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http://dx.doi.org/10.12989/gae.2022.31.1.113

Utilizing the GOA-RF hybrid model, predicting the CPT-based pile set-up parameters  

Zhao, Zhilong (Shaanxi Construction of Land Comprehensive Development Co. Ltd)
Chen, Simin (Shaanxi Construction of Land Comprehensive Development Co. Ltd)
Zhang, Dengke (Shaanxi Construction of Land Comprehensive Development Co. Ltd)
Peng, Bin (Shaanxi Construction of Land Comprehensive Development Co. Ltd)
Li, Xuyang (Shaanxi Construction of Land Comprehensive Development Co. Ltd)
Zheng, Qian (Faculty of Civil Engineering, UAE Branch, Islamic Azad University)
Publication Information
Geomechanics and Engineering / v.31, no.1, 2022 , pp. 113-127 More about this Journal
Abstract
The undrained shear strength of soil is considered one of the engineering parameters of utmost significance in geotechnical design methods. In-situ experiments like cone penetration tests (CPT) have been used in the last several years to estimate the undrained shear strength depending on the characteristics of the soil. Nevertheless, the majority of these techniques rely on correlation presumptions, which may lead to uneven accuracy. This research's general aim is to extend a new united soft computing model, which is a combination of random forest (RF) with grasshopper optimization algorithm (GOA) to the pile set-up parameters' better approximation from CPT, based on two different types of data as inputs. Data type 1 contains pile parameters, and data type 2 consists of soil properties. The contribution of this article is that hybrid GOA - RF for the first time, was suggested to forecast the pile set-up parameter from CPT. In order to do this, CPT data and related bore log data were gathered from 70 various locations across Louisiana. With an R2 greater than 0.9098, which denotes the permissible relationship between measured and anticipated values, the results demonstrated that both models perform well in forecasting the set-up parameter. It is comprehensible that, in the training and testing step, the model with data type 2 has finer capability than the model using data type 1, with R2 and RMSE are 0.9272 and 0.0305 for the training step and 0.9182 and 0.0415 for the testing step. All in all, the models' results depict that the A parameter could be forecasted with adequate precision from the CPT data with the usage of hybrid GOA - RF models. However, the RF model with soil features as input parameters results in a finer commentary of pile set-up parameters.
Keywords
cone penetration test; grasshopper optimization algorithm; pile parameters; pile set-up parameter A; random forest model; soil properties;
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1 Mirjalili, S.Z., Mirjalili, S., Saremi, S., Faris, H. and Aljarah, I. (2018), "Grasshopper optimization algorithm for multi-objective optimization problems", Appl. Intell., 48(4), 805-820. https://doi.org/10.1007/s10489-017-1019-8.   DOI
2 Mojumder, M.A.H. (2020), "Evaluation of undrained shear strength of soil, ultimate pile capacity and pile set-up parameter from Cone Penetration Test (CPT) using Artificial Neural Network (ANN)", LSU Master's Theses. 5145. https://digitalcommons.lsu.edu/gradschool_theses/5145.
3 Ng, K.W., Suleiman, M.T. and Sritharan, S. (2013), "Pile setup in cohesive soil. II: Analytical quantifications and design recommendations", J. Geotech. Geoenviron. Eng., 139(2), 210-222. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000753.   DOI
4 Paikowsky, S.G., Regan, J.E. and McDonnell, J.J. (1994), A simplified field method for capacity evaluation of driven piles final report.
5 Poorjafar, A., Esmaeili-Falak, M. and Katebi, H. (2021), "Pile-soil interaction determined by laterally loaded fixed head pile group", Geomech. Eng., 26(1), 13-25. https://doi.org/10.12989/gae.2021.26.1.013.   DOI
6 Esmaeili-Falak, Mahzad, Katebi, H., Vadiati, M. and Adamowski, J. (2019), "Predicting triaxial compressive strength and Young's modulus of frozen sand using artificial intelligence methods", J. Cold Regions Eng., 33(3), 4019007. https://doi.org/10.1061/(ASCE)CR.1943-5495.0000188.   DOI
7 Esmaeili Falak, M. and Sarkhani Benemaran, R. (2022), "Investigating the stress-strain behavior of frozen clay using triaxial test", J. Struct. Constr. Eng.. https://doi.org/10.22065/JSCE.2022.332406.2747.   DOI
8 Sarkhani Benemaran, R., Esmaeili-Falak, M. and Katebi, H. (2020), "Physical and numerical modelling of pile-stabilized saturated layered slopes", Proceedings of the Institution of Civil Engineers - Geotechnical Engineering, 1-50. https://doi.org/10.1680/jgeen.20.00152.   DOI
9 Richardson, B.D. (2011), "A case study on pile relaxation in dilative silts," University of Rhode Island".
10 Saremi, S., Mirjalili, S. and Lewis, A. (2017), "Grasshopper optimisation algorithm: theory and application", Adv. Eng. Softw., 105, 30-47. https://doi.org/10.1016/j.advengsoft.2017.01.004.   DOI
11 Hammerstrom, D. (1993), "Neural networks at work", IEEE Spectrum, 30(6), 26-32.   DOI
12 Liaw, A. and Wiener, M. (2002), "Classification and regression by random forest", R News, 2(3), 18-22.
13 Liu, J., Jiang, Y., Zhang, Y. and Sakaguchi, O. (2021), "Influence of different combinations of measurement while drilling parameters by artificial neural network on estimation of tunnel support patterns", Geomech. Eng., 25(6), 439-453. https://doi.org/10.12989/gae.2021.25.6.439.   DOI
14 Stone, M. (1974), "Cross-validatory choice and assessment of statistical predictions", J. Roy. Stat. Soc.: Series B (Methodological), 36(2), 111-133. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x.   DOI
15 Haque, M.N. (2015), "Field instrumentation and testing to study set-up phenomenon of driven piles and its implementation in LRFD design methodology."
16 Luat, N.V., Lee, K. and Thai, D.K. (2020), "Application of artificial neural networks in settlement prediction of shallow foundations on sandy soils", Geomech. Eng., 20(5), 385-397. https://doi.org/10.12989/gae.2020.20.5.385.   DOI
17 Mafarja, M., Aljarah, I., Heidari, A.A., Hammouri, A.I., Faris, H., Ala'M, A.Z. and Mirjalili, S. (2018), "Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems", Knowledge-Based Syst., 145, 25-45. https://doi.org/10.1016/j.knosys.2017.12.037.   DOI
18 McVay, M.C., Schmertmann, J., Townsend, F. and Bullock, P. (1999), "Pile friction freeze: a field investigation study", Research Report No. WPI 0510632.
19 Ge, D.M., Zhao, L.C. and Esmaeili-Falak, M. (2022), "Estimation of rapid chloride permeability of SCC using hyperparameters optimized random forest models", J. Sustain. Cement-Based Mater., 1-19. https://doi.org/10.1080/21650373.2022.2093291.   DOI
20 Guang-Yu, Z. (1988), "Wave equation applications for piles in soft ground", Proceedings of the 3rd International Conference on the Application of Stress-Wave Theory to Piles. Canada: Ottawa.
21 Haque, M.N., Abu-Farsakh, M.Y., Chen, Q. and Zhang, Z. (2014), "Case study on instrumenting and testing full-scale test piles for evaluating setup phenomenon", T. Res.Record, 2462(1), 37-47. https://doi.org/10.3141/2462-05.   DOI
22 Hoang, N.D., Chen, C.T. and Liao, K.W. (2017), "Prediction of chloride diffusion in cement mortar using multi-gene genetic programming and multivariate adaptive regression splines", Measurement, 112, 141-149. https://doi.org/10.1016/j.measurement.2017.08.031.   DOI
23 ASTM D4643-17. (2017), "Standard test method for determination of water content of soil and rock by microwave oven heating", https://doi.org/10.1520/D4643-17.   DOI
24 Sun, D., Shi, S., Wen, H., Xu, J., Zhou, X. and Wu, J. (2021), "A hybrid optimization method of factor screening predicated on GeoDetector and random forest for landslide susceptibility mapping," Geomorphology, 379, 107623. https://doi.org/10.1016/j.geomorph.2021.107623.   DOI
25 Huo, W., Li, W., Zhang, Z., Sun, C., Zhou, F. and Gong, G. (2021), "Performance prediction of proton-exchange membrane fuel cell based on convolutional neural network and random forest feature selection", Energ. Convers. Manag., 243, 114367. https://doi.org/10.1016/j.enconman.2021.114367.   DOI
26 Hong, H., Pourghasemi, H.R. and Pourtaghi, Z.S. (2016), "Landslide susceptibility assessment in Lianhua County (China): a comparison between a random forest data mining technique and bivariate and multivariate statistical models", Geomorphology, 259, 105-118. https://doi.org/10.1016/j.geomorph.2016.02.012.   DOI
27 ASTM D422-63. (2017), "Standard test method for particle-size analysis of soils", https://doi.org/10.1520/D0422-63R98.   DOI
28 ASTM D4318-00. (2017), "Standard test methods for liquid limit, plastic limit, and plasticity index of soils", https://doi.org/10.1520/D4318-00.   DOI
29 ASTM D7263-21. (2021), "Standard test methods for laboratory determination of density and unit weight of soil specimens", https://doi.org/10.1520/D7263-21.   DOI
30 Talaat, M., Hatata, A.Y., Alsayyari, A.S. and Alblawi, A. (2020), "A smart load management system based on the grasshopper optimization algorithm using the under-frequency load shedding approach", Energy, 190, 116423. https://doi.org/10.1016/j.energy.2019.116423.   DOI
31 Wang, J., Fa, Y., Tian, Y. and Yu, X. (2021), "A machine-learning approach to predict creep properties of Cr-Mo steel with time-temperature parameters", J. Mater. Res. Technol., 13, 635-650. https://doi.org/10.1016/j.jmrt.2021.04.079.   DOI
32 Yang, C., Feng, H. and Esmaeili-Falak, M. (2022), "Predicting the compressive strength of modified recycled aggregate concrete", Structural Concrete.
33 Zhang, P., Yin, Z.Y., Jin, Y.F. and Chan, T.H.T. (2020), "A novel hybrid surrogate intelligent model for creep index prediction based on particle swarm optimization and random forest", Eng. Geol., 265, 105328. https://doi.org/10.1016/j.enggeo.2019.105328.   DOI
34 Bond, A.J. and Jardine, R.J. (1991), "Effects of installing displacement piles in a high OCR clay," Geotechnique, 41(3), 341-363. https://doi.org/10.1680/geot.1991.41.3.341.   DOI
35 Axelsson, G. (1998), "Long-term set-up of driven piles in non-cohesive soils evaluated from dynamic tests on penetration rods", Geotech. Site Character., 895-900.
36 Benemaran, R.S. and Esmaeili-Falak, M. (2020), "Optimization of cost and mechanical properties of concrete with admixtures using MARS and PSO", Comput. Concrete, 26(4), 309-316. https://doi.org/10.12989/cac.2020.26.4.309.   DOI
37 Biau, G., Devroye, L. and Lugosi, G. (2008), "Consistency of random forests and other averaging classifiers", J. Machine Learning Res.h, 9(9).
38 Steward, E.J. and Wang, X. (2011), "Predicting pile setup (freeze): a new approach considering soil aging and pore pressure dissipation", In Geo-Frontiers 2011: Advances in Geotechnical Engineering.
39 Bergahl, U. (1981), "Load tests on friction piles in clay", Proceedings of the 10th Int. Conf. on SMFE.
40 Breiman, L. (2001), "Random forests", Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324.   DOI
41 Stumpf, A. and Kerle, N. (2011), "Object-oriented mapping of landslides using random forests", Remote Sens. Environ., 115(10), 2564-2577. https://doi.org/10.1016/j.rse.2011.05.013.   DOI
42 Qi, C., Chen, Q., Fourie, A. and Zhang, Q. (2018), "An intelligent modelling framework for mechanical properties of cemented paste backfill", Miner. Eng., 123, 16-27. https://doi.org/10.1016/j.mineng.2018.04.010.   DOI
43 Maghsoodi, V., Atermoghaddam, F. and Esmaeili-Falak, M. (2013), "Parametric and two dimensional study of seismic behavior of micro pile group in sandy soil", Intl. Res. J. Appl. Basic. Sci., 6(7), 901-909.
44 Mohammad, L.N., Raghavendra, A., Medeiros, M., Hassan, M. and King, W. "Bill" (2018), "Louisiana transportation research center", Louisiana State University, 70808(225), http://www.ltrc.lsu.edu/downloads.html.
45 Nhu, V.H., Hoang, N.D., Duong, V.B., Vu, H.D. and Bui, D.T. (2020), "A hybrid computational intelligence approach for predicting soil shear strength for urban housing construction: a case study at Vinhomes Imperia project, Hai Phong city (Vietnam)", Eng. with Comput., 36(2), 603-616. https://doi.org/10.1007/s00366-019-00718-z.   DOI
46 Sarkhani Benemaran, R., Esmaeili-Falak, M. and Javadi, A. (2022), "Predicting resilient modulus of flexible pavement foundation using extreme gradient boosting based optimised models", Int. J. Pavement Eng. 1-20. https://doi.org/10.1080/10298436.2022.2095385.   DOI
47 Shahin, M.A., Maier, H.R. and Jaksa, M.B. (2004), "Data division for developing neural networks applied to geotechnical engineering", J. Comput. Civil Eng., 18(2), 105-114.   DOI
48 Skov, R. and Denver, H. (1988), "Time-Dependence of bearing capacity of piles", Proceedings of the 3rd Int. Conf. App. Stress-Wave Theory to Piles.
49 Zhu, W., Huang, L., Mao, L. and Esmaeili-Falak, M. (2022), "Predicting the uniaxial compressive strength of oil palm shell lightweight aggregate concrete using artificial intelligence-based algorithms", Struct. Concrete, https://doi.org/10.1002/suco.202100656   DOI
50 Sun, D., Xu, J., Wen, H. and Wang, D. (2021), "Assessment of landslide susceptibility mapping based on Bayesian hyperparameter optimization: A comparison between logistic regression and random forest", Eng. Geol., 281, 105972. https://doi.org/10.1016/j.enggeo.2020.105972.   DOI
51 Iqbal, M., Zhang, D. and Jalal, F.E. (2021), "Durability evaluation of GFRP rebars in harsh alkaline environment using optimized tree-based random forest model", J. Ocean Eng. Sci., https://doi.org/10.1016/j.joes.2021.10.012.   DOI
52 Bullock, Paul Joseph. (1999), "Pile friction freeze: A field and laboratory study," University of Florida.
53 Camp III, W.M. and Parmar, H.S. (1999), "Characterization of pile capacity with time in the Cooper Marl: study of applicability of a past approach to predict long-term pile capacity", T. Res. Record, 1663(1), 16-24. https://doi.org/10.3141/1663-0.   DOI
54 Bullock, Paul J. (2008), "The easy button for driven pile setup: dynamic testing", From Research to Practice in Geotechnical Engineering, 471-488.
55 Zhou, X., Wen, H., Zhang, Y., Xu, J. and Zhang, W. (2021), "Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization", Geosci. Front., 12(5), 101211. https://doi.org/10.1016/j.gsf.2021.101211.   DOI
56 Sun, D., Wen, H., Wang, D. and Xu, J. (2020), "A random forest model of landslide susceptibility mapping based on hyperparameter optimization using Bayes algorithm", Geomorphology, 362, 107201. https://doi.org/10.1016/j.geomorph.2020.107201.   DOI
57 Johari, A, Habibagahi, G. and Ghahramani, A. (2011), "Prediction of SWCC using artificial intelligent systems: A comparative study", Scientia Iranica, 18(5), 1002-1008. https://doi.org/10.1016/j.scient.2011.09.002.   DOI
58 Johari, A, Javadi, A.A. and Habibagahi, G. (2011), "Modelling the mechanical behaviour of unsaturated soils using a genetic algorithm-based neural network", Comput. Geotech., 38(1), 2-13. https://doi.org/10.1016/j.compgeo.2010.08.011.   DOI
59 Khan, M.M., Ahmad, A.M., Khan, G.M. and Miller, J.F. (2013), "Fast learning neural networks using cartesian genetic programming", Neurocomput., 121, 274-289. https://doi.org/10.1016/j.neucom.2013.04.005.   DOI
60 Komurka, V.E., Wagner, A.B. and Edil, T.B. (2003), Estimating soil/pile set-up. Citeseer.
61 Lee, J., Prezzi, M. and Salgado, R. (2011), "Experimental investigation of the combined load response of model piles driven in sand", Geotech. Test. J., 34(6), 653-667. https://doi.org/10.1520/GTJ103269.   DOI
62 Wang, S.T. and Reese, L.C. (1989), "Predictions of response of piles to axial loading", Predicted and Observed Axial Behavior of Piles: Results of a Pile Prediction Symposium, 173-187.
63 Svinkin, M.R., Morgano, C.M. and Morvant, M. (1994), "Pile capacity as a function of time in clayey and sandy soils", Proceedings of the Deep Foundations Institute Fifth International Conference and Exhibition on Piling and Deep Foundations, 1.
64 Topaz, C.M., Bernoff, A.J., Logan, S. and Toolson, W. (2008), "A model for rolling swarms of locusts", Eur. Phys. J. Spec. Topics, 157(1), 93-109. https://doi.org/10.1140/epjst/e2008-00633-y.   DOI
65 Trigila, A., Iadanza, C., Esposito, C. andScarascia-Mugnozza, G. (2015), "Comparison of logistic regression and random forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy)", Geomorphology, 249, 119-136. https://doi.org/10.1016/j.geomorph.2015.06.001.   DOI
66 Xiang, G., Yin, D., Cao, C. and Yuan, L. (2021), "Application of artificial neural network for prediction of flow ability of soft soil subjected to vibrations", Geomech. Eng., 25(5), 395-403. https://doi.org/10.12989/gae.2021.25.5.395.   DOI
67 Yuan, J., Zhao, M. and Esmaeili-Falak, M. (2022), "A comparative study on predicting the rapid chloride permeability of self-compacting concrete using meta-heuristic algorithm and artificial intelligence techniques", Struct. Concrete, 23(2), 753-774. https://doi.org/10.1002/suco.202100682.   DOI
68 Zakariazadeh, A. (2022), "Smart meter data classification using optimized random forest algorithm", ISA Transactions, 126, 361-369. https://doi.org/10.1016/j.isatra.2021.07.051.   DOI
69 Looney, C.G. (1996), "Advances in feedforward neural networks: demystifying knowledge acquiring black boxes", IEEE T. Knowledge Data Eng., 8(2), 211-226.   DOI
70 Lee, W., Kim, D., Salgado, R. andZaheer, M. (2010), "Setup of driven piles in layered soil", Soils Found., 50(5), 585-598. https://doi.org/10.3208/sandf.50.585.   DOI
71 Johari, Ali, Javadi, A.A. and Najafi, H. (2016), "A genetic-based model to predict maximum lateral displacement of retaining wall in granular soil", Scientia Iranica, 23(1), 54-65. https://doi.org/10.24200/SCI.2016.2097.   DOI
72 Esmaeili-Falak, M. (2013), "Two-dimensional finite element analysis of influence of plasticity on the seismic soil-micropiles-structure interaction", Tech. J. Eng. Appl. Sci., 3(13), 1301-1305.
73 Chen, W., Wang, Y., Cao, G., Chen, G. and Gu, Q. (2014), "A random forest model based classification scheme for neonatal amplitude-integrated EEG", Biomed. Eng. Online, 13(2), 1-13. https://doi.org/10.1186/1475-925X-13-S2-S4.   DOI
74 Chow, F.C., Jardine, R.J., Brucy, F. and Nauroy, J.F. (1998), "Effects of time on capacity of pipe piles in dense marine sand", J. Geotech. Geoenviron. Eng., 124(3), 254-264. https://doi.org/10.1061/(ASCE)1090-0241(1998)124:3(254).   DOI
75 Elias, M.B. (2008), "Numerical simulation of pile installation and setup", 70(1).
76 Esmaeili-Falak, M, Katebi, H. and Javadi, A.A. (2018), "Experimental study of the mechanical behavior of frozen soils - a case study of Tabriz subway", Periodica Polytechnica Civil Eng., 62(1), 117-125. https://doi.org/10.3311/PPci.10960.   DOI
77 Esmaeili-Falak, M., Katebi, H. and Javadi, A.A. (2020), "Effect of freezing on stress-strain characteristics of granular and cohesive soils", J. Cold Regions Eng., 34(2), 05020001. https://doi.org/10.1061/(ASCE)CR.1943-5495.0000205.   DOI
78 Abellan-Garcia, J. andGuzman-Guzman, J.S. (2021), "Random forest-based optimization of UHPFRC under ductility requirements for seismic retrofitting applications", Constr. Build. Mater., 285, 122869. https://doi.org/10.1016/j.conbuildmat.2021.122869.   DOI
79 Abu-Farsakh, M.Y. and Mojumder, M.A.H. (2020), "Exploring artificial neural network to evaluate the undrained shear strength of soil from cone penetration test data", T. Res. Record, 2674(4), 11-22. https://doi.org/10.1177/0361198120912426.   DOI
80 Archer, K.J. and Kimes, R.V. (2008), "Empirical characterization of random forest variable importance measures", Comput. Stat. Data Anal., 52(4), 2249-2260. https://doi.org/10.1016/j.csda.2007.08.015.   DOI
81 Asadi, S., Roshan, S. and Kattan, M.W. (2021), "Random forest swarm optimization-based for heart diseases diagnosis", J. Biomed. Inform., 115, 103690. https://doi.org/10.1016/j.jbi.2021.103690.   DOI
82 ASTM D2850-03. (2017), "Standard test method for unconsolidated-undrained triaxial compression test on cohesive soils", https://doi.org/10.1520/D2850-03.   DOI
83 ASTM D3441-16. (2018), "Standard test method for mechanical cone penetration testing of soils", https://doi.org/10.1520/D3441-16.   DOI
84 Abdelsalam, M., Diab, H.Y. and El-Bary, A.A. (2021), "A metaheuristic harris hawk optimization approach for coordinated control of energy management in distributed generation based microgrids", Appl. Sci., 11(9), 4085. https://doi.org/10.3390/app11094085.   DOI
85 Skov, R. and Denver, H. (1988), "Time-dependence of bearing capacity of piles", Proceedings of the 3rd International Conference on the Application of Stress-Wave Theory to Piles. Ottawa.
86 Schmertmann, J.H. (1991), "The mechanical aging of soils", J. Geotech. Eng., 117(9), 1288-1330.   DOI
87 Shozib, I.A., Ahmad, A., Rahaman, M.S.A., majdi Abdul-Rani, A., Alam, M.A., Beheshti, M. and Taufiqurrahman, I. (2021), "Modelling and optimization of microhardness of electroless Ni-P-TiO2 composite coating based on machine learning approaches and RSM", J. Mater. Res. Technol., 12, 1010-1025. https://doi.org/10.1016/j.jmrt.2021.03.063.   DOI
88 Singh, G., Singh, B. and Kaur, M. (2019), "Grasshopper optimization algorithm-based approach for the optimization of ensemble classifier and feature selection to classify epileptic EEG signals", Med. Biol. Eng. Comput., 57(6), 1323-1339. https://doi.org/10.1007/s11517-019-01951-w.   DOI