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

doi:10.1007/s11709-016-0363-9

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

Faeze 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

Download: PDF(1664 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

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): Faeze KHADEMI

Online First Date: 16 November 2016 Issue Date: 27 February 2017

Cite this article:

Faeze 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:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-016-0363-9
https://academic.hep.com.cn/fsce/EN/Y2017/V11/I1/90

Fig.1 Overview of this research

Tab.1 Characteristics of cylindrical samples [13]

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

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

1	F Khademi, M Akbari, S M Jamal. Prediction of compressive strength of concrete by data-driven models. i-manager’s Journal on Civil Engineering, 2015, 5(2): 16–23
2	M Nikoo, F Torabian Moghadam, L Sadowski. Prediction of concrete compressive strength by evolutionary artificial neural networks. Advances in Materials Science and Engineering, 2015
3	J Sobhani, M Najimi, A R Pourkhorshidi, T Parhizkar. Prediction of the compressive strength of no-slump concrete: a comparative study of regression, neural network and ANFIS models. Construction & Building Materials, 2010, 24(5): 709–718
4	M Sarıdemir. Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic. Advances in Engineering Software, 2009, 40(9): 920–927
5	A A Ramezanianpour, M Sobhani , J Sobhani . Application of network-based neuro-fuzzy system for prediction of the strength of high strength concrete, 2004
6	L Bal, F Buyle-Bodin. Artificial neural network for predicting drying shrinkage of concrete. Construction & Building Materials, 2013, 38: 248–254
7	U Atici. Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network. Expert Systems with Applications, 2011, 38(8): 9609–9618
8	Ł Sadowski, M Nikoo, M Nikoo. Principal Component analysis combined with a self-organization feature map to determine the pull-off adhesion between concrete layers. Construction & Building Materials, 2015, 78: 386–396
9	M Nikoo, P Zarfam, M Nikoo. Determining displacement in concrete reinforcement building with using evolutionary artificial neural networks. World Applied Sciences Journal, 2012, 16(12): 1699–1708
10	J Amani, R Moeini. Prediction of shear strength of reinforced concrete beams using adaptive neuro-fuzzy inference system and artificial neural network. Scientia Iranica, 2012, 19(2): 242–248
11	L Sadowski, M Nikoo. Corrosion current density prediction in reinforced concrete by imperialist competitive algorithm. Neural Computing & Applications, 2014, 25(7–8): 1627–1638
12	F Khademi, K Behfarnia. Evaluation of concrete compressive strength using artificial neural network and multiple linear regression models. Iran University of Science & t echnology (Elmsford, N.Y.), 2016, 6(3): 423–432
13	M Nikoo, P Zarfam, H Sayahpour. Determination of compressive strength of concrete using self organization feature map (SOFM). Engineering with Computers, 2015, 31(1): 113–121
14	N Vu-Bac, T Lahmer, Y Zhang, X Zhuang, T Rabczuk. Stochastic predictions of interfacial characteristic of polymeric nanocomposites (PNCs). Composites. Part B, Engineering, 2014, 59: 80–95
15	N Vu-Bac, T Lahmer, H Keitel, J Zhao, X Zhuang, T Rabczuk. Stochastic predictions of bulk properties of amorphous polyethylene based on molecular dynamics simulations. Mechanics of Materials, 2014, 68: 70–84
16	N Vu-Bac, R Rafiee, X Zhuang, T Lahmer, T Rabczuk. Uncertainty quantification for multiscale modeling of polymer nanocomposites with correlated parameters. Composites. Part B, Engineering, 2015, 68: 446–464
17	F Khademi, S M. Jamal, Predicting the 28 Days compressive strength of concrete using artificial neural network. i-Manager's Journal on Civil Engineering, 2016, 6(2): 1
18	A Sadrmomtazi, J Sobhani, M A Mirgozar. Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS. Construction & Building Materials, 2013, 42: 205–216
19	J Hoła, K. SchabowiczApplication of artificial neural networks to determine concrete compressive strength based on non−destructive tests. Journal of civil Engineering and Management, 2005, 11(1): 23–32
20	H Naderpour, A Kheyroddin, G G Amiri. Prediction of FRP-confined compressive strength of concrete using artificial neural networks. Composite Structures, 2010, 92(12): 2817–2829
21	K Hornik, M Stinchcombe, H White. Multilayer feedforward networks are universal approximators. Neural Networks, 1989, 2(5): 359–366
22	D I Jeong, Y O Kim. Rainfall-runoff models using artificial neural networks for ensemble streamflow prediction. Hydrological Processes, 2005, 19(19): 3819–3835
23	A R Boğa, M Öztürk, İ B Topçu. Using ANN and ANFIS to predict the mechanical and chloride permeability properties of concrete containing GGBFS and CNI. Composites. Part B, Engineering, 2013, 45(1): 688–696
24	J S R Jang, C T Sun. Neuro-fuzzy modeling and control. Proceedings of the IEEE, 1995, 83(3): 378–406
25	MATLAB and Statistics Toolbox Release. The MathWorks, Inc., Natick, Massachusetts, United States, 2014
26	A H Gandomi, S Sajedi, B Kiani, Q Huang. Genetic programming for experimental big data mining: A case study on concrete creep formulation. Automation in Construction, 2016, 70: 89–97
27	F Khademi, M Akbari, S M. JamalMeasuring compressive strength of puzzolan concrete by ultrasonic pulse velocity method. i-Manager’s Journal on Civil Engineering, 2015, 5(3), 23
28	S Sajedi, A Razavizadeh, Z Minaii, F Ghassemzadeh, M Shekarchi. A rational method for calculation of restrained shrinkage stresses in repaired concrete members. Concrete Solutions, 2011, 461
29	S Sajedi, Q Huang. Probabilistic prediction model for average bond strength at steel–concrete interface considering corrosion effect. Engineering Structures, 2015, 99: 120–131
30	N Vu-Bac, M Silani, T Lahmer, X Zhuang, T Rabczuk. A unified framework for stochastic predictions of mechanical properties of polymeric nanocomposites. Computational Materials Science, 2015, 96: 520–535

[1]	Jiasheng JIANG, Haifeng YANG, Zhiheng DENG, Yu ZHANG. Damage-constitutive model for seawater coral concrete using different stirrup confinements subjected to axial loading[J]. Front. Struct. Civ. Eng., 2023, 17(3): 429-447.
[2]	Sayed AHMED, Hossam ATEF, Mohamed HUSAIN. Experimental and parametrical investigation of pre-stressed ultrahigh-performance fiber-reinforced concrete railway sleepers[J]. Front. Struct. Civ. Eng., 2023, 17(3): 411-428.
[3]	Reza JAVANMARDI, Behrouz AHMADI-NEDUSHAN. Optimal design of double-layer barrel vaults using genetic and pattern search algorithms and optimized neural network as surrogate model[J]. Front. Struct. Civ. Eng., 2023, 17(3): 378-395.
[4]	Jiang CHEN, Zizhen ZENG, Ying LUO, Feng XIONG, Fei CHENG. Crack detection for wading-concrete structures using water irrigation and electric heating[J]. Front. Struct. Civ. Eng., 2023, 17(3): 368-377.
[5]	Mohamed NOURELDIN, Tabish ALI, Jinkoo KIM. Machine learning-based seismic assessment of framed structures with soil-structure interaction[J]. Front. Struct. Civ. Eng., 2023, 17(2): 205-223.
[6]	Smitha GOPINATH, Ramesh GOPAL, Hemalatha THIYAGARAJAN. Experimental investigation on concrete overlaid with textile reinforced mortar: Influences of mix, temperature, and chemical exposure[J]. Front. Struct. Civ. Eng., 2023, 17(2): 271-283.
[7]	Hai-Van Thi MAI, May Huu NGUYEN, Son Hoang TRINH, Hai-Bang LY. Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting concrete[J]. Front. Struct. Civ. Eng., 2023, 17(2): 284-305.
[8]	Junqi HUANG, Qing JIANG, Xun CHONG, Xianguo YE, Caihua LIU. Structural performance of a façade precast concrete sandwich panel enabled by a bar-type basalt fiber-reinforced polymer connector[J]. Front. Struct. Civ. Eng., 2023, 17(1): 122-137.
[9]	Mohammad SYED, Pinar OKUMUS. Slender reinforced concrete shear walls with high-strength concrete boundary elements[J]. Front. Struct. Civ. Eng., 2023, 17(1): 138-151.
[10]	Xinyu WANG, Jian WU, Xin YIN, Quansheng LIU, Xing HUANG, Yucong PAN, Jihua YANG, Lei HUANG, Shuangping MIAO. QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency[J]. Front. Struct. Civ. Eng., 2023, 17(1): 25-36.
[11]	Yue CHEN, Rong XU, Hao WU, Tao SHENG. A multi-objective design method for seismic retrofitting of existing reinforced concrete frames using pin-supported rocking walls[J]. Front. Struct. Civ. Eng., 2022, 16(9): 1089-1103.
[12]	Demewoz W. MENNA, Aikaterini S. GENIKOMSOU, Mark F. GREEN. Compressive and cyclic flexural response of double-hooked-end steel fiber reinforced concrete[J]. Front. Struct. Civ. Eng., 2022, 16(9): 1104-1126.
[13]	Van Quan TRAN, Van Loi GIAP, Dinh Phien VU, Riya Catherine GEORGE, Lanh Si HO. Application of machine learning technique for predicting and evaluating chloride ingress in concrete[J]. Front. Struct. Civ. Eng., 2022, 16(9): 1153-1169.
[14]	Xin LIU, Dongning SUN, Jinhe LIAO, Zhiwei SHAO, Yunqiang SHI, Siqing ZHANG, Yunlong YAO, Baoning HONG. Quality evaluation of lightweight cellular concrete by an ultrasound-based method[J]. Front. Struct. Civ. Eng., 2022, 16(9): 1170-1182.
[15]	Dung Quang VU, Fazal E. JALAL, Mudassir IQBAL, Dam Duc NGUYEN, Duong Kien TRONG, Indra PRAKASH, Binh Thai PHAM. Novel hybrid models of ANFIS and metaheuristic optimizations (SCE and ABC) for prediction of compressive strength of concrete using rebound hammer field test[J]. Front. Struct. Civ. Eng., 2022, 16(8): 1003-1016.

Viewed

Full text

Abstract

Cited

Shared

Discussed