Journal of the Korean GEO-environmental Society (한국지반환경공학회 논문집)

Korean Geo-Environmental Society (한국지반환경공학회)
권호 표지
DOI QR코드

DOI QR Code

Multi-layered Ground Back Analysis of Retaining Wall Using Differential Evolution Algorithm : Basic Research of Digital Twin

차분진화 알고리즘을 이용한 흙막이 벽체의 다층지반 역해석 : 디지털 트윈 기초연구

  • Lee, Donggun (Department of Civil Engineering, Inha University) ;
  • Kang, Kyungnam (Department of Civil Engineering, Inha University) ;
  • Song, Kiil (Department of Civil Engineering, Inha University)
  • Received : 2021.11.10
  • Accepted : 2021.12.03
  • Published : 2022.01.01
Download PDF
⟨ Previous Next ⟩

Abstract

It is very important to investigate the ground properties of a construction site for the stability during the construction of the retaining wall. In the retaining wall construction stage, ground properties are checked through ground investigation, but the actual ground properties may be different from the ground investigation result. In order to analyze the stability of the retaining wall in real time, it is important to reflect the properties of the actual ground. Also, when it is judged that the wall is unstable, an appropriate solution must be provided for the stability of the wall. This study aims to present a technique for predicting the actual ground properties through a differential evolution algorithm and judging the stability of the earth wall in real time through the digital twin of the retaining wall.

흙막이 벽체의 시공 중 안정성을 확보하기 위하여 시공현장의 지반 정보를 조사하는 것은 매우 중요한 일이다. 흙막이 벽체 시공단계에서 지반의 정보는 지반조사를 통해 확인하고 있지만, 실제 지반 정보의 특성은 결과와 상이할 수 있다. 실시간으로 흙막이 벽체의 안정성을 분석하기 위해서는 실제 지반의 상태를 반영하는 것이 중요하다. 또한 벽체가 불안정하다고 판단 시 벽체의 안정성을 확보하기 위하여 적절한 솔루션을 제공해야 한다. 본 연구는 차분진화 알고리즘을 통하여 실제 지반의 정보를 예측하고 흙막이 벽체의 디지털 트윈을 통하여 흙막이 벽체의 안정성을 실시간으로 판단하는 기법을 제시하고자 한다.

Keywords

Acknowledgement

이 연구는 국토교통부/국토교통과학기술진흥원이 시행하고 한국도로공사가 총괄하는 "스마트건설기술개발 국가 R&D사업(과제번호 21SMIP-A158708-02)"의 지원으로 수행되었습니다.

References

  1. An, J. S. (2017), The development of differential evolution-based back analysis algorithm for the evaluation of operating tunnel stability, Ph.D's thesis, Inha University.
  2. Han, M. S., Shin, S. B., Moon, T. W., Kim, D. W. and Lee, J. H. (2019), Digital twin model of a beam structure using strain measurement data, Journal of KBIM, Vol. 9, No. 3, pp. 1~7.
  3. Jeong, D. Y. (2021), A five-level model of the technical definition and detailed evolution of the digital twin, OSIA Standards & Technology Review, Vol. 34, No. 1, pp. 10~16.
  4. Jeong, J. H., Hwang, S. Y., Yun, H. J. and Yu, U. S. (2020), [IV Focus Area] Smart construction digital platform construction and test bed operation, Construction Engineering and Management, Vol. 21, No. 4, pp. 25~30.
  5. Kang, K. N. (2019), Design and reinforcement plan of NATM tunnel wih optimization algorithm, Master's thesis, Inha University.
  6. Schweiger, H. F. (2002), Benchmarking in geotechnics 1. Computational Geotechnics Group, CGG IR006.
  7. Seong, J. H., Jeong, S. H. and Shin, J. Y. (2011), A study on safety management improvement plan for retaining excavation construction through accident case analysis, Journal of the Korea institute for structural maintenance and inspection., Vol. 15, No. 6, pp. 175~183. https://doi.org/10.11112/jksmi.2011.15.6.175
  8. Shim, C. S., Jeon, C. H., Kang, W. R., Dang, G. S. and Sso, K. Y. (2018), Definition of digital twin models for prediction of future performance of bridges, Journal of KBIM, Vol. 8, No. 4, pp. 13~22.
  9. Vesterstrom, J. and Thomsen, R. (2004), A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems, In Proceedings of the 2004 congress on evolutionary computation (IEEE Cat. No. 04TH8753), Vol. 2, pp. 1980~1987.
  10. Yin, Z. Y., Jin, Y. F., Shen, J. S. and Hicher, P. Y. (2018), Optimization techniques for identifying soil parameters in geotechnical engineering: comparative study and enhancement, International Journal for Numerical and Analytical Methods in Geomechanics, Vol. 42, No. 1, pp. 70~94. https://doi.org/10.1002/nag.2714