New Applications of a Supervised Computational Intelligence (CI) Approach: Case Study in Civil Engineering

Jebur, Ameer A.; Al-Jumeily, Dhiya; Aljanabi, Khalid R.; Khaddar, Rafid M. Al; Atherton, William; Alattar, Zeinab I.; Majeed, Adel H.; Mustafina, Jamila

doi:10.1007/978-3-030-22475-2_8

Ameer A. Jebur^5,6,
Dhiya Al-Jumeily⁷,
Khalid R. Aljanabi⁸,
Rafid M. Al Khaddar⁵,
William Atherton⁵,
Zeinab I. Alattar⁶,
Adel H. Majeed⁶ &
…
Jamila Mustafina⁹

Part of the book series: Unsupervised and Semi-Supervised Learning ((UNSESUL))

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1 Citations

Abstract

In this study, artificial neural network (ANN) techniques are used in an attempt to predict the nonlinear hyperbolic soil stress–strain relationship parameters (k and R _f). Two ANN models are developed and trained to achieve the planned target, in an attempt at making the experimental test (unconsolidated undrained triaxial test) unnecessary. The first is logarithm of modulus number (log k), and the second is failure ratio (R _f). A database of laboratory measurements comprises a total of (83) case records for modulus number (k) and failure ratio (R _f). Four parameters are considered to have the most significant impact on the nonlinear soil stress–strain relationship parameters, which are used as an independent input variables (IIVs) to the developed the proposed ANNs models. These comprise of: Plasticity index (PI), Dry unit weight (γ _dry), Water content (ω _o), and Confining stress (σ ₃), the output models are respectively, (log k), and (R _f). Multilayer perceptron trainings using back-propagation algorithm are used in this work. The effect of a number of issues in relation to ANN construction such as ANN geometry and internal parameters on the performance of ANN models is investigated. Information on the relative importance of the factors affecting the (log k), and (R _f) is presented, and practical equations for their prediction are proposed.

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References

Abad, S., Yilmaz, M., Armaghani, D. J., & Tugrul, A. (2018). Prediction of the durability of limestone aggregates using computational techniques. Neural Computing and Applications, 29, 423–433.
Article Google Scholar
Douma, O. B., Boukhatem, B., Ghrici, M., & Tagnit-Hamou, A. (2017). Prediction of properties of self-compacting concrete containing fly ash using artificial neural network. Neural Computing and Applications, 28, S707–S718.
Article Google Scholar
Shirazi, A. Z., & Mohammadi, Z. (2017). A hybrid intelligent model combining ANN and imperialist competitive algorithm for prediction of corrosion rate in 3C steel under seawater environment. Neural Computing and Applications, 28, 3455–3464.
Article Google Scholar
Millie, D. F., Weckman, G. R., Young II, W. A., Ivey, J. E., Carrick, H. J., & Fahnenstiel, G. L. (2012). Modeling microalgal abundance with artificial neural networks: Demonstration of a heuristic ‘grey-box’ to deconvolve and quantify environmental influences. Environmental Modelling and Software, 38, 27–39.
Article Google Scholar
Jebur, A. A., Atherton, W., Al Khaddar, R. M., & Loffill, E. (2018b). Settlement prediction of model piles embedded in sandy soil using the Levenberg–Marquardt (LM) training algorithm. Geotechnical and Geological Engineering, 36(5), 2893–2906.
Article Google Scholar
Tarawneh, B. (2017). Predicting standard penetration test N-value from cone penetration test data using artificial neural networks. Geoscience Frontiers, 8(1), 199–204.
Article MathSciNet Google Scholar
Moayedi, H., & Rezaei, A. (2017). An artificial neural network approach for under-reamed piles subjected to uplift forces in dry sand. Neural Computing and Applications. https://doi.org/10.1007/s00521-017-2990-z
Article Google Scholar
Singh, G., & Walia, B. S. (2017). Performance evaluation of nature-inspired algorithms for the design of bored pile foundation by artificial neural networks. Neural Computing and Applications, 28, 289–298.
Article Google Scholar
Alizadeh, B., Najjari, S., & Kadkhodaie-Ilkhchi, A. (2012). Artificial neural network modeling and cluster analysis for organic facies and burial history estimation using well log data: A case study of the South Pars Gas Field, Persian Gulf, Iran. Computers and Geosciences, 45, 261–269.
Article Google Scholar
Mohammed, M. A., Ghani, M. K. A., & Hamed, R. I. (2017). Analysis of an electronic methods for nasopharyngeal carcinoma: Prevalence, diagnosis, challenges and technologies. Journal of Computational Science, 21, 241–254.
Article Google Scholar
Tarawneh, B. (2013). Pipe pile setup: Database and prediction model using artificial neural network. Soils and Foundations, 53(4), 607–615.
Article Google Scholar
Yadav, A. K., Malik, H., & Chandel, S. S. (2014). Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models. Renewable and Sustainable Energy Reviews, 31, 509–519.
Article Google Scholar
Feng, Y., Barr, W., & Harper, W. F. (2013). Neural network processing of microbial fuel cell signals for the identification of chemicals present in water. Journal of Environmental Management, 120, 84–92.
Article Google Scholar
Jaeel, A. J., Al-wared, A. I., & Ismail, Z. Z. (2016). Prediction of sustainable electricity generation in microbial fuel cell by neural network: Effect of anode angle with respect to flow direction. Journal of Electroanalytical Chemistry, 767, 56–62.
Article Google Scholar
Nguyen-Truong, H. T., & Le, H. M. (2015). An implementation of the Levenberg–Marquardt algorithm for simultaneous-energy-gradient fitting using two-layer feed forward neural networks. Chemical Physics Letters, 629, 40–45.
Article Google Scholar
Kriesel, D. (2011). Brief introduction to neural networks [Online]. University of Bonn, Germany. Retrieved July 20, 2018, from http://www.dkriesel.com/_media/science/neuronalenetze-en-zeta2-2col-dkrieselcom.pdf.
Jebur, A. A., Atherton, W., & Al Khaddar, R. M. (2018a). Feasibility of an evolutionary artificial intelligence (AI) scheme for modelling of load settlement response of concrete piles embedded in cohesionless soil. Ships and Offshore Structures, 13(7), 705–718.
Article Google Scholar
Chokshi, P., Dashwood, R., & Hughes, D. J. (2017). Artificial Neural Network (ANN) based microstructural prediction model for 22MnB5 boron steel during tailored hot stamping. Computers and Structures, 190, 162–172.
Article Google Scholar
Caudill, M. (1988). Neural networks primer. Part III. AI Expert, 3(6), 53–59.
Google Scholar
Ismail, A., & Jeng, D. S. (2011). Modelling load settlement behaviour of piles using High-Order Neural Network (HON-PILE model). Engineering Application of Artificial Intelligence, 24(5), 813–821.
Article Google Scholar
Al-Janabi, K. R. (2006). Laboratory leaching process modeling in gypseous soils using Artificial Neural Networks (ANNs). PhD thesis, Building and Construction Engineering Department, University of Technology, Iraq.
Google Scholar
Garson, G. D. (1991). Interpreting neural-network connection weights. AI Expert, 6, 46–51.
Google Scholar
Wong, K. S., & Duncan, J. M. (1974). Hyperbolic stress–strain parameters for nonlinear finite element analyses of stresses and movements in soil masses. La Jolla, CA: College of Engineering, Office of Research Services, University of California.
Google Scholar
Abdellatif, M. E. M. (2013). Modelling the impact of climate change on urban drainage system. PhD thesis, Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool, UK.
Google Scholar
Jebur, A. A., Atherton, W., Alkhadar, R. M., & Loffill, E. (2017). Piles in sandy soil: A numerical study and experimental validation. Procedia Engineering, 196, 60–67.
Article Google Scholar
Boscardin, M. D., Selig, E. T., Lin, R.-S., & Yang, G.-R. (1990). Hyperbolic parameters for compacted soils. Journal of Geotechnical Engineering, 116, 88–104.
Article Google Scholar

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Acknowledgments

The authors would like to acknowledge the Iraqi Ministry of Higher Education and Scientific Research and Wasit University for the grant provided to carry out this research under the grant agreement number 162575, dated 28/05/2013, with the Liverpool John Moores University, university reference number (744221).

Author information

Authors and Affiliations

Department of Civil Engineering, Liverpool John Moores University, Liverpool, UK
Ameer A. Jebur, Rafid M. Al Khaddar & William Atherton
Department of Civil Engineering, Al-Mustansiriya University, Baghdad, Iraq
Ameer A. Jebur, Zeinab I. Alattar & Adel H. Majeed
Department of Computer Science, Liverpool John Moores University, Liverpool, UK
Dhiya Al-Jumeily
Department of Civil Engineering, University of Anbar, Ramadi, Iraq
Khalid R. Aljanabi
Kazan Federal University, Kazan, Russia
Jamila Mustafina

Authors

Ameer A. Jebur
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Dhiya Al-Jumeily
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Khalid R. Aljanabi
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Rafid M. Al Khaddar
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William Atherton
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Zeinab I. Alattar
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Adel H. Majeed
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Jamila Mustafina
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Correspondence to Ameer A. Jebur .

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Editors and Affiliations

Department of Electrical Engineering and Computer Science, University of Tennessee at Knoxville, Knoxville, TN, USA
Michael W. Berry
Faculty of Computer & Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
Azlinah Mohamed
Advanced Analytics Engineering Centre, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
Bee Wah Yap

Appendices

Appendix 1

Case no.	Reference	Unified soil classif.	Cohesion (kN/m²)	Friction angle	PI (%)	Dry unit weight γ _d (kN/m³)	Water content ω _c (%)	Confining stress (kN/m²)	log k	n	R _f
1	Wong and Duncan [23]	ML	193.68	19	4	17.4	15.6	860.672	2.301	0.59	0.86
2	Wong and Duncan [23]	ML	41.964	30	4	17.5	12.7	860.672	1.4314	1.43	0.72
3	Wong and Duncan [23]	ML	45.192	31	1	16.65	11.6	349.648	2.3802	0.31	0.83
4	Wong and Duncan [23]	ML	20.444	31	1	16.65	13.6	403.44	2.4314	0.38	0.82
5	Wong and Duncan [23]	ML	58.104	27	1	16.65	16.6	403.44	2	0.84	0.77
6	Wong and Duncan [23]	CL	57.028	29	20	17.4	16.7	715.433	2.415	0.6	0.87
7	Wong and Duncan [23]	CL	129.12	14	20	17.13	19.5	494.886	1.5911	0.48	0.58
8	Wong and Duncan [23]	CL	102.22	0	23	17.15	19.1	193.651	1.8195	0	0.75
9	Wong and Duncan [23]	CL	45.192	0	23	16.65	21.2	193.651	1	0.03	0.52
10	Wong and Duncan [23]	CL	107.6	31	22	16.33	21.7	193.651	1.5563	0	0.57
11	Wong and Duncan [23]	CL	98.992	17	16	16.87	11.5	215.168	2.8129	−0.68	0.9
12	Wong and Duncan [23]	CL	161.4	6	16	17.46	14.3	376.544	2.8261	−0.14	0.93
13	Wong and Duncan [23]	CL	139.88	24	16	17.45	16.8	376.544	2.6335	0.1	0.93
14	Wong and Duncan [23]	CL	193.68	13	16	18	11.5	376.544	3.3802	−0.74	0.92
15	Wong and Duncan [23]	CL	204.44	32	16	18.36	14.5	376.544	3.301	−0.3	0.97
16	Wong and Duncan [23]	CL	161.4	18	16	17.41	8.71	376.544	3.9494	−1.1	0.94
17	Wong and Duncan [23]	CL	139.88	29	16	19.1	11.7	376.544	3.699	−0.28	0.95
18	Wong and Duncan [23]	CL	68.864	25	15	16.81	12.5	403.44	2.5051	−0.21	0.8
19	Wong and Duncan [23]	CL	53.8	2	15	16.81	14.5	349.648	2.2788	0.02	0.81
20	Wong and Duncan [23]	CL	107.6	1	30	17.13	17.2	349.648	1.8692	0.23	0.87
21	Wong and Duncan [23]	CL	107.6	1	30	16.36	17	349.648	1.8325	−0.05	0.84
22	Wong and Duncan [23]	CL	48.42	25	30	16.42	20	349.648	1.4314	0.18	0.85
23	Wong and Duncan [23]	CL	61.332	4	16	17.33	14.6	349.648	2.5051	0.29	0.85
24	Wong and Duncan [23]	CL	161.4	3	32	15.54	23.2	349.648	2.301	0.29	0.89
25	Wong and Duncan [23]	CL	129.12	1	32	14.68	23.3	403.44	2	0.18	0.86
26	Wong and Duncan [23]	CL	68.864	22	32	14.53	26.7	349.648	1.7243	0.14	0.9
27	Wong and Duncan [23]	CL	90.384	22	16	17.9	15.1	349.648	2.2041	0.34	0.79
28	Wong and Duncan [23]	CL	59.18	28	16	17	15	349.648	2.4624	0.27	0.91
29	Wong and Duncan [23]	CL	83.928	25	12	16	13.5	349.648	2.8325	−0.36	0.84
30	Wong and Duncan [23]	CL	161.4	6	12	17	13.3	349.648	2.7782	0.18	0.68
31	Wong and Duncan [23]	CL	79.624	18	12	16.42	19.3	349.648	1.3617	0.32	0.61
32	Wong and Duncan [23]	CL	97.916	20	12	17	16.7	349.648	2.4472	0.6	0.93
33	Wong and Duncan [23]	CL	71.016	8	12	16.25	16.3	349.648	2.3424	0.23	0.9
34	Wong and Duncan [23]	CL	139.88	13	25	16.8	18.6	349.648	2.1461	0.2	0.84
35	Wong and Duncan [23]	CL	107.6	2	25	16.31	17.1	349.648	2.0792	0.09	0.83
36	Wong and Duncan [23]	CL	86.08	24	25	16.5	19.7	349.648	1.6721	0.33	0.82
37	Wong and Duncan [23]	CL	161.4	8	25	17	13.9	349.648	2.9777	−0.15	0.9
38	Wong and Duncan [23]	CL	161.4	4	25	17.33	16.9	349.648	2.6721	0	0.95
39	Wong and Duncan [23]	CL	72.092	23	23	15.8	20.8	349.648	1.8751	0.44	0.88
40	Wong and Duncan [23]	CL	193.68	12	23	16.8	14.8	349.648	2.9243	−0.19	0.84
41	Wong and Duncan [23]	CL	129.12	29	23	16.2	17.4	349.648	2.4314	0.6	0.87
42	Wong and Duncan [23]	CL	150.64	13	23	16	14.2	349.648	3.0414	−0.36	0.83
43	Wong and Duncan [23]	CL	150.64	2	23	16.71	17.5	349.648	2.6128	0.15	0.87
44	Wong and Duncan [23]	CL	82.852	1	27	15.68	24	322.752	1.7559	0.43	0.86
45	Wong and Duncan [23]	CL	104.372	2	18	15.96	22.9	215.168	2.0414	0.43	0.9
46	Wong and Duncan [23]	CL	118.36	1	20	15.86	22.7	430.336	2	0.27	0.89
47	Wong and Duncan [23]	CL	106.524	3	24	15.7	23.9	430.336	2.2041	0.54	0.97
48	Wong and Duncan [23]	CL	118.36	2	24	15.83	22.7	430.336	2.1139	0.46	0.91
49	Wong and Duncan [23]	CL	83.928	0	24	15.5	22.7	430.336	1.7243	0.41	0.85
50	Wong and Duncan [23]	CL	129.12	0	26	15.62	23.4	860.672	2.3802	0	0.95
51	Wong and Duncan [23]	CL	102.22	12	24	17.19	18.1	860.672	2.2041	0	0.93
52	Wong and Duncan [23]	CL	172.16	20	18	18.38	12.2	403.44	2.1761	0.16	0.79
53	Wong and Duncan [23]	CL	215.2	20	20	17.75	13	823	2.6435	0.17	0.85
54	Wong and Duncan [23]	CL	269	16	19	18.54	13.1	823	2.6435	0.34	0.86
55	Wong and Duncan [23]	CL	107.6	11	19	18	16.2	392.681	2.0414	0.94	0.91
56	Wong and Duncan [23]	CL	150.64	9	19	17.96	16.6	274.339	1.8261	0.71	0.77
57	Wong and Duncan [23]	CL	107.6	3	19	17.65	17.3	279.718	1.5682	0.37	0.65
58	Wong and Duncan [23]	CL	236.72	4	19	17	16.2	946.739	1.8513	1.06	0.98
59	Wong and Duncan [23]	CL	65.636	0	19	14.4	28.8	215.168	1.9638	0.21	0.89
60	Wong and Duncan [23]	CL	39.812	1	38	15.73	31.1	193.651	1.3222	0	0.65
61	Wong and Duncan [23]	CL	54.876	1	36	14.77	28.6	193.651	1.8261	0.02	0.79
62	Wong and Duncan [23]	CL	67.788	0	45	10.63	26.5	215.168	1.8129	0.14	0.77
63	Wong and Duncan [23]	CL	129.12	2	36	14.45	27.4	860.672	1.5563	0.72	0.91
64	Wong and Duncan [23]	SC	279.76	26	36	14.52	24.4	860.672	1.716	0.66	0.89
65	Wong and Duncan [23]	SC	193.68	4	11	19.72	9.6	1452.38	3.5911	−0.08	0.93
66	Wong and Duncan [23]	CL	98.992	31	18	20.17	8.3	107.584	2.7076	0.37	0.64
67	Wong and Duncan [23]	CL	161.4	17	16	16.87	11.5	215.168	2.8129	−0.68	0.9
68	Wong and Duncan [23]	CL	139.88	6	16	17.46	14.3	376.544	2.8261	−0.14	0.93
69	Wong and Duncan [23]	CL	193.68	24	16	17.45	16.8	376.544	2.6335	0.1	0.93
70	Wong and Duncan [23]	CL	204.44	13	16	18.04	11.5	376.544	3.3802	−0.74	0.92
71	Wong and Duncan [23]	CL	161.4	32	16	18.36	14.5	215.168	3.301	−0.3	0.97
72	Wong and Duncan [23]	CL	139.88	18	16	17.41	8.71	376.544	3.9494	−1.1	0.94
73	Wong and Duncan [23]	CL	68.864	29	16	19	11.7	376.544	3.699	−0.28	0.95
74	Boscardin [26]	ML	28	34	4	18.05	12.1	207.5	2.6435	0.4	0.95
75	Boscardin [26]	ML	24	32	4	17.1	12.1	207.5	2.301	0.26	0.89
76	Boscardin [26]	ML	21	30	4	16.15	12.1	207.5	2.0414	0.25	0.85
77	Boscardin [26]	ML	17	28	4	15.2	12.1	207.5	1.8751	0.25	0.8
78	Boscardin [26]	ML	0	23	4	11.59	12.1	207.5	1.2041	0.95	0.55
79	Boscardin [26]	CL	62	13	15	15.67	21	207.5	2.0792	0.45	1
80	Boscardin [26]	CL	48	15	15	14.85	21	207.5	1.8751	0.54	0.94
81	Boscardin [26]	CL	41	14	15	14.02	21	207.5	1.699	0.6	0.9
82	Boscardin [26]	CL	35	13	15	13.2	21	207.5	1.5441	0.66	0.87
83	Boscardin [26]	CL	0	19	15	10.06	21	207.5	1.2041	0.95	0.75

Appendix 2

	Input variables				Output variables
Case no.	PI	Dry unit weight (kN/m³)	Water content ω _o (%)	Confining stress (kN/m²)	R _f	n	log k
1	4	17.4	15.6	860.672	0.86	0.59	2.30103
2	4	17.5	12.7	860.672	0.72	1.43	1.43136
3	1	16.65	11.6	349.648	0.83	0.31	2.38021
4	1	16.65	13.6	403.44	0.82	0.38	2.43136
5	1	16.65	16.6	403.44	0.77	0.84	2
6	20	17.4	16.7	715.433	0.87	0.6	2.41497
7	20	17.13	19.5	494.886	0.58	0.48	1.59106
8	23	17.15	19.1	193.651	0.75	0	1.81954
9	23	16.65	21.2	193.651	0.52	0.03	1
10	22	16.33	21.7	193.651	0.57	0	1.5563
11	16	16.87	11.5	215.168	0.9	−0.68	2.81291
12	16	17.46	14.3	376.544	0.93	−0.14	2.82607
13	16	17.45	16.8	376.544	0.93	0.1	2.63347
14	16	18	11.5	376.544	0.92	−0.74	3.38021
15	16	18.36	14.5	376.544	0.97	−0.3	3.30103
16	16	17.41	8.71	376.544	0.94	−1.1	3.94939
17	16	19.1	11.7	376.544	0.95	−0.28	3.69897
18	15	16.81	12.5	403.44	0.8	−0.21	2.50515
19	15	16.81	14.5	349.648	0.81	0.02	2.27875
20	30	17.13	17.2	349.648	0.87	0.23	1.86923
21	30	16.36	17	349.648	0.84	−0.05	1.83251
22	30	16.42	20	349.648	0.85	0.18	1.43136
23	16	17.33	14.6	349.648	0.85	0.29	2.50515
24	32	15.54	23.2	349.648	0.89	0.29	2.30103
25	32	14.68	23.3	403.44	0.86	0.18	2
26	32	14.53	26.7	349.648	0.9	0.14	1.72428
27	16	17.9	15.1	349.648	0.79	0.34	2.20412
28	16	17	15	349.648	0.91	0.27	2.4624
29	12	16	13.5	349.648	0.84	−0.36	2.83251
30	12	17	13.3	349.648	0.68	0.18	2.77815
31	12	16.42	19.3	349.648	0.61	0.32	1.36173
32	12	17	16.7	349.648	0.93	0.6	2.44716
33	12	16.25	16.3	349.648	0.9	0.23	2.34242
34	25	16.8	18.6	349.648	0.84	0.2	2.14613
35	25	16.31	17.1	349.648	0.83	0.09	2.07918
36	25	16.5	19.7	349.648	0.82	0.33	1.6721
37	25	17	13.9	349.648	0.9	−0.15	2.97772
38	25	17.33	16.9	349.648	0.95	0	2.6721
39	23	15.8	20.8	349.648	0.88	0.44	1.87506
40	23	16.8	14.8	349.648	0.84	−0.19	2.92428
41	23	16.2	17.4	349.648	0.87	0.6	2.43136
42	23	16	14.2	349.648	0.83	−0.36	3.04139
43	23	16.71	17.5	349.648	0.87	0.15	2.61278
44	27	15.68	24	322.752	0.86	0.43	1.75587
45	18	15.96	22.9	215.168	0.9	0.43	2.04139
46	20	15.86	22.7	430.336	0.89	0.27	2
47	24	15.7	23.9	430.336	0.97	0.54	2.20412
48	24	15.83	22.7	430.336	0.91	0.46	2.11394
49	24	15.5	22.7	430.336	0.85	0.41	1.72428
50	26	15.62	23.4	860.672	0.95	0	2.38021
51	24	17.19	18.1	860.672	0.93	0	2.20412
52	18	18.38	12.2	403.44	0.79	0.16	2.17609
53	20	17.75	13	823	0.85	0.17	2.64345
54	19	18.54	13.1	823	0.86	0.34	2.64345
55	19	18	16.2	392.681	0.91	0.94	2.04139
56	19	17.96	16.6	274.339	0.77	0.71	1.82607
57	19	17.65	17.3	279.718	0.65	0.37	1.5682
58	19	17	16.2	946.739	0.98	1.06	1.85126
59	19	14.4	28.8	215.168	0.89	0.21	1.96379
60	38	15.73	31.1	193.651	0.65	0	1.32222
61	36	14.77	28.6	193.651	0.79	0.02	1.82607
62	45	10.63	26.5	215.168	0.77	0.14	1.81291
63	36	14.45	27.4	860.672	0.91	0.72	1.5563
64	36	14.52	24.4	860.672	0.89	0.66	1.716
65	11	19.72	9.6	1452.38	0.93	−0.08	3.59106
66	18	20.17	8.3	107.584	0.64	0.37	2.70757
67	16	16.87	11.5	215.168	0.9	−0.68	2.81291
68	16	17.46	14.3	376.544	0.93	−0.14	2.82607
69	16	17.45	16.8	376.544	0.93	0.1	2.63347
70	16	18.04	11.5	376.544	0.92	−0.74	3.38021
71	16	18.36	14.5	215.168	0.97	−0.3	3.30103
72	16	17.41	8.71	376.544	0.94	−1.1	3.94939
73	16	19	11.7	376.544	0.95	−0.28	3.69897
74	4	18.05	12.1	207.5	0.95	0.4	2.64345
75	4	17.1	12.1	207.5	0.89	0.26	2.30103
76	4	16.15	12.1	207.5	0.85	0.25	2.04139
77	4	15.2	12.1	207.5	0.8	0.25	1.87506
78	4	11.59	12.1	207.5	0.55	0.95	1.20412
79	15	15.67	21	207.5	1	0.45	2.07918
80	15	14.85	21	207.5	0.94	0.54	1.87506
81	15	14.02	21	207.5	0.9	0.6	1.69897
82	15	13.2	21	207.5	0.87	0.66	1.54407
83	15	10.06	21	207.5	0.75	0.95	1.20412

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Jebur, A.A. et al. (2020). New Applications of a Supervised Computational Intelligence (CI) Approach: Case Study in Civil Engineering. In: Berry, M., Mohamed, A., Yap, B. (eds) Supervised and Unsupervised Learning for Data Science . Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-22475-2_8

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DOI: https://doi.org/10.1007/978-3-030-22475-2_8
Published: 05 September 2019
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22474-5
Online ISBN: 978-3-030-22475-2
eBook Packages: EngineeringEngineering (R0)

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