESEARCH ARTICLE
Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete
Faezehossadat KHADEMI1(),Mahmoud AKBARI2,Sayed Mohammadmehdi JAMAL3,Mehdi NIKOO4
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
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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.

Keywords concrete      28 days compressive strength      multiple linear regression      artificial neural network      ANFIS      sensitivity analysis (SA)     
Corresponding Author(s): Faezehossadat KHADEMI   
Online First Date: 16 November 2016    Issue Date: 27 February 2017
 Cite this article:   
Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL, et al. Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete[J]. Front. Struct. Civ. Eng., 2017, 11(1): 90-99.
 URL:  
http://journal.hep.com.cn/fsce/EN/10.1007/s11709-016-0363-9
http://journal.hep.com.cn/fsce/EN/Y2017/V11/I1/90
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Faezehossadat KHADEMI
Mahmoud AKBARI
Sayed Mohammadmehdi JAMAL
Mehdi NIKOO
Fig.1  Overview of this research
number parameters unit maximum minimum
1 compressive strength of concrete kg/cm2 394.00 173.00
2 water-cement ratio _ 0.50 0.24
3 maximum size of aggregate mm 50.00 5.12
4 gravel kg 1050.00 559.00
5 cement kg 549.00 243.00
6 sand 3/8 kg 523.00 303.00
7 sand 3/4 kg 693.00 365.00
8 fineness modulus of sand _ 9.20 2.40
Tab.1  Characteristics of cylindrical samples [13]
Number parameters standard deviation (?) mean (µ)
1 compressive strength of concrete 54.98 279.27
2 water-cement ratio 0.19 0.43
3 maximum size of aggregate 14.25 23.89
4 gravel 95.71 779.13
5 cement 72.70 385.55
6 sand 3/8 64.45 427.05
7 sand 3/4 90.06 563.31
8 fineness modulus of Sand 1.30 3.27
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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