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 Author(s): 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

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