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)
  • 투고 : 2022.01.13
  • 심사 : 2022.07.05
  • 발행 : 2022.08.10

초록

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.

키워드

과제정보

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).

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