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2017 Vol.33, Issue 6 Preview Page
Prediction of Ultimate Bearing Capacity of Soft Soils Reinforced by Gravel Compaction Pile Using Multiple Regression Analysis and Artificial Neural Network

다중회귀분석 및 인공신경망을 이용한 자갈다짐말뚝 개량지반의 극한 지지력 예측

Tae-Ho Bong1
,
Byoung-Il Kim2*
봉 태호1
,
김 병일2*
1Post Doctoral Researcher, School of Civil and Construction Engrg., Oregon State Univ., U.S.A.
2Prof., Dept. of Civil and Environmental Engrg., Myongji Univ.
1오레곤 주립대학교 토목건설공학과 박사후 연구원
2명지대학교 토목환경공학과 교수
*교신저자. *Corresponding Author. 
30 June 2017. pp. 27-36
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Abstract

Gravel compaction pile method has been widely used to improve the soft ground on the land or sea as one of the soft ground improvement technique. The ultimate bearing capacity of the ground reinforced by gravel compaction piles is affected by the soil strength, the replacement ratio of pile, construction conditions, and so on, and various prediction equations have been proposed to predict this. However, the prediction of the ultimate bearing capacity using the existing models has a very large error and variation, and it is not suitable for practical design. In this study, multiple regression analysis was performed using field loading test results to predict the ultimate bearing capacity of ground reinforced by gravel compaction pile, and the most efficient input variables are selected through evaluation of error by leave one out cross validation, and a multiple regression equation for the prediction of ultimate bearing capacity was proposed. In addition, the prediction error was evaluated by applying artificial neural network using the selected input variables, and the results were compared with those of the existing model.

Keywords
Gravel compaction pile
Bearing capacity
Multiple regression analysis
Artificial neural network
Cross validation

자갈다짐말뚝(Gravel Compaction Pile) 공법은 연약지반 개량공법 중의 하나로 육상 및 해상에서 연약 지반을 개량하기 위해 많이 사용되어 왔다. 자갈다짐말뚝으로 보강된 지반의 극한 지지력은 자갈다짐말뚝 및 지반의 강도, 치환율, 시공조건 등에 영향을 받으며 이를 예측하기 위한 다양한 예측식이 제안되었다. 하지만 기존 예측식을 활용한 극한지지력 예측은 오차율 및 변동성이 매우 크며, 실제 설계에 활용하기에는 부적합한 것으로 나타났다. 본 연구에서는 자갈다짐말뚝으로 보강된 지반의 극한 지지력을 예측하기 위하여 현장 재하시험결과를 활용한 다중회귀분석을 수행하였으며, 단일잔류 교차검증에 따른 예측오차평가를 통하여 가장 효율적인 입력변수를 선정하고 이에 대한 극한 지지력 예측식을 제안하였다. 또한 선정된 입력변수를 활용하여 인공신경망 적용에 따른 극한 지지력 예측오차를 평가하고 이를 기존 예측식에 따른 결과와 비교･분석하였다.

References
1
Barksdale, R. D. and Bachus, R. C. (1983), “Design and Construction of Stone Columns”, Rep. No. FHWA/RD 83/026, Federal Highway Administration, Washington, DC.
2
Bergado, D. T. and Lam, F. L. (1987), “Full Scale Load Test of Granular Piles with Different Densities and Different Proportions of Gravel and Sand in the Soft Bangkok Clay”, Soils Found., Vol.27, No.1, pp.86-93.
3
Brauns, J. (1978), “Initial Bearing Capacity of Stone Columns and Sand Piles”, Vol. I, Proc., Soil Reinforcing and Stabilizing Techniques in Engineering Practice, New South Wales Institute of Technology, Sydney, Australia, pp.497-512.
4
Cho, S. E. and Byeon, W. Y. (2007), “Reliability Analysis of Slopes Using ANN-based Limit-state Function”, Journal of the Korean Geotechnical Society, Vol.23, No.8, pp.117-127.
5
Greenwood, D. A. (1970), “Mechanical Improvement of Soils Below Ground Surface”, Proc. Ground Engineering Conference, Institute of Civil Engineering, pp.9-20.
6
Helsel, D. R. and Hirsch, R. M. (2002), Statistical Methods in Water Resources, USGS, TWRI-4-A3, p.310.
7
Hong, W. P., Kim, W. Y.,  Song, Y. S., and Lim, S. G. (2004), “Prediction of Landslide Using Artificial Neural Network Model”, Vol.20, No.8, pp.67-75.
8
Hughes, J. M. O., Withers, N. J., and Greenwood, D. A. (1975), “A Field Trial of the Reinforcing Effect of a Stone Column in Soil”, Geotechnique, Vol.25, No.1, pp.31-44.
9
Juang, C. H., Chen, C. J., and Tien. Y. M. (1999), “AppraisingCPT- based Liquefaction Resistance Evaluation Methods-ArtificialNeural Network Approach”, Canadian Geotechnical Journal, Vol.36, No.3, pp.443-454.
10
Kim, B. I., Cho, S. M., Kim, J. H. and Kim, S. R. (2015), Soft Ground Improvement Method, CIR Publishing Co. pp.239-283.
11
Kitazume, M. (2005), The Sand Cpmpaction Pile Method, Balkema, pp.1-7.
12
Mitchell, J. K. (1981), “Soil Improvement – State-of-the-art Report”, Proc., 10th Int. Conf. on Soil Mechanics and Foundation Engineering, Session 12, Int. Society of Soil Mechanics and Foundation Engineering, London, Vol.4, pp.506-565.
13
Montgomery, D. C. and Runger, G. C. (2010), Applied statistics and probability for engineers, 5th Ed., Wiley, New York.
14
Stone, M. (1974), “Cross-validatory Choice and Assessment of Statistical Predictions”, J. Roy. Statist. Soc. Ser. B, 36, pp.111-147.
15
Stuedlein, A. W. and Holtz, R. D. (2013), “Bearing Capacity of Spread Footings on Aggregate Pier Reinforced Clay”, J. Geotech. Geoenviron. Eng., Vol.139, No.1, pp.49-58.
16
Sulewska, M. J. (2011), “Applying Artificial Neural Networks for Analysis of Geotechnical Problems”, Computer Assisted Mechanics and Engineering Sciences, 18: 231-241.
17
Vesic, A. S. (1972), “Expansion of Cavities in Infinite Soil Mass”, J. Soil Mech, and Found. Div., Vol.98, No.3, pp.265-290.
Information
  • Publisher :The Korean Geotechnical Society
  • Publisher(Ko) :한국지반공학회
  • Journal Title :Journal of the Korean Geotechnical Society
  • Journal Title(Ko) :한국지반공학회 논문집
  • Volume : 33
  • No :6
  • Pages :27-36
  • Received Date : 2017-03-24
  • Accepted Date : 2017-05-23
  • DOI :https://doi.org/10.7843/kgs.2017.33.6.27
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