Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network

doi:10.1007/s11709-017-0445-3

Front. Struct. Civ. Eng.

2018, Vol. 12

Issue (4) : 490-503 https://doi.org/10.1007/s11709-017-0445-3

RESEARCH ARTICLE

Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network

T. Chandra Sekhara REDDY(

)

Civil Engineering, G.P.R. Engineering College, Kurnool 518002, Andhra Pradesh, India

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Abstract

This paper is aimed at adapting Artificial Neural Networks (ANN) to predict the strength properties of SIFCON containing different minerals admixture. The investigations were done on 84 SIFCON mixes, and specimens were cast and tested after 28 days curing. The obtained experimental data are trained using ANN which consists of 4 input parameters like Percentage of fiber (PF), Aspect Ratio (AR), Type of admixture (TA) and Percentage of admixture (PA). The corresponding output parameters are compressive strength, tensile strength and flexural strength. The predicted values obtained using ANN show a good correlation between the experimental data. The performance of the 4-14-3 architecture was better than other architectures. It is concluded that ANN is a highly powerful tool suitable for assessing the strength characteristics of SIFCON.

Keywords artificial neural networks root mean square error SIFCON silica fume metakaolin steel fiber

Corresponding Authors: T. Chandra Sekhara REDDY

Online First Date: 26 December 2017 Issue Date: 20 November 2018

Cite this article:

T. Chandra Sekhara REDDY. Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network[J]. Front. Struct. Civ. Eng., 2018, 12(4): 490-503.

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http://journal.hep.com.cn/fsce/EN/10.1007/s11709-017-0445-3
http://journal.hep.com.cn/fsce/EN/Y2018/V12/I4/490

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Fig.1 Methodology of development of the Artificial Neural Network (ANN)

INPUT					OUTPUT
Sl No.	% fibre	Aspect ratio	Mineral admixture	%Mineral admixture	Compressive strength (MPa)	Tensile strength (MPa)	Flexural strength (MPa)
1	6	40	1	0	46.44	8	18.6
2	8	40	1	0	48.02	8.6	22.6
3	12	40	1	0	51	9.82	31.82
4	6	50	1	0	48	9.28	21.2
5	8	50	1	0	49.4	9.75	25.37
6	10	50	1	0	50.98	12	30.6
7	12	50	1	0	52.86	12.6	34.58
8	6	60	1	0	51.56	10.8	24.1
9	8	60	1	0	53.03	11.6	29.26
10	10	60	1	0	54.35	12.41	37.28
11	6	40	2	10	51.6	8.91	21.2
12	8	40	2	10	54.2	10.21	25.31
13	12	40	2	10	59.08	12.36	37.18
14	6	50	2	10	54.95	9.85	22.6
15	8	50	2	10	57.8	10.75	27.9
16	12	50	2	10	64.32	13.32	42.56
17	6	60	2	10	62.6	11.2	25.8
18	8	60	2	10	65.3	12.45	33.25
19	12	60	2	10	76.93	14.7	46.55
20	6	40	3	10	51.39	8.98	24.04
21	8	40	3	10	55.98	10.6	28.03
22	10	40	3	10	59.55	11.94	33.35
23	6	50	3	10	55.9	10	25.27
24	8	50	3	10	59.3	10.95	31.82
25	10	50	3	10	62.82	12.45	40
26	12	50	3	10	66.4	13.9	45.2
27	6	60	3	10	63.1	11.2	28.03
28	8	60	3	10	67.66	12.74	37.24
29	10	60	3	10	73.07	13.88	46.6
30	12	60	3	10	78.2	15.2	50.64
31	8	40	1	20	52.88	9.96	24.76
32	10	40	1	20	54.22	11.21	29.33
33	12	40	1	20	56.88	12.46	36.26
34	8	50	1	20	55.55	10.72	27.2
35	10	50	1	20	57.77	11.74	35.06
36	12	50	1	20	61.77	12.99	41.6
37	6	60	1	20	60.44	10.89	24.66
38	8	60	1	20	63.11	12.17	32.53
39	10	60	3	20	69.33	13.27	40.13
40	6	40	3	20	51.11	8.86	23.33
41	10	40	3	20	58.22	11.46	32.4
42	12	40	3	20	61.33	12.56	38.93
43	6	50	3	20	54.22	9.68	24.66
44	8	50	3	20	57.77	10.81	30.93
45	10	50	3	20	61.23	12.2	39.06
46	12	50	3	20	64.88	13.58	44.86
47	6	60	3	20	61.33	11.06	26.93
48	8	60	3	20	65.77	12.45	36.13
49	10	60	3	20	71.11	13.72	44.8
50	10	40	1	30	49.77	9.99	26.4
51	12	40	1	30	52.88	11.21	33.6
52	6	50	1	30	49.77	8.86	20.34
53	8	50	1	30	51.11	9.65	25.2
54	10	50	1	30	53.77	10.78	32.4
55	12	50	1	30	57.77	11.96	38.4
56	6	60	1	30	56	10.04	22.66
57	10	60	1	30	64	12.2	37.2
58	12	60	1	30	68.44	13.23	42
59	6	40	2	30	47.11	8.05	21.6
60	8	40	2	30	50.22	8.61	25.06
61	10	40	2	30	53.33	10.55	30
62	12	40	2	30	56.44	11.54	36
63	6	50	2	30	50.22	8.9	22.66
64	8	50	2	30	52.88	9.93	28.8
65	10	50	2	30	56.44	11.18	35.86
66	6	60	2	30	56.44	10	24.93
67	8	60	2	30	59.55	11.43	33.46
68	10	60	2	30	64.88	12.59	41.87

Tab.1 Part of the training parameters

Tab.2 Part of the testing parameters

Tab.3 Scaling factors for input parameters

Node ?No.	Output parameters	Min. value (MPa)	Max. value (MPa)	Mean	Scaling factor
1	Compressive Strength ( $F c$ )	44.88	78.20	57.8784	100
2	Tensile Strength ( $F t$ )	8.00	15.20	11.2197	20
3	Flexural Strength ( $F f$ )	18.60	50.64	31.375	80

Tab.4 Scaling factors for output parameters

Network	Architecture	AF for hidden layer	AF for output layer	CW	R²
					Compressive strength		Tensile strength		Flexural strength
					Training	Testing	Training	Testing	Training	Testing
N1	4-5-3	tansig(n)	purelin (n)	43	0.9952	0.9948	0.9939	0.9967	0.9910	0.9855
N2	4-6-3	tansig(n)	purelin (n)	51	0.9963	0.9953	0.9942	0.9970	0.9916	0.9868
N3	4-7-3	tansig(n)	purelin (n)	59	0.9969	0.9957	0.9947	0.9976	0.9920	0.9874
N4	4-8-3	tansig(n)	purelin (n)	67	0.9975	0.9960	0.9952	0.9980	0.9927	0.9879
N5	4-9-3	tansig(n)	purelin (n)	75	0.9984	0.9963	0.9969	0.9986	0.9934	0.9883
N6	4-10-3	tansig(n)	purelin (n)	83	0.9973	0.9961	0.9975	0.9990	0.9939	0.9897
N7	4-11-3	tansig(n)	purelin (n)	91	0.9982	0.9964	0.9982	0.9996	0.9942	0.9902
N8	4-12-3	tansig(n)	purelin (n)	99	0.9986	0.9967	0.9988	0.9999	0.9946	0.9907
N9	4-13-3	tansig(n)	purelin (n)	107	0.9990	0.9970	0.9991	0.99101	0.9950	0.9911
N10	4-14-3	tansig(n)	purelin (n)	115	0.9992	0.9974	0.9996	0.99107	0.9958	0.9914
N11	4-15-3	tansig(n)	purelin (n)	123	0.9991	0.9981	0.9998	0.99115	0.9961	0.9922
N12	4-5-5-3	tansig(n)	purelin (n)	73	0.9987	0.9987	0.9994	0.99121	0.9969	0.9929
N13	4-5-6-3	tansig(n)	purelin (n)	82	0.9996	0.9998	0.9997	0.99129	0.9974	0.9935
N14	4-5-7-3	tansig(n)	purelin (n)	91	0.9981	0.9999	0.9992	0.99135	0.9980	0.9943
N15	4-5-8-3	tansig(n)	purelin (n)	100	0.9979	0.9987	0.9987	0.99142	0.9989	0.9957
N16	4-6-5-3	tansig(n)	purelin (n)	83	0.9960	0.9985	0.9984	0.99149	0.9991	0.9962
N17	4-6-6-3	tansig(n)	purelin (n)	93	0.9955	0.9983	0.9982	0.99155	0.9994	0.9970
N18	4-6-7-3	tansig(n)	purelin (n)	103	0.9954	0.9982	0.9980	0.99158	0.9998	0.9973
N19	4-6-8-3	tansig(n)	purelin (n)	113	0.9951	0.9970	0.9978	0.99164	0.9989	0.9977
N20	4-7-5-3	tansig(n)	purelin (n)	93	0.9956	0.9975	0.9974	0.99167	0.9984	0.9980
N21	4-7-6-3	tansig(n)	purelin (n)	104	0.9980	0.9974	0.9960	0.99170	0.9980	0.9983
N22	4-7-7-3	tansig(n)	purelin (n)	115	0.9989	0.9970	0.9962	0.99175	0.9978	0.9985
N23	4-7-8-3	tansig(n)	purelin (n)	126	0.9991	0.9967	0.9964	0.99179	0.9975	0.9989
N24	4-8-5-3	tansig(n)	purelin (n)	103	0.9993	0.9964	0.9960	0.99185	0.9970	0.9990
N25	4-8-6-3	tansig(n)	purelin (n)	115	0.9998	0.9950	0.9958	0.99189	0.9968	0.9992
N26	4-8-7-3	tansig(n)	purelin (n)	127	0.9997	0.9958	0.9955	0.99190	0.9965	0.9994
N27	4-8-8-3	tansig(n)	purelin (n)	139	0.9992	0.9954	0.9955	0.99191	0.9963	0.9995

Tab.5 Comparison between specifications of different architectures

Fig.2 Architecture of neural network model

Fig.3 (a) Relationship between actual and predicted for compressive strength; (b) Relationship between actual and predicted for tensile strength; (c) Relationship between actual and predicted for flexural strength

Fig.4 (a) Relationship between actual and predicted for compressive strength; (b) Relationship between actual and predicted for tensile strength; (c) Relationship between actual and predicted for flexural strength

Tab.6 Comparison of learning algorithms

Tab.7 The MAPE and RMSE statistics of comparison

Fig.5 (a) Learning progress of the network 4-14-3; (b) Testing progress of the network 4-14-3

Tab.8 Learning progress of the network 4-14-3

Fig.6 Learning for compressive strength

Fig.7 Learning for tensile strength

Fig.8 Learning for flexural strength

Fig.9 Testing for compressive strength

Fig.10 Testing for tensile strength

Fig.11 Testing for flexural strength

1	Lankard D R. Properties application slurry infiltrated fiber concrete (SIFCON). Concrete International, 1984, 6: 44–47
2	Tayfur G, Erdem T, Kırca Ö. Strength Prediction of High-Strength Concrete by Fuzzy Logic and Artificial Neural Networks. Journal of Materials in Civil Engineering, 2014, 26(11): 04014079 https://doi.org/10.1061/(ASCE)MT.1943-5533.0000985
3	Topçu I B, Saridemir M. Prediction of rubberized mortar properties using artificial neural network and fuzzy logic. Journal of Materials Processing Technology, 2008, 199(1-3): 108–118 https://doi.org/10.1016/j.jmatprotec.2007.08.042
4	Zhang X, Wang H, Wang D, Li C. Prediction of Concrete Strength based on Self organizing Fuzzy Neural Network. In: Proceeding of the 11th World Congress on Intelligent Control and Automation Shenyang, China, June-July, 2014
5	Abdalla A J, Hawileh R, Al-Tamimi A. Prediction of FRP-concrete ultimate bond strength using Artificial Neural Network. In: International Conference on Modeling, Simulation and Applied Optimization (ICMSAO), Kuala Lumpur, April, 2011
6	Amani J, Moeini R. 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 https://doi.org/10.1016/j.scient.2012.02.009
7	Ghaboussi J, Garrett J H Jr, Wu X. Knowledge-Based Modeling of Material Behavior with Neural Networks. Journal of Engineering Mechanics, 1991, 117(1): 132–153 https://doi.org/10.1061/(ASCE)0733-9399(1991)117:1(132)
8	Mukherjee A, Schemauder S, Ruhle M. Artificial neural network for the prediction of the mechanical behaviour of metal matrix composite. Acta Metallurgica et Materialia, 1995, 43(11): 4083–4091 https://doi.org/10.1016/0956-7151(95)00076-8
9	Chopra P, Sharma R K, Kumar M. Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming. Advances in Materials Science and Engineering, 2016, (2): 1-10
10	Akkurt S, Tayfur G, Can S. Fuzzy logic model for the prediction of cement compressive strength. Cement and Concrete Research, 2004, 34(8): 1429–1433 https://doi.org/10.1016/j.cemconres.2004.01.020
11	Demir A. Prediction of Hybrid fibre-added concrete strength using artificial neural networks. Computers and Concrete, 2015, 15(4): 503–514 https://doi.org/10.12989/cac.2015.15.4.503
12	Hamdia K M, Lahmer T, Nguyen-Thoi T, Rabczuk T. Predicting the fracture toughness of PNCs: A stochastic approach based on ANN and ANFIS. Computational Materials Science, 2015, 102: 304–313 https://doi.org/10.1016/j.commatsci.2015.02.045
13	Bal L, Buyle-Bodin F. Artificial neural network for predicting drying shrinkage of concrete. Construction & Building Materials, 2013, 38: 248–254 https://doi.org/10.1016/j.conbuildmat.2012.08.043
14	Başyigit C, Akkurt I, Kilincarslan S, Beycioglu A. Prediction of compressive strength of heavyweight concrete by ANN and FL models. Neural Computation, 2010, 19(4): 507–513 https://doi.org/10.1007/s00521-009-0292-9
15	Dias W P S, Pooliyadda S P. Neural networks for predicting properties of concretes with admixtures. Construction & Building Materials, 2001, 15(7): 371–379 https://doi.org/10.1016/S0950-0618(01)00006-X
16	Adeli H. Neural networks in civil engineering: 1989-2000. Comput-Aided. Civ. Inf., 2001, 16: 126–142
17	Aiyer B G, Kim D, Karingattikkal N, Samui P, Rao P R.Prediction of Compressive Strength of Self- Compacting Concrete using Least Square Support Vector Machine and Relevance Vector Machine. KSCE Journal of Civil Engineering, 2014, 18(6): 1753–1758 https://doi.org/10.1007/s12205-014-0524-0
18	Boukhatem B, Kenai S, Hamou A T, Ziou D, Ghrici M. Predicting concrete properties using neural networks (NN) with principal component analysis (PCA) technique. Computers and Concrete, 2012, 10(6): 557–573 https://doi.org/10.12989/cac.2012.10.6.557
19	Alexandridis A, Triantis D, Stavrakas I, Stergiopoulos C. A neural network approach for compressive strength prediction in cement-based materials through the study of pressure-stimulated electrical signals. Construction & Building Materials, 2012, 30: 294–300 https://doi.org/10.1016/j.conbuildmat.2011.11.036
20	Muhammad K, Mohammad N, Rehman F. Modeling shotcrete mix design using artificial neural network. Computers and Concrete, 2015, 15(2): 167–181 https://doi.org/10.12989/cac.2015.15.2.167
21	Bilim C, Atis C D, Tanyildizi H, Karahan O. Predicting the compressive strength of ground granulated blast furnace slag concrete using artiﬁcial neural network. Advances in Engineering Software, 2009, 40(5): 334–340 https://doi.org/10.1016/j.advengsoft.2008.05.005
22	Erdem H. Prediction of the moment capacity of reinforced concrete slabs in fire using artificial neural networks. Advances in Engineering Software, 2010, 41(2): 270–276 https://doi.org/10.1016/j.advengsoft.2009.07.006
23	Erdal H I, Karakurt O, Namli E. High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform. Engineering Applications of Artificial Intelligence, 2013, 26(4): 1246–1254 https://doi.org/10.1016/j.engappai.2012.10.014
24	Cheng M, Firdausi P M, Prayogo D. High-performance concrete compressive strength prediction using Genetic Weighted Pyramid Operation Tree (GWPOT). Engineering Applications of Artificial Intelligence, 2014, 29: 104–113 https://doi.org/10.1016/j.engappai.2013.11.014
25	Ghafari E, Bandarabadi M, Costa H, Júlio E. Prediction of Fresh and Hardened State Properties of UHPC: Comparative Study of Statistical Mixture Design and an Artificial Neural Network Model. Journal of Materials in Civil Engineering, 2015, 27(11): 04015017 https://doi.org/10.1061/(ASCE)MT.1943-5533.0001270
26	Gupta S. Using Artificial Neural Network to Predict the Compressive Strength of Concrete containing Nano-silica. Civil Engineering and Architecture, 2013, 1: 96–102
27	Hush D R, Horne B G. Progress in supervised Neural Network: What is New since Lippman. IEEE Signal Processing Magazine, 1993, 10: 8–39 https://doi.org/10.1109/79.180705
28	Najigivi A, Khaloo A. A. Iraji zad, and S.A. Rashid, An Artiﬁcial Neural Networks Model for Predicting Permeability Properties of Nano Silica–Rice Husk Ash Ternary Blended Concrete. IJCSM, 2013, 7: 225–238
29	Pham A, Hoang N, Nguyen Q. Predicting Compressive Strength of High-Performance Concrete Using Metaheuristic-Optimized Least Squares Support Vector Regression. Journal of Computing in Civil Engineering, 2016, 30(3): 06015002 https://doi.org/10.1061/(ASCE)CP.1943-5487.0000506
30	A.Sincero. Predicting Mixing Power Using Artificial Neural Network. In: Proceedings of World Water & Environmental Resources Congress, Philadelphia, Pennsylvania, United States, June, 2003
31	Słoński M. A comparison of model selection methods for compressive strength prediction of high performance concrete using neural networks. Computers & Structures, 2010, 88(21-22): 1248–1253 https://doi.org/10.1016/j.compstruc.2010.07.003
32	Sarıdemir M. Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic. Advances in Engineering Software, 2009, 40(9): 920–927 https://doi.org/10.1016/j.advengsoft.2008.12.008
33	Yeh I C. Analysis of Strength of Concrete Using Design of Experiments and Neural Networks. Journal of Materials in Civil Engineering, 2006, 18(4): 597–604 https://doi.org/10.1061/(ASCE)0899-1561(2006)18:4(597)
34	Yeh I C. Design of High-Performance Concrete Mixture Using Neural Networks and Nonlinear Programming. Journal of Computing in Civil Engineering, 1999, 13(1): 36–42 https://doi.org/10.1061/(ASCE)0887-3801(1999)13:1(36)
35	Hou T H, Su C H, Chang H Z. Using neural networks and immune algorithms to find the optimal parameters for an IC wire bonding process. Expert Systems with Applications, 2008, 34(1): 427–436 https://doi.org/10.1016/j.eswa.2006.09.024
36	Kostić S, Vasović D. Prediction model for compressive strength of basic concrete mixture using artificial neural networks. Neural Computing & Applications, 2015, 26(5): 1005–1024 https://doi.org/10.1007/s00521-014-1763-1
37	Lee S C. Prediction of concrete strength using artificial neural networks. Engineering Structures, 2003, 25(7): 849–857 https://doi.org/10.1016/S0141-0296(03)00004-X
38	Öztaş A, Pala M, Özbay E, Kanca E, Çagˇlar N, Bhatti M A. Predicting the compressive strength and slump of high strength concrete using neural network. Construction & Building Materials, 2006, 20(9): 769–775 https://doi.org/10.1016/j.conbuildmat.2005.01.054
39	Parichatprecha R, Nimityongskul P. Analysis of durability of high performance concrete using artiﬁcial neural networks. Construction & Building Materials, 2009, 23(2): 910–917 https://doi.org/10.1016/j.conbuildmat.2008.04.015
40	Morova N, Karahancer S, Terzi S, Serin S. Modeling Marshall Stability of Light Asphalt Concretes Fabricated using Expanded Clay Aggregate with Artificial Neural Networks. International Symposium on Innovations in Intelligent Systems and Applications, Turkey, 2012
41	Vu-Bac N, Lahmer T, Zhuang X, Rabczuk T. A software framework for probabilistic sensitivity analysis for computationally expensive models. Advances in Engineering Software, 2016, 100: 19–31 https://doi.org/10.1016/j.advengsoft.2016.06.005

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