Geomechanics and Engineering

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

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)
  • Received : 2021.10.03
  • Accepted : 2022.10.04
  • Published : 2022.10.10
⟨ Previous Next ⟩

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

References

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. ASTM D2850-03. (2017), "Standard test method for unconsolidated-undrained triaxial compression test on cohesive soils", https://doi.org/10.1520/D2850-03.
  7. ASTM D3441-16. (2018), "Standard test method for mechanical cone penetration testing of soils", https://doi.org/10.1520/D3441-16.
  8. ASTM D422-63. (2017), "Standard test method for particle-size analysis of soils", https://doi.org/10.1520/D0422-63R98.
  9. ASTM D4318-00. (2017), "Standard test methods for liquid limit, plastic limit, and plasticity index of soils", https://doi.org/10.1520/D4318-00.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. Bergahl, U. (1981), "Load tests on friction piles in clay", Proceedings of the 10th Int. Conf. on SMFE.
  15. Biau, G., Devroye, L. and Lugosi, G. (2008), "Consistency of random forests and other averaging classifiers", J. Machine Learning Res.h, 9(9).
  16. 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.
  17. Breiman, L. (2001), "Random forests", Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324.
  18. Bullock, Paul J. (2008), "The easy button for driven pile setup: dynamic testing", From Research to Practice in Geotechnical Engineering, 471-488.
  19. Bullock, Paul Joseph. (1999), "Pile friction freeze: A field and laboratory study," University of Florida.
  20. 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.
  21. 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.
  22. 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).
  23. Elias, M.B. (2008), "Numerical simulation of pile installation and setup", 70(1).
  24. 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.
  25. 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.
  26. 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.
  27. 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.
  28. 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.
  29. 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.
  30. 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.
  31. Hammerstrom, D. (1993), "Neural networks at work", IEEE Spectrum, 30(6), 26-32. https://doi.org/10.1109/6.214579
  32. 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.
  33. Haque, M.N. (2015), "Field instrumentation and testing to study set-up phenomenon of driven piles and its implementation in LRFD design methodology."
  34. 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.
  35. 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.
  36. 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.
  37. 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.
  38. 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.
  39. 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.
  40. 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.
  41. 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.
  42. Komurka, V.E., Wagner, A.B. and Edil, T.B. (2003), Estimating soil/pile set-up. Citeseer.
  43. 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.
  44. 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.
  45. Liaw, A. and Wiener, M. (2002), "Classification and regression by random forest", R News, 2(3), 18-22.
  46. 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.
  47. Looney, C.G. (1996), "Advances in feedforward neural networks: demystifying knowledge acquiring black boxes", IEEE T. Knowledge Data Eng., 8(2), 211-226. https://doi.org/10.1109/69.494162
  48. 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.
  49. 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.
  50. 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.
  51. McVay, M.C., Schmertmann, J., Townsend, F. and Bullock, P. (1999), "Pile friction freeze: a field investigation study", Research Report No. WPI 0510632.
  52. 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.
  53. 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.
  54. 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.
  55. 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.
  56. 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.
  57. Paikowsky, S.G., Regan, J.E. and McDonnell, J.J. (1994), A simplified field method for capacity evaluation of driven piles final report.
  58. 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.
  59. 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.
  60. Richardson, B.D. (2011), "A case study on pile relaxation in dilative silts," University of Rhode Island".
  61. 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.
  62. 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.
  63. 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.
  64. Schmertmann, J.H. (1991), "The mechanical aging of soils", J. Geotech. Eng., 117(9), 1288-1330. https://doi.org/10.1061/(ASCE)0733-9410(1991)117:9(1288)
  65. 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. https://doi.org/10.1061/(ASCE)0887-3801(2004)18:2(105)
  66. 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.
  67. 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.
  68. 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.
  69. 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.
  70. 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.
  71. 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.
  72. 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.
  73. 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.
  74. 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.
  75. 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.
  76. 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.
  77. 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.
  78. 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.
  79. 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.
  80. 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.
  81. 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.
  82. 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.
  83. Yang, C., Feng, H. and Esmaeili-Falak, M. (2022), "Predicting the compressive strength of modified recycled aggregate concrete", Structural Concrete.
  84. 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.
  85. 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.
  86. 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.
  87. 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.
  88. 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