Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Advertisement

Springer Nature Link
Log in

Evaluation of liquefaction potential based on CPT data using random forest

  • Original Paper
  • Published:
Natural Hazards Aims and scope Submit manuscript

Abstract

The prediction of liquefaction potential of soil due to an earthquake is an essential task in civil engineering. In this paper, random forest (RF) method is introduced and investigated for the prediction of seismic liquefaction potential of soil based on the cone penetration test data. RF has been proposed on the basis of classification and regression trees with “ensemble learning” strategy. The RF models were developed and validated on a relatively large dataset comprising 226 field records of liquefaction performance and cone penetration test measurements. The database contains the information about depth of potentially liquefiable soil layer (D), cone tip resistance (\(q_{\text{c}}\)), sleeve friction ratio (\(R_{\text{f}}\)), effective vertical stress (\(\sigma_{0}^{\prime }\)), total vertical stress (\(\sigma_{0}\)), maximum horizontal ground surface acceleration (\(\alpha_{\hbox{max} }\)) and earthquake magnitude (\(M_{\text{w}}\)). Two RF models (Model I and Model II) are developed for predicting the occurrence and non-occurrence of liquefaction on the basis of combination of above input parameters. The results of RF models have been compared with the available artificial neural network (ANN) and support vector machine (SVM) models. It is shown that the proposed RF models provide more accurate results than the ANN and SVM models proposed in the literature. The developed RF provides a viable tool for civil engineers to determine the liquefaction potential of soil.

This is a preview of subscription content,log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Breiman L (1996) Bagging predictors. Mach Learn 24:123–140. doi:10.1007/BF00058655

    Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45:5–32. doi:10.1023/A:1010933404324

    Article  Google Scholar 

  • Calderoni L, Ferrara M, Franco A, Maio D (2015) Indoor localization in a hospital environment using random forest classifiers. Expert Syst Appl 42:125–134

    Article  Google Scholar 

  • Dietterich TG (2000) An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach Learn 40:139–157. doi:10.1023/A:1007607513941

    Article  Google Scholar 

  • Gandomi AH, Fridline MM, Roke DA (2013) Decision tree approach for soil liquefaction assessment. Sci World J. doi:10.1155/2013/346285

  • Goh AT (1996) Neural-network modeling of CPT seismic liquefaction data. J Geotech Eng 122:70–73. doi:10.1061/(ASCE)0733-9410(1996)122:1(70)

    Article  Google Scholar 

  • Goh AT, Goh S (2007) Support vector machines: their use in geotechnical engineering as illustrated using seismic liquefaction data. Comput Geotech 34:410–421. doi:10.1016/j.compgeo.2007.06.001

    Article  Google Scholar 

  • Ham J, Chen Y, Crawford MM, Ghosh J (2005) Investigation of the random forest framework for classification of hyperspectral data. IEEE Trans Geosci Remote Sens 43:492–501. doi:10.1109/TGRS.2004.842481

    Article  Google Scholar 

  • Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20:832–844. doi:10.1109/34.709601

    Article  Google Scholar 

  • Javadi AA, Ahangar-Asr A, Johari A, Faramarzi A, Toll D (2012) Modelling stress–strain and volume change behaviour of unsaturated soils using an evolutionary based data mining technique, an incremental approach. Eng Appl Artif Intell 25:926–933

    Article  Google Scholar 

  • Joelsson SR, Benediktsson JA, Sveinsson JR (2006) Feature selection for morphological feature extraction using random forests. In: IEEE, proceedings of the 7th Nordic, signal processing symposium, 2006. NORSIG 2006, pp 10–13

  • Juang CH, Yuan H, Lee D-H, Lin P-S (2003) Simplified cone penetration test-based method for evaluating liquefaction resistance of soils. J Geotech Geoenviron Eng 129:66–80. doi:10.1061/(ASCE)1090-0241(2003)129:1(66)

    Article  Google Scholar 

  • Kohestani VR, Hassanlourad M (2015) Modeling the mechanical behavior of carbonate sands using artificial neural networks and support vector machines. Int J Geomech. doi:10.1061/(ASCE)GM.1943-5622.0000509

    Google Scholar 

  • Liao SS, Veneziano D, Whitman RV (1988) Regression models for evaluating liquefaction probability. J Geotech Eng 114:389–411. doi:10.1061/(ASCE)0733-9410(1988)114:4(389)

    Article  Google Scholar 

  • Liaw A, Wiener M (2002) Classification and regression by random forest. R News 2:18–22

    Google Scholar 

  • Livingston G, Piantedosi M, Kurup P, Sitharam T (2008) Using decision-tree learning to assess liquefaction potential from CPT and Vs. In: Proceedings of the 4th geotechnical earthquake engineering and soil dynamics congress, pp 1–10

  • Mendez G, Lohr S (2011) Estimating residual variance in random forest regression. Comput Stat Data Anal 55:2937–2950

    Article  Google Scholar 

  • Oommen T, Baise LG, Vogel R (2010) Validation and application of empirical liquefaction models. J Geotech Geoenviron Eng 136:1618–1633

    Article  Google Scholar 

  • Pal M (2006) Support vector machines-based modelling of seismic liquefaction potential. Int J Numer Anal Meth Geomech 30:983–996. doi:10.1002/nag.509

    Article  Google Scholar 

  • Samui P (2007) Seismic liquefaction potential assessment by using relevance vector machine. Earthq Eng Eng Vib 6:331–336. doi:10.1007/s11803-007-0766-7

    Article  Google Scholar 

  • Samui P, Karthikeyan J (2014) The use of a relevance vector machine in predicting liquefaction potential. Indian Geotech J 44:458–467. doi:10.1007/s40098-013-0094-y

    Article  Google Scholar 

  • Samui P, Sitharam T (2011) Machine learning modelling for predicting soil liquefaction susceptibility. Nat Hazards Earth Syst Sci 11:1–9. doi:10.5194/nhess-11-1-2011

    Article  Google Scholar 

  • Seed HB, Idriss IM (1971) Simplified procedure for evaluating soil liquefaction potential. J Soil Mech Found Div 97:1249–1273

    Google Scholar 

  • Verikas A, Gelzinis A, Bacauskiene M (2011) Mining data with random forests: a survey and results of new tests. Pattern Recogn 44:330–349. doi:10.1016/j.patcog.2010.08.011

    Article  Google Scholar 

  • Vincenzi S, Zucchetta M, Franzoi P, Pellizzato M, Pranovi F, De Leo GA, Torricelli P (2011) Application of a random forest algorithm to predict spatial distribution of the potential yield ofRuditapes philippinarum in the Venice lagoon, Italy. Ecol Model 222:1471–1478. doi:10.1016/j.ecolmodel.2011.02.007

    Article  Google Scholar 

  • Vitorino D, Coelho S, Santos P, Sheets S, Jurkovac B, Amado C (2014) A random forest algorithm applied to condition-based wastewater deterioration modeling and forecasting. Procedia Eng 89:401–410

    Article  Google Scholar 

  • Watts JD, Lawrence RL, Miller PR, Montagne C (2009) Monitoring of cropland practices for carbon sequestration purposes in north central Montana by Landsat remote sensing. Remote Sens Environ 113:1843–1852. doi:10.1016/j.rse.2009.04.015

    Article  Google Scholar 

  • Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, Los Altos

    Google Scholar 

  • Xue X, Yang X (2013) Application of the adaptive neuro-fuzzy inference system for prediction of soil liquefaction. Nat Hazards 67:901–917

    Article  Google Scholar 

  • Youd T et al (2001) Liquefaction resistance of soils: summary report from the 1996 NCEER and 1998 NCEER/NSF workshops on evaluation of liquefaction resistance of soils. J Geotech Geoenviron Eng 127:817–833. doi:10.1061/(ASCE)1090-0241(2001)127:10(817)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran

    V. R. Kohestani, M. Hassanlourad & A. Ardakani

Authors
  1. V. R. Kohestani

    You can also search for this author inPubMed Google Scholar

  2. M. Hassanlourad

    You can also search for this author inPubMed Google Scholar

  3. A. Ardakani

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toV. R. Kohestani.

Rights and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kohestani, V.R., Hassanlourad, M. & Ardakani, A. Evaluation of liquefaction potential based on CPT data using random forest.Nat Hazards79, 1079–1089 (2015). https://doi.org/10.1007/s11069-015-1893-5

Download citation

Keywords

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Advertisement

[8]ページ先頭
©2009-2025 Movatter.jp