Geomechanics and Engineering

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Study of oversampling algorithms for soil classifications by field velocity resistivity probe

  • Lee, Jong-Sub (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Park, Junghee (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Kim, Jongchan (Department of Civil and Environmental Engineering, University of California at Berkeley) ;
  • Yoon, Hyung-Koo (Department of Construction and Disaster Prevention Engineering, Daejeon University)
  • Received : 2022.01.13
  • Accepted : 2022.07.05
  • Published : 2022.08.10
⟨ Previous Next ⟩

Abstract

A field velocity resistivity probe (FVRP) can measure compressional waves, shear waves and electrical resistivity in boreholes. The objective of this study is to perform the soil classification through a machine learning technique through elastic wave velocity and electrical resistivity measured by FVRP. Field and laboratory tests are performed, and the measured values are used as input variables to classify silt sand, sand, silty clay, and clay-sand mixture layers. The accuracy of k-nearest neighbors (KNN), naive Bayes (NB), random forest (RF), and support vector machine (SVM), selected to perform classification and optimize the hyperparameters, is evaluated. The accuracies are calculated as 0.76, 0.91, 0.94, and 0.88 for KNN, NB, RF, and SVM algorithms, respectively. To increase the amount of data at each soil layer, the synthetic minority oversampling technique (SMOTE) and conditional tabular generative adversarial network (CTGAN) are applied to overcome imbalance in the dataset. The CTGAN provides improved accuracy in the KNN, NB, RF and SVM algorithms. The results demonstrate that the measured values by FVRP can classify soil layers through three kinds of data with machine learning algorithms.

Keywords

Acknowledgement

This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 21CTAP-C164152-01).

References

  1. Abraham, S., Huynh, C. and Vu, H. (2020), "Classification of soils into hydrologic groups using machine learning", Data, 5(1), 2. https://doi.org/10.3390/data5010002.
  2. Al-Bared, M.A., Harahap, I.S., Marto, A., Abad, S.V.A.N.K. and Ali, M.O. (2019), "Undrained shear strength and microstructural characterization of treated soft soil with recycled materials", Geomech. Eng., 18(4), 427-437. https://doi.org/10.12989/gae.2019.18.4.427.
  3. Bai, X.D., Cheng, W.C., Ong, D.E. and Li, G. (2021), "Evaluation of geological conditions and clogging of tunneling using machine learning", Geomech. Eng., 25(1), 59-73. https://doi.org/10.12989/gae.2021.25.1.059.
  4. Byun, Y.H., Hong, W.T. and Yoon, H.K. (2019), "Characterization of cementation factor of unconsolidated granular materials through time domain reflectometry with variable saturated conditions", Mater., 12(8), 1340. https://doi.org/10.3390/ma12081340.
  5. Chen, Y., Irfan, M., Uchimura, T., Cheng, G. and Nie, W. (2018), "Elastic wave velocity monitoring as an emerging technique for rainfall-induced landslide prediction", Landslid., 15(6), 1155-1172. https://doi.org/10.1007/s10346-017-0943-3.
  6. Colmenares, J., Davila, J., Vega, J. and Shin, J. (2018), "Tunnelling on terrace soil deposits: Characterization and experiences on the Bogota-Villavicencio road", Geomech. Eng., 15(3), 899-910. https://doi.org/10.12989/gae.2018.15.3.899.
  7. Engelmann, J. and Lessmann, S. (2021), "Conditional Wasserstein GAN-based oversampling of tabular data for imbalanced learning", Exp. Syst. Appl., 174, 114582. https://doi.org/10.1016/j.eswa.2021.114582.
  8. Feng, X., Li, S., Yuan, C., Zeng, P. and Sun, Y. (2018), "Prediction of slope stability using naive Bayes classifier", KSCE J. Civil Eng., 22(3), 941-950. https://doi.org/10.1007/s12205-018-1337-3.
  9. Fereidooni, D. (2018), "Assessing the effects of mineral content and porosity on ultrasonic wave velocity", Geomech. Eng., 14(4), 399-406. https://doi.org/10.12989/gae.2018.14.4.399.
  10. Forte, G., Chioccarelli, E., De Falco, M., Cito, P., Santo, A. and Iervolino, I. (2019), "Seismic soil classification of Italy based on surface geology and shear-wave velocity measurements", Soil Dyn. Earthq. Eng., 122, 79-93. https://doi.org/10.1016/j.soildyn.2019.04.002.
  11. Gambill, D.R., Wall, W.A., Fulton, A.J. and Howard, H.R. (2016), "Predicting USCS soil classification from soil property variables using Random Forest", J. Terramech., 65, 85-92. https://doi.org/10.1016/j.jterra.2016.03.006.
  12. Heung, B., Ho, H.C., Zhang, J., Knudby, A., Bulmer, C.E. and Schmidt, M.G. (2016), "An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping", Geoderma, 265, 62-77. https://doi.org/10.1016/j.geoderma.2015.11.014.
  13. Kovacevic, M., Bajat, B. and Gajic, B. (2010), "Soil type classification and estimation of soil properties using support vector machines", Geoderma, 154(3-4), 340-347. https://doi.org/10.1016/j.geoderma.2009.11.005.
  14. Lee, J.S. and Yoon, H.K. (2015), "Theoretical relationship between elastic wave velocity and electrical resistivity", J. Appl. Geophys., 116, 51-61. https://doi.org/10.1016/j.jappgeo.2015.02.025.
  15. Lee, J.S. and Yoon, H.K. (2018), "Application example: Field Velocity Resistivity Probe (FVRP) for predicting pore pressure parameter B", Soil Dyn. Earthq. Eng., 107, 214-217. https://doi.org/10.1016/j.soildyn.2018.01.039
  16. Lee, J.S., Byun, Y.H. and Yoon, H.K. (2017), "Study of Activation Energy in Soil through Elastic Wave Velocity and Electrical Resistivity", Vadose Zone J., 16(6), 1-9. https://doi.org/10.2136/vzj2016.08.0073
  17. Lee, S.J. and Yoon, H.K. (2021), "Discontinuity predictions of porosity and hydraulic conductivity based on electrical resistivity in slopes through deep learning algorithms", Sensor., 21(4), 1412. https://doi.org/10.3390/s21041412.
  18. Liu, L.L., Yang, C. and Wang, X.M. (2020), "Landslide susceptibility assessment using feature selection-based machine learning models", Geomech. Eng., 25, 1-16. https://doi.org/10.12989/gae.2021.25.1.001.
  19. Luzi, L., Puglia, R., Pacor, F., Gallipoli, M.R., Bindi, D. and Mucciarelli, M. (2011), "Proposal for a soil classification based on parameters alternative or complementary to Vs 30", Bull. Earthq. Eng., 9(6), 1877-1898. https://doi.org/10.1007/s10518-011-9274-2.
  20. Min, D.H. and Yoon, H.K. (2021), "Suggestion for a new deterministic model coupled with machine learning techniques for landslide susceptibility mapping", Sci. Rep., 11(1), 1-24. https://doi.org/10.1038/s41598-021-86137-x.
  21. Puri, A. and Gupta, M.K. (2021), "Knowledge discovery from noisy imbalanced and incomplete binary class data", Exp. Syst. Appl., 181, 115179. https://doi.org/10.1016/j.eswa.2021.115179.
  22. Raizada, R.D. and Lee, Y.S. (2013), "Smoothness without smoothing: why Gaussian naive Bayes is not naive for multisubject searchlight studies", PloS one, 8(7), e69566. https://doi.org/10.1371/journal.pone.0069566.
  23. Samui, P. and Sitharam, T.G. (2011), "Machine learning modelling for predicting soil liquefaction susceptibility", Nat. Hazard. Earth Syst. Sci., 11(1), 1-9. https://doi.org/10.5194/nhess-11-1-2011.
  24. Saranya, N. and Mythili, A. (2020), "Classification of soil and crop suggestion using machine learning techniques", IJERT, 9(02), 671-673.
  25. Seong, H., Son, H. and Kim, C. (2018), "A comparative study of machine learning classification for color-based safety vest detection on construction-site images", KSCE J. Civil Eng., 22(11), 4254-4262. https://doi.org/10.1007/s12205-017-1730-3.
  26. Smith, D. and Peng, W. (2009), "Machine learning approaches for soil classification in a multi-agent deficit irrigation control system", 2009 IEEE International Conference on Industrial Technology, 1-6.
  27. Song, J.U., Lee, J.S. and Yoon, H.K. (2019), "Application of electrical conductivity method for adsorption of lead ions by rice husk ash", Measure., 144, 126-134. https://doi.org/10.1016/j.measurement.2019.04.094.
  28. Taher, K.I., Abdulazeez, A.M. and Zebari, D.A. (2021), "Data mining classification algorithms for analyzing soil data", Asian J. Res. Comput., 17-28. https://doi.org/10.9734/ajrcos/2021/v8i230196.
  29. Yoon, H.K. (2020), "Relationship between aspect ratio and crack density in porous-cracked rocks using experimental and optimization methods", Appl. Sci., 10(20), 7147. https://doi.org/10.3390/app10207147.
  30. Yoon, H.K. and Lee, J.S. (2010), "Field velocity resistivity probe for estimating stiffness and void ratio", Soil Dyn. Earthq. Eng., 30(12), 1540-1549. https://doi.org/10.1016/j.soildyn.2010.07.008.