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Arabian Journal for Science and Engineering

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06-03-2020 | Research Article-Civil Engineering

Utilization of Support Vector Models and Gene Expression Programming for Soil Strength Modeling

Authors: Ashwini R. Tenpe, Anjan Patel

Published in: Arabian Journal for Science and Engineering | Issue 5/2020

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Abstract

The subgrade strength of roads and highways is based on the California bearing ratio (CBR) value. In this investigation, attempts have been made to overcome the limited boundary condition approach by using advanced methods, support vector machine (SVM) and gene expression programming (GEP) for prediction of CBR value. A large and wide range of datasets of different types of soils have been utilized in the analysis. The grain size distribution, Atterberg’s limits and compaction characteristics of soils have been used as the input variables. Best models with different variables were developed by using GEP and the same were used for SVM analysis. The advantage of SVM over others is that it works on the principle of statistical risk minimization. A comparative study of SVM and GEP models indicates that the SVM has better predictability than GEP. Further, it was found that the five-input variable (including gravel content, sand content, plasticity index, maximum dry density and optimum moisture content) model is the best one to predict the CBR value. The detailed statistical analysis including Pearson coefficient correlation (R) and Error analysis have also been carried out. Based upon the statistical analysis, overfitting ratio of SVM was found to be 0.630 against the value of 1.02 in GEP analysis. Further, sensitivity analysis was carried out and it was found that the CBR value is highly dependent on gravel and sand contents. On the other hand, plastic limit plays an insignificant role in determining the CBR value of soils.

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Purwana, Y.M.; Nikraz, H.; Jitsangiam, P.: Experimental study of suction-monitored cbr test on sand-kaolin clay mixture. Int. J. Geomater. (2012). https://doi.org/10.21660/2012.6.39 CrossRef

Taskiran, T.: Prediction of California bearing ratio (CBR) of fine-grained soils by AI methods. Adv. Eng. Softw. (2010). https://doi.org/10.1016/j.advengsoft.1010.01.003 CrossRef

Yildirim, B.; Gunaydin, O.: Estimation of California bearing ratio by using soft computing systems. Expert Syst. Appl. (2011). https://doi.org/10.1016/j.eswa.2010.12.054 CrossRef

Sukumaran, B.; Kyatham, V.; Shah, A.; Sheth, D.: Suitability of using california bearing ratio test to predict resilient modulus. Airpt. Technol. Transf. Conf. (2002). https://doi.org/10.1016/0266-352X(93)90014-X CrossRef

Patel, S.R.; Desai, M.D.: CBR predicted by index properties for alluvial soils of South Gujarat. Proc. Indian Geotech. Conf. India 16–18, 79–82 (2010)

Kumar, P.K.J.; Patel, H.Y.M.: Soft computing technique for prediction of CBR from index properties of subgrade. J. Innov. Res. Sci. Eng. Technol, Soil Int (2016). https://doi.org/10.15680/IJIRSET.2016.0507214 CrossRef

Anupama, U.; Harini, H.N.: Prediction of CBR value of coarse-grained soils by soft computing techniques. Int. J. Sci. Res. Sci. Eng. Technol. 2(4), 545–550 (2016)

Katte, V.Y.; Mfoyet, S.M.; Manefouet, B.; Wouatong, A.S.L.; Bezeng, A.L.: Correlation of California Bearing Ratio (CBR) value with soil properties of road subgrade soil. Geotech. Geol. Eng. (2018). https://doi.org/10.1007/s10706-018-0604-x CrossRef

Vapnik, V.; Golowich, S.; Smola, A.J.: Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing in Neural Information Processing Systems. MIT Press, Cambridge (1997)

10.

Gunn, S.R.: Support vector machines for classification and regression. Technical Report ISIS-1-98, University of Southampton (1998)

11.

Kayadelen, C.; Gunaydin, O.; Fener, M.; Demir, A.; Ozvan, A.: Modeling of the angle of shearing resistance of soils using soft computing systems. Expert Syst. Appl. (2009). https://doi.org/10.1016/j.eswa.2009.04.008 CrossRef

12.

Park, H.I.; Kweon, G.C.; Lee, S.R.: Prediction of resilient modulus of granular subgrade soils and subbase materials using artificial neural network. Road Mater. Pavement Des. (2009). https://doi.org/10.1080/14680629.2009.9690218 CrossRef

13.

Sinaei, H.; Jumaat, H.Z.; Shariati, M.: Numerical investigation on exterior reinforced concrete beam-column joint strengthened by composite fiber reinforced polymer (CFRP). Int. J. Phys. Sci. (2011). https://doi.org/10.5897/IJPS11.1225 CrossRef

14.

Mohammadhassani, M.; Suhatrila, M.; Shariatib, M.; Ghanbari, F.: Ductility and strength assessment of HSC beams with varying of tensile reinforcement ratios. Eng. Mech, Struct (2013). https://doi.org/10.12989/sem.2013.48.6.833 CrossRef

15.

Toghroli, A.; Mohammadhassani, M.; Shariati, M.; Suhatril, M.; Ibrahim, Z.; Sulong, N.H.R.: Prediction of shear capacity of channel shear connectors using the ANFIS model. Struct, Steel Compos (2014). https://doi.org/10.12989/scs.2014.17.5.000 CrossRef

16.

Mohammadhassani, M.; Saleh, A.M.D.; Suhatril, M.; Safa, M.: Fuzzy modelling approach for shear strength prediction of RC deep beams. Syst, Smart Struct (2015). https://doi.org/10.12989/sss.2015.16.3.497 CrossRef

17.

Safa, M.; Shariati, M.; Ibrahim, Z.; Toghroli, A.; Baharom, S.B.; Nor, N.M.; Petkovic, D.: Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam’s shear strength. Steel Compos. Struct. (2016). https://doi.org/10.12989/scs.2016.21.3.679 CrossRef

18.

Toghroli, A.: Applications of the ANFIS and LR Models in the Prediction of Shear Connection in Composite Beams. University of Malaya, Kuala Lumpur (2016)

19.

Mansouri, I.; Safa, M.; Ibrahim, Z.; Kisi, O.; Tahir, M.M.; Baharom, S.; Azimi, M.: Strength prediction of rotary brace damper using MLR and MARS. Struct. Eng. Mech. (2016). https://doi.org/10.12989/sem.2016.60.3.471 CrossRef

20.

Toghroli, A.; Suhatril, M.; Ibrahim, Z.; Safa, M.; Shariati, M.; Shamshirband, S.: Potential of soft computing approach for evaluating the factors affecting the capacity of steel-concrete composite beam. J. Intell. Manuf. (2016). https://doi.org/10.1007/s10845-016-1217-y CrossRef

21.

Sedghi, Y.; Zandi, Y.; Shariati, M.; Ahmadi, E.; Azar, V.M.; Toghroli, A.; Safa, M.; Mohamad, E.T.; Khorami, M.; Wakil, K.: Application of ANFIS technique on performance of C and L shaped angle shear connectors. Smart Struct. Syst. (2018). https://doi.org/10.12989/sss.2018.22.3.335 CrossRef

22.

Paknahad, M.; Shariati, M.; Sedghi, Y.; Bazzaz, M.; Khorami, M.: Shear capacity equation for channel shear connectors in steel-concrete composite beams. Steel Compos. Struct. (2018). https://doi.org/10.12989/scs.2018.28.4.483 CrossRef

23.

Safa, M.; Wakil, K.; Khorami, M.; Shariati, M.; Toghrali, A.: Application of support vector machine with firefly algorithm for investigation of the factors affecting the shear strength of angle shear connectors. Smart Struct. Syst. 1, 2 (2018). https://doi.org/10.12989/sss.2018.22.4.413 CrossRef

24.

Toghroli, A.; Darvishmoghaddam, E.; Zandi, Y.; Parvan, M.; Safa, M.; Mu’azu, M.A.; Heydari, A.; Wakil, K.; Gebreel, S.A.M.; Khorami, M.: Evaluation of the parameters affecting the Schmidt rebound hammer reading using ANFIS method. Comput. Concr. 1, 2 (2018). https://doi.org/10.12989/cac.2018.21.5.525 CrossRef

25.

Armaghani, D.J.; Hasanipanah, M.; Amnieh, H.B.; Bui, D.T.; Mehrabi, P.; Khorami, M.: Development of a novel hybrid intelligent model for solving engineering problems using GS-GMDH algorithm. Eng. Comput. (2019). https://doi.org/10.1007/s00366-019-00769-2 CrossRef

26.

Shariati, M.; Trung, N.T.; Wakil, K.; Mehrabi, P.; Safa, M.; Khoram, M.: Moment-rotation estimation of steel rack connection using extreme learning machine. Steel Compos. Struct. (2019). https://doi.org/10.12989/scs.2019.31.5.427 CrossRef

27.

Trung, N.T.; Shahgoli, A.F.; Zandi, Y.; Shariati, M.; Wakil, K.; Safa, M.; Khorami, M.: Moment-rotation prediction of precast beam-to-column connections using extreme learning machine. Struct. Eng. Mech. (2019). https://doi.org/10.12989/sem.2019.70.5.639 CrossRef

28.

Mansouri, I.; Shariati, M.; Safa, M.; Ibrahim, Z.; Tahir, M.M.; Petkovic, D.: Analysis of influential factors for predicting the shear strength of a V-shaped angle shear connector in composite beams using an adaptive neuro-fuzzy technique. J. Intell. Manuf. (2019). https://doi.org/10.1007/s10845-017-1306-6 CrossRef

29.

Sari, P.A.; Suhatri, M.; Osman, N.; Mu’azu, M.A.; Dehghani, H.; Sedghi, Y.; Safa, M.; Hasanipanah, M.; Wakil, K.; Khorami, M.; Djuric, S.: An intelligent based-model role to simulate the factor of safe slope by support vector regression. Eng. Comput. (2019). https://doi.org/10.1007/s00366-018-0677-4 CrossRef

30.

Xie, Q.; Sinaei, H.; Shariati, M.; Khorami, M.; Mohamad, E.T.; Bui, D.T.: An experimental study on the effect of CFRP on behavior of reinforce concrete beam column connections. Steel Compos. Struct. (2019). https://doi.org/10.12989/scs.2019.30.5.433 CrossRef

31.

Ferreira, C.: Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst. 13(2), 87–129 (2001)MathSciNetMATH

32.

Oltean, M.; Grosan, C.: A comparison of several linear genetic programming techniques. Complex Syst. 14(4), 285–314 (2003)MathSciNetMATH

33.

Mollahasani, A.; Alavi, A.H.; Gandomi, A.H.: Empirical modeling of plate load test moduli of soil via gene expression programming. Comput. Geotech. (2011). https://doi.org/10.1016/j.compgeo.2010.11.008 CrossRef

34.

Xu, C.; Zhang, X.; Haido, J.H.; Mehrabi, P.; Shariati, A.; Mohamad, E.T.; Nguyen, H.; Wakil, K.: Using genetic algorithms method for the paramount design of reinforced concrete structures. Struct. Eng. Mech. (2019). https://doi.org/10.12989/sem.2019.71.5.503 CrossRef

35.

Dibike, Y.B.; Velickov, S.; Solomatine, D.; Abbott, M.B.: Model induction with support vector machines: introduction and applications. J. Comput. Civ. Eng. 15, 208–216 (2001)CrossRef

36.

Ahmad, A.S.; Hassan, M.Y.; Abdullah, M.P.; Rahman, H.A.; Hussin, F.; Abdullah, H.; Saidur, R.: A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renew. Sustain. Energy Rev. (2014). https://doi.org/10.1016/j.rser.2014.01.069 CrossRef

37.

Nasien, D.; Yuhaniz, S.; Haron, H.: Statistical learning theory and support vector machines. In: Second International Conference on Computer Research and Development, IEEE (2010). http://doi.org/10.1109/ICCRD.2010.183

38.

Putri, E.E.; Rao, N.K.; Mannan, M.A.: Evaluation of modulus of elasticity and modulus of subgrade reaction of soils using CBR test. J. Civ. Eng. Res. (2012). https://doi.org/10.5923/j.jce.20120201.05 CrossRef

39.

Zumrawi, M.: Prediction of CBR from index properties of cohesive soils. In: Chang, S.-Y., AlBahar, S.K., Zhao, J. (eds.) Advances in Civil Engineering and Building Materials. CRC Press, Boca Raton (2012). https://doi.org/10.13140/2.1.4033.6326 CrossRef

40.

Alawi, M.; Rajab, M.: Prediction of California bearing ratio of sub-base layer using multiple linear regression models. Road Mater. Pavement Des. (2013). https://doi.org/10.1080/14680629.2012.757557 CrossRef

41.

Satyanarayana Reddy, C.N.V.; Pavani, K.: Mechanically stabilised soils-regression equation for CBR evaluation. In: Proceedings of Indian Geotechnical Conference, Chennai, India. Dec. 14–16, pp. 731–734 (2006)

42.

Vinod, P.; Reena, C.: Prediction of CBR value of lateritic soils using liquid limit and gradation characteristics data. Highw. Res. J. IRC 1(1), 89–98 (2008)

43.

Venkatasubramanian, C.; Dhinakaran, G.: ANN model for predicting CBR from index properties of soils. Int. J. Civ. Struct. Eng. 2(2), 605–611 (2011)

44.

Patel, M.A.; Patel, H.S.: Laboratory assessment to correlate strength parameter from physical properties of subgrade. In: Procedia Engineering (2013). Non-Circuit Branches of the 3rd Nirma University International Conference on Engineering http://doi.org/10.1016/j.proeng.2013.01.029

45.

Ramasubbarao, G.V.; Siva Sankar, G.: Predicting soaked CBR value of fine-grained soils using index and compaction characteristics. Jordan J. Civ. Eng. 7(3), 354–360 (2013)

46.

Saxena, A.K.; Jain, P.K.; Jain, R.: Application of machine learning techniques to predict soaked CBR of remolded soils. Int. J. Eng. Res. Technol. (IJERT) 2(6), 3019–3024 (2013)

47.

Talukdar, D.K.: A study of correlation between California Bearing Ratio (CBR) values with other properties of soil. Int. J. Emerg. Technol. Adv. Eng. 4(1), 559–562 (2014)

48.

Prashanth Kumar, K.S.; Nanduri, R.K.; Kumar, N.D.: Validation of Predicted California bearing ratio values from different correlations. Am. J. Eng. Res. (AJER) 3(8), 344–352 (2014)

49.

Rakaraddi, P.G.; Gomarsi, V.: Establishing relationship between CBR with different soil properties. Int. J. Res. Eng. Technol. 4(2), 182–188 (2015)CrossRef

50.

Sabat, A.K.: Prediction of California bearing ratio of a stabilized expansive soil using artificial neural network and support vector machine. Electron. J. Geotech. Eng. 20(3), 981–991 (2015)

51.

Araujo, W.; Ruiz, G.: Correlation equations of CBR with index properties of soil in the City of Piura. In: 14th LACCEI International Multi-Conference for Engineering, Education, and Technology: Engineering Innovations for Global Sustainability, San Jose, Costa Rica 20–22 July (2016). http://doi.org/10.18687/LACCEI2016.1.1.029

52.

Rehman, Z.U.; Khalid, U.; Farooq, K.; Mujtaba, H.: Prediction of CBR value from index properties of different soils. Tech. J. Univ. Eng. Technol. (UET) Taxila Pak. 22(II), 17–26 (2017)

53.

Farias, I.G.; Araujo, W.; Ruiz, G.: Prediction of California bearing ratio from index properties of soils using parametric and non-parametric models. Geotech. Geol. Eng. (2018). https://doi.org/10.1007/s10706-018-0548-1 CrossRef

54.

Tenpe, A.; Patel, A.: Application of genetic expression programming and artificial neural network for prediction of CBR. Road Mater. Pavement Des. (2018). https://doi.org/10.1080/14680629.2018.1544924 CrossRef

55.

Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)MATH

56.

Koza, J.R.: Genetic Programming on the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)MATH

57.

Simpson, A.R.; Priest, S.D.: The application of genetic algorithms to optimisation problems in geotechnics. Comput. Geotech. 15(1), 1–19 (1993)CrossRef

58.

Pal, S.; Wathugala, G.W.; Kundu, S.: Calibration of a constitutive model using genetic algorithms. Comput. Geotech. (1996). https://doi.org/10.1016/S0266-352X(96)00006-7 CrossRef

59.

McCombie, P.; Wilkinson, P.: The use of the simple genetic algorithm in finding the critical factor of safety in slope stability analysis. Comput. Geotech. (2002). https://doi.org/10.1016/S0266-352X(02)00027-7 CrossRef

60.

Yang, C.; ThamFeng, X.T.; Wang, Y.J.; Lee, P.K.K.: Two-stepped evolutionary algorithm and its application to stability analysis of slopes. J. Comput. Civ. Eng. 18(2), 145 (2004). https://doi.org/10.1061/(ASCE)0887-3801(2004) CrossRef

61.

Javadi, A.A.; Rezania, M.; Nezhad, M.M.: Evaluation of liquefaction induced lateral displacements using genetic programming. Comput. Geotech. (2006). https://doi.org/10.1016/j.compgeo.2006.05.001 CrossRef

62.

Johari, A.; Habibagahi, G.; Ghahramani, A.: Prediction of soil–water characteristic curve using genetic programming. J. Geotech. Geoenviron. Eng. 132(1), 661 (2006). https://doi.org/10.1061/(ASCE)1090-0241(2006) CrossRef

63.

Alavi, A.H.; Heshmati, A.A.R.; Gandomi, A.H.; Askarinejad, A.; Mirjalili, M.: Utilisation of computational intelligence techniques for stabilised soil. In: Engineering Computational Technology, Civil-Comp Press, Edinburgh (2008)

64.

Alavi, A.H.; Gandomi, A.H.: Energy-based numerical models for assessment of soil liquefaction. Geosci. Front. (2012). https://doi.org/10.1016/j.gsf.2011.12.008 CrossRef

65.

Cevik, A.; Cabalar, A.F.: Modelling damping ratio and shear modulus of sand–mica mixtures using genetic programming. Expert Syst. Appl. (2009). https://doi.org/10.1016/j.eswa.2008.09.010 CrossRef

66.

Cabalar, A.F.; Cevik, A.: Genetic programming-based attenuation relationship: an application of recent earthquakes in turkey. Comput. Geosci. (2009). https://doi.org/10.1016/j.cageo.2008.10.015 CrossRef

67.

Baykasoglu, A.; Gullu, H.; Canakci, H.; Ozbakir, L.: Prediction of compressive and tensile strength of limestone via genetic programming. Expert Syst. Appl. (2008). https://doi.org/10.1016/j.eswa.2007.06.006 CrossRef

68.

Onen, F.: Prediction of scour at a side-weir with GEP, ANN and regression models. Arabian Journal for Science and Engineering (2013). https://doi.org/10.1007/s13369-014-1244-y CrossRef

69.

Gandomi, A.H.; Alavi, A.H.; Mirzahosseini, M.R.; Nejad, F.M.: Nonlinear genetic-based for prediction of flow number of asphalt mixtures. J. Mater. Civ. Eng. (2011). https://doi.org/10.1061/(ASCE)MT.1943-5533.0000154 CrossRef

70.

Teodorescu, L.; Sherwood, D.: High energy physics event selection with gene expression programming. Comput. Phys. Commun. (2008). https://doi.org/10.1016/j.cpc.2007.10.003 CrossRef

71.

Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)CrossRef

72.

Elbisy, M.S.: Support vector machine and regression analysis to predict the field hydraulic conductivity of sandy soil. KSCE J. Civ. Eng. (2015). https://doi.org/10.1007/s12205-015-0210-x CrossRef

73.

Cortes, C.; Vapnik, V.: Support vector machine. Mach. Learn. 20(3), 273–297 (1995)MATH

74.

Vapnik, V.: Statistical learning theory. Weley, New York (1998)MATH

75.

Li, X.Z.; Kong, J.M.: Application of GA–SVM method with parameter optimization for landslide development prediction. Nat. Hazards Earth Syst. Sci. (2014). https://doi.org/10.5194/nhess-14-525-2014 CrossRef

76.

Cawley, G.C.; Talbot, N.L.C.: Fast exact leave-one-out cross-validation of sparse least-squares support vector machines. Neural Netw. (2004). https://doi.org/10.1016/j.neunet.2004.07.002 CrossRefMATH

77.

Samui, P.: Slope stability analysis: a support vector machine approach. Environ. Geol. 56, 255–267 (2008)CrossRef

78.

Oltean, M.; Dumitrescu, D.: Multi expression programming. Technical Report (2002)

79.

Erzin, Y.; Turkoz, D.: Use of neural networks for the prediction of the CBR value of some Aegean sands. Neural Comput. Appl. (2016). https://doi.org/10.1007/s00521-015-1943-7 CrossRef

80.

Goyal, P.; Chowdhary, S.; Agnihotri, K.: Modelling of angle of shearing resistance using support vector machines. Int. J. Eng. Res. Technol. IJERT 3(3), 2478–2481 (2014)

Title: Utilization of Support Vector Models and Gene Expression Programming for Soil Strength Modeling
Authors: Ashwini R. Tenpe
Anjan Patel
Publication date: 06-03-2020
Publisher: Springer Berlin Heidelberg
Published in: Arabian Journal for Science and Engineering / Issue 5/2020
Print ISSN: 2193-567X
Electronic ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-020-04441-6

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