All Issue

2025 Vol.24, Issue 1 Preview Page

Research Article

Determination of Upper and Lower Estimates of Undrained Shear Strength in Coastal Soils through Statistical Analysis

통계분석을 통한 연안 지반의 비배수전단강도 상한 및 하한 결정 방안

Dae-Gun Kim1
,
Yeong-Jae Lee1*
,
Woo-Young Kim2
,
Tae-Hyung Kim3
김 대근1
,
이 영재1*
,
김 우영2
,
김 태형3
1Non-Member, Graduate Student, Dept. of Civil Engineering, Korea Maritime and Ocean Univ., 727 Taejong-ro, Yeongdo-gu, Busan 49112, Republic of Korea
2Member, Graduate Student, Dept. of Civil Engineering, Korea Maritime and Ocean Univ., 727 Taejong-ro, Yeongdo-gu, Busan 49112, Republic of Korea
3Member, Professor, Dept. of Civil Engrg., Korea Maritime and Ocean Univ., 727 Taejong-ro, Yeongdo-gu, Busan 49112, Republic of Korea
*Corresponding Author
30 March 2025. pp. 31-37
PDF XML Citation
Abstract

In domestic design practices, undrained shear strength values are obtained at various depths but are often simplified into a single representative value for design, leading to issues of underdesign or overdesign. To address this, European practices utilize statistical analysis to estimate upper and lower bound values for each soil depth and apply these bounds in design. In this study, the statistical analysis method presented in DNV-RP-C207 was applied to undrained shear strength data to determine upper and lower bound values. Specifically, the KernelCPD algorithm from the ruptures library in Python was used to segment the data according to soil depth, followed by the calculation of the coefficient of variation (CV). Based on this CV, the mean value and linear regression analysis were applied to estimate the upper bound. The lower bound was determined by applying quantile regression to the lower 20% of the data. The results of the statistically derived upper and lower bounds appropriately reflect the characteristics of the dataset and are expected to provide suitable design parameters depending on the specific objectives.

Keywords
Statistical analysis
Python
Undrained shear strengh
Upper estimate, Lower estimate

국내 설계에서는 심도별 비배수전단강도를 취득한 후 대표 단일 값을 설계에 사용하고 있어 과소 혹은 과대 설계되는 문제가 발생하고 있다. 이를 해결하기 위해 유럽에서는 통계적 분석을 활용하여 지반 심도별로 상한값과 하한값을 산정하여 설계에 적용하고 있다. 본 연구에서는 DNV-RP-C207에서 제시하는 통계적 분석 방법을 비배수전단강도에 적용하여 상한값과 하한값을 산정하였다. 지반 심도에 따른 비배수전단강도에 대하여 파이썬에서 제공하는 ruptures 라이브러리의 KernelCPD 알고리즘을 적용하여 구간을 나눈 후 변동계수를 계산하였으며 변동계수에 따라 평균값과 선형회귀분석을 적용하여 상한값을 산정하였다. 하한값은 하위 20%의 데이터에 대해 분위수 회귀를 적용하여 산정하였다. 통계적 분석을 적용한 상한값과 하한값의 결과는 데이터의 특성을 적절하게 반영하는 것으로 분석되며 목적에 따라 적절한 설계 변수를 제공할 것으로 판단된다.

References
1

Allan, B. P., Klaus, N. M. and Carsten, L. (2014), Hywind Offshore Wind Farm Geotechnical Investigations 2014, GEO Project No. 36685, Prepared for Dudgeon Offshore Wind Ltd., London, UK.

2

Bouchikhi, I., Ferrari, A., Richard, C., Bourrier, A. and Bernot, M. (2019), Kernel based online change point detection, 2019 27th European Signal Processing Conference (EUSIPCO), pp.1-5.

10.23919/EUSIPCO.2019.8902582
3

DET NORSKE VERITAS. (2010), DNV-RP-C207 Statistical representation of soil data, Recommended Practice, October 2010, Det Norske Veritas, Norway.

4

DET NORSKE VERITAS. (2021), DNV-RP-E303 Geotechnical design and installation of suction anchors in clay, Recommended Practice, October 2021, Det Norske Veritas, Norway.

5

Fan, Y., Woo, Y. and Kim, S. (2015), "Selection of Kernels and its Parameters in Applying SVM to ASV", Proceedings of the Korean Institute of Information and Commucation Sciences Conference, pp.1045-1046.

6

Killick, R., Fearnhead, P. and Eckley, I. A. (2012), "Optimal Detection of Changepoints With a Linear Computational Cost", Journal of the American Statistical Association, Vol.107, No.500, pp.1590-1598.

10.1080/01621459.2012.737745
7

Kim, M. and Baek, C. (2022), "High-dimensional change point detection using MOSUM-based sparse projection", The Korean Journal of Applied Statistics, Vol.35, No.1, pp.63-75.

10.5351/KJAS.2025.38.1.063
8

Kim, S. Y. (2019), A Study on the Latest Trends in Small Area Estimation Techniques, Statistics Research Institute, Statistics Korea (KOSTAT), Korea.

9

Lee, D. H. (2014), "Game programming education using the Python language", Journal of Korea Game Society, Vol.11, No.1, pp.8-16.

10

Lee, J., Jeong, J. and Chae, Y. (2020), "Fault Detection Algorithm for Photovoltaic Generation Systems with CUSUM", Journal of Korean Society of Living Environmental System, 27(3), pp.318-327.

10.21086/ksles.2020.06.27.3.318
11

Park, H. I., Kim, Y., Hwang, D. and Lee, S. R. (2006), "Prediction of Settlement of Vertical Drainage-Reinforced Soft Clay Ground using Back-Analysis", Journal of the Korean Society of Civil Engineers, Vol.26, No.4, pp.229-238.

12

Park, T. and Sim, G. (2023), "A Comparative study on smoothing techniques for performance improvement of LSTM learning model", Journal of The Korea Society of Computer and Information, Vol.28, No.1, pp.17-26.

13

Rheem, S. and Rheem, I. (2012), "The Model-Fit Proportion and the Coefficient of Pure Error Variation, the Measures to Supplement the Lack-of-Fit Test in Response Surface Analysis", Journal of The Korean Data Analysis Society, Vol.14, No.6, pp.2989-2994.

14

Yu, Y., Lee, M., Lee, C., Cheon, Y., Baek, S., Kim, Y. and Jeong, J. (2023), "Estimating APC Model Parameters for Dynamic Intervals Determined Using Change-Point Detection in Continuous Processes in the Petrochemical Industry", Processes, Vol.11, No.8, 2229.

10.3390/pr11082229
Information
  • Publisher :Korean Geosythetics Society
  • Publisher(Ko) :한국지반신소재학회
  • Journal Title :Journal of the Korean Geosynthetics Society
  • Journal Title(Ko) :한국지반신소재학회 논문집
  • Volume : 24
  • No :1
  • Pages :31-37
  • Received Date : 2025-01-21
  • Revised Date : 2025-02-07
  • Accepted Date : 2025-02-07
  • DOI :https://doi.org/10.12814/jkgss.2025.24.1.031
Journal Informaiton Journal of the Korean Geosynthetics Society Journal of the Korean Geosynthetics Society
  • NRF
  • KOFST
  • KISTI Current Status
  • KISTI Cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close