Journal of the Earthquake Engineering Society of Korea (한국지진공학회논문집)

Earthquake Engineering Society of Korea (한국지진공학회)
권호 표지
DOI QR코드

DOI QR Code

Predictive Models of Deep Shear Wave Velocity Profiles at Excavated Rock Sites in Korea

국내 굴착 암반 부지에 대한 심부 전단파속도 주상도 예측 모델 개발

  • Kim, Jieun ( Department of Civil Urban Earth and Environmental Engineering, Ulsan National Institute of Science and Technology) ;
  • Kim, Byungmin ( Department of Civil Urban Earth and Environmental Engineering, Ulsan National Institute of Science and Technology) ;
  • Cho, Youngkyu ( Department of Civil Urban Earth and Environmental Engineering, Ulsan National Institute of Science and Technology)
  • 김지은 (울산과학기술원 지구환경도시건설공학과) ;
  • 김병민 (울산과학기술원 지구환경도시건설공학과) ;
  • 조영규 (울산과학기술원 지구환경도시건설공학과)
  • Received : 2024.11.16
  • Accepted : 2024.12.09
  • Published : 2025.01.01
Download PDF
⟨ Previous Next ⟩

Abstract

IIn the context of site response analysis, the use of shear wave velocity (VS) profiles that consider the seismological rock (VS ≥ 3,000 m/s) depth is recommended. This study proposes regression analysis and machine learning-based models to predict deep VS profiles for a specialized excavated rock site in South Korea. The regression model was developed by modifying mathematical expressions from a previous study and analyzing the correlation between VS50 and model variables to predict deep VS beyond 50 m. The machine learning models, designed using tree-based algorithms and a fully connected hierarchical structure, were developed to predict VS from 51 m to 300 m at 1 m intervals. These models were validated by comparing them with measured deep VS profiles and accurately estimating the trend of deep VS variations. The proposed prediction models are expected to improve the accuracy of ground motion predictions for a specialized excavated rock site in Korea.

Keywords

Acknowledgement

본 논문은 한국수력원자력(주)에서 재원을 부담하여 "제 2022-기술-03호"에서 수행한 연구입니다. 머신러닝 기반의 예측 모델 개발과 관련하여 도움을 주신 교토대학교 김지송 박사님께 감사를 표합니다.

References

  1. Ministry of Land. Infrastructure and Transport. Korean Design Standard. KDS 17 10 00; c2024.
  2. Hashash YM, Kottke AR, Stewart JP, Campbell KW, Kim B, Moss C, Nikolaou S, Rathje EM, Silva WJ. Reference rock site condition for central and eastern North America. Bull Seismol Soc Am. 2014;104(2):684-701. https://doi.org/10.1785/0120130132
  3. Poggi, V., Edwards, B., Fäh, D. Derivation of a reference shear-wave velocity model from empirical site amplification. Bull Seismol Soc Am. 2011;101(1):258-274. https://doi.org/10.1785/0120100060
  4. Kawase H, Nagashima F, Nakano K, Mori Y. Direct evaluation of S-wave amplification factors from microtremor H/V ratios: Double empirical corrections to "Nakamura" method. Soil Dyn Earthq Eng. 2019;126:105067. https://doi.org/10.1016/j.soildyn.2018.01.049
  5. Sun CG, Chung CK, Kim DS. Determination of mean shear wave velocity to the depth of 30m based on shallow shear wave velocity profile. J Earthq Eng Soc Korea. 2007;11(1):45-57. https://doi.org/10.5000/EESK.2007.11.1.045
  6. Shi J, Asimaki D. A generic velocity profile for basin sediments in California conditioned on VS30. Seismol Res Lett. 2018;89(4):1397-1409. https://doi.org/10.1785/0220170268
  7. Passeri F, Foti S, Rodriguez-Marek A. A new geostatistical model for shear wave velocity profiles. Soil Dyn Earthq Eng. 2020;136:106247. https://doi.org/10.1016/j.soildyn.2020.106247
  8. Singh, S., Kanli, A. I. Estimating shear wave velocities in oil fields: a neural network approach. Geosci J. 2016;20:221-228. https://doi.org/10.1007/s12303-015-0036-z
  9. Anemangely M, Ramezanzadeh A, Amiri H, Hoseinpour SA. Machine learning technique for the prediction of shear wave velocity using petrophysical logs. J Pet Sci Eng. 2019;174:306-327. https://doi.org/10.1016/j.petrol.2018.11.032
  10. Rajabi M, Hazbeh O, Davoodi S, Wood DA, Tehrani PS, Ghorbani H, Mehrad M, Mohamadian N, Rukavishnikov VS, Radwan AE. Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms. J Pet Explor Prod Technol. 2023;13(1):19-42. https://doi.org/10.1007/s13202-022-01531-z
  11. Cox BR, Teague DP. Layering ratios: a systematic approach to the inversion of surface wave data in the absence of a priori information. Geophys J Int. 2016;207(1):422-438. https://doi.org/10.1093/gji/ggw282
  12. Vrettos C. Simple inversion procedure for shallow seismic refraction in continuously nonhomogeneous soils. Soil Dyn Earthq Eng. 1996;15(6):381-386. https://doi.org/10.1016/0267-7261(96)00012-7
  13. Toro G. Probabilistic models of site velocity profiles for generic and site-specific ground-motion amplification studies. Brookhaven National Library, Upton, New York; c1995.
  14. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay É. Scikit-learn: Machine learning in Python. J Mach Learn Res. 2011;12:2825-2830.