[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: a multi-dataset study

  • Original Paper
  • Published:
Bulletin of Engineering Geology and the Environment Aims and scope Submit manuscript

Abstract

Assessment of the earthquake-induced liquefaction potential is a critical concern in design processes of construction projects. This study proposes a novel soft computing model with a hierarchical structure for evaluating earthquake-induced soil liquefaction. The new approach, named KFDA-LSSVM, combines kernel Fisher discriminant analysis (KFDA) with a least squares support vector machine (LSSVM). Based on the original data set, KFDA is used as a first-level analysis to construct an additional feature that best represents the data structure with consideration of different class labels. In the next level of analysis, based on such additional features and the original features, LSSVM generalizes a classification boundary that separates the learning space into two decision domains: “liquefaction” and “non-liquefaction.” Three data sets of liquefaction records have been used to train and verify the proposed method. The model performance is reliably assessed via a repeated sub-sampling process. Experimental results supported by the Wilcoxon signed-rank test demonstrate significant improvements of the hybrid framework over other benchmark approaches.

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
£29.99 /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 (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Abbaszadeh Shahri A (2016) Assessment and prediction of liquefaction potential using different artificial neural network models: a case study. Geotech Geol Eng 34(3):807–815

    Article  Google Scholar 

  • Achen CH (2005) Two-step hierarchical estimation: beyond regression analysis. Polit Anal 13(4):447–456

    Article  Google Scholar 

  • Alavi AH, Gandomi AH (2012) Energy-based numerical models for assessment of soil liquefaction. Geosci Front 3(4):541–555

    Article  Google Scholar 

  • Ares J, Lara JA, Lizcano D, Suárez S (2016) A soft computing framework for classifying time series based on fuzzy sets of events. Inf Sci 330:125–144

    Article  Google Scholar 

  • Balabin RM, Lomakina EI (2011) Support vector machine regression (LS-SVM)-an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data? Phys Chem Chem Phys 13(24):11710–11718

    Article  Google Scholar 

  • Battiti R (1994) Using mutual information for selecting features in supervised neural net learning. IEEE Trans Neural Netw 5(4):537–550

    Article  Google Scholar 

  • Baziar MH, Jafarian Y, Shahnazari H, Movahed V, Amin Tutunchian M (2011) Prediction of strain energy-based liquefaction resistance of sand–silt mixtures: an evolutionary approach. Comput Geosci 37(11):1883–1893

    Article  Google Scholar 

  • Cabalar AF, Cevik A, Gokceoglu C (2012) Some applications of adaptive neuro-fuzzy inference system (ANFIS) in geotechnical engineering. Comput Geotech 40:14–33

    Article  Google Scholar 

  • Cheng M-Y, Hoang N-D (2015) Typhoon-induced slope collapse assessment using a novel bee colony optimized support vector classifier. Nat Hazards 78(3):1961–1978

    Article  Google Scholar 

  • Cheng M-Y, Hoang N-D, Limanto L, Wu Y-W (2014) A novel hybrid intelligent approach for contractor default status prediction. Knowl Based Syst 71:314–321

    Article  Google Scholar 

  • Chou J-S, Tsai C-F (2012) Concrete compressive strength analysis using a combined classification and regression technique. Autom Constr 24:52–60

    Article  Google Scholar 

  • Daftari A (2015) New approach in prediction of soil liquefaction. Doctor dissertation, Freiberg University of Mining and Technology

  • De Brabanter K, Karsmakers P, Ojeda F, Alzate C, De Brabanter J, Pelckmans K, De Moor B, Vandewalle J, Suykens JAK (2010) LS-SVMlab toolbox user’s guide version 1.8. Internal Report 10-146, ESAT-SISTA, K.U.Leuven (Leuven, Belgium)

  • Dong S, Wang Z, Zeng L (2016) Lithology identification using kernel Fisher discriminant analysis with well logs. J Pet Sci Eng 143:95–102

    Article  Google Scholar 

  • Duda RO, Hart PE, Stock DG (2001) Pattern classification, 2nd edn. Wiley, Hoboken

    Google Scholar 

  • Erzin Y, Ecemis N (2015) The use of neural networks for CPT-based liquefaction screening. Bull Eng Geol Environ 74(1):103–116

    Article  Google Scholar 

  • Farrokhzad F, Choobbasti AJ, Barari A (2012) Liquefaction microzonation of Babol city using artificial neural network. J King Saud Univ Sci 24(1):89–100

    Article  Google Scholar 

  • Franc V, Hlavc V (2004) Statistical pattern recognition toolbox for matlab. Research reports of CMP, Czech Technical University in Prague

  • Gestel TV, Suykens JAK, Baesens B, Viaene S, Vanthienen J, Dedene G, Moor BD, Vandewalle J (2004) Benchmarking least squares support vector machine classifiers. Mach Learn 54:5–32

    Article  Google Scholar 

  • Goh A (1994) Seismic liquefaction potential assessed by neural networks. J Geotech Eng ASCE 120(9):1467–1480

    Article  Google Scholar 

  • Goh A (1996) Neural-network modeling of CPT seismic liquefaction data. J Geotech Eng ASCE 122(1):70–73

    Article  Google Scholar 

  • Goh ATC, Goh SH (2007) Support vector machines: their use in geotechnical engineering as illustrated using seismic liquefaction data. Comput Geotech 34(5):410–421

    Article  Google Scholar 

  • Goh ATC, Zhang WG (2014) An improvement to MLR model for predicting liquefaction-induced lateral spread using multivariate adaptive regression splines. Eng Geol 170:1–10

    Article  Google Scholar 

  • Hanna AM, Ural D, Saygili G (2007) Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data. Soil Dyn Earthq Eng 27(6):521–540

    Article  Google Scholar 

  • Hoang N-D, Tien Bui D, Liao K-W (2016) Groutability estimation of grouting processes with cement grouts using differential flower pollination optimized support vector machine. Appl Soft Comput 45:173–186

    Article  Google Scholar 

  • Hu J-L, Tang X-W, Qiu J-N (2015) A Bayesian network approach for predicting seismic liquefaction based on interpretive structural modeling. Georisk Assess Manag Risk Eng Syst Geohazards 9(3):200–217

    Article  Google Scholar 

  • Huang G-B (2016) Basic ELM algorithms. http://www.ntu.edu.sg/home/egbhuang/elm_codes.html. Accessed 1 Feb 2016

  • Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501

    Article  Google Scholar 

  • Juang CH, Chen CJ (2000) A rational method for development of limit state for liquefaction evaluation based on shear wave velocity measurements. Int J Numer Anal Methods Geomech 24:1–27

    Article  Google Scholar 

  • Kang F, Li JJ, Zhou H (2013) Artificial neural network model for evaluating gravelly soils liquefaction using shear wave velocity. In: Proc. of Sixth China-Japan-US Trilateral Symposium on Lifeline Earthquake Engineering, Chengdu, China, May 28–June 1, pp 608–615. doi.10.1061/9780784413234.078

  • Kwak N, Choi C-H (2002) Input feature selection by mutual information based on Parzen window. IEEE Trans Pattern Anal Mach Intell 24(12):1667–1671

    Article  Google Scholar 

  • Lary DJ, Alavi AH, Gandomi AH, Walker AL (2016) Machine learning in geosciences and remote sensing. Geosci Front 7(1):3–10

    Article  Google Scholar 

  • López V, Fernández A, García S, Palade V, Herrera F (2013) An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inf Sci 250:113–141

    Article  Google Scholar 

  • Mathworks (2015) Statistics and machine learning toolbox. The MathWorks, Inc, Massachusetts, United States

  • McRae GJ, Tilden JW, Seinfeld JH (1982) Global sensitivity analysis—a computational implementation of the Fourier amplitude sensitivity test (FAST). Comput Chem Eng 6(1):15–25

    Article  Google Scholar 

  • Mika S, Rätsch G, Weston J, Schölkopf B, Müller K (1999) Fisher discriminant analysis with kernels. In: Proc. of the 1999 IEEE Neural Networks for Signal Processing, Madison, WI, 23–25 Aug 1999, pp 41–48

  • Min H-K, Hou Y, Park S, Song I (2016) A computationally efficient scheme for feature extraction with kernel discriminant analysis. Pattern Recognit 50:45–55

    Article  Google Scholar 

  • Mittal A, Devi G, Chauhan PKS (2014) Application of ANN to predict liquefaction potential of soil deposits for Chandigarh region, India. In: Pant M, Deep K, Nagar A, Bansal CJ (eds) Proceedings of the Third International Conference on Soft Computing for Problem Solving: SocProS 2013, vol. 1, Springer India, New Delhi, pp 441–450

  • Morgenroth J, Almond P, Scharenbroch BC, Wilson TM, Sharp-Heward S (2014) Soil profile inversion in earthquake-induced liquefaction-affected soils and the potential effects on urban trees. Geoderma 213:155–162

    Article  Google Scholar 

  • Muller K, Mika S, Ratsch G, Tsuda K, Scholkopf B (2001) An introduction to kernel-based learning algorithms. IEEE Trans Neural Netw 12(2):181–201

    Article  Google Scholar 

  • Pal M (2006) Support vector machines-based modelling of seismic liquefaction potential. Int J Numer Anal Methods Geomech 30(10):983–996

    Article  Google Scholar 

  • Peng H, Fulmi L, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238

    Article  Google Scholar 

  • Pianosi F, Sarrazin F, Wagener T (2015) A matlab toolbox for global sensitivity analysis. Environ Model Softw 70:80–85

    Article  Google Scholar 

  • Piotrowski AP (2014) Differential evolution algorithms applied to neural network training suffer from stagnation. Appl Soft Comput 21:382–406

    Article  Google Scholar 

  • Rezania M, Faramarzi A, Javadi AA (2011) An evolutionary based approach for assessment of earthquake-induced soil liquefaction and lateral displacement. Eng Appl Artif Intel 24(1):142–153

    Article  Google Scholar 

  • Rousu J, Saunders C, Szedmak S (2005) Learning hierarchical multi-category text classification models. In: Proc. of the 22nd International Conference on Machine Learning (ICML ‘05), Bonn, Germany, 07–11 Aug 2005, pp 744–751

  • Samui P (2011) Least square support vector machine and relevance vector machine for evaluating seismic liquefaction potential using SPT. Nat Hazards 59(2):811–822

    Article  Google Scholar 

  • Samui P, Karthikeyan J (2013) Determination of liquefaction susceptibility of soil: a least square support vector machine approach. Int J Numer Anal Methods Geomech 37(9):1154–1161

    Article  Google Scholar 

  • Samui P, Jagan J, Hariharan R (2016) An alternative method for determination of liquefaction susceptibility of soil. Geotech Geol Eng 34(2):735–738

    Article  Google Scholar 

  • Seed HB, Idriss IM (1971) Simplified procedure for evaluating soil liquefaction potential. J Soil Mech Found ASCE 97(9):1249–1273

    Google Scholar 

  • Seo M-W, Olson SM, Sun C-G, Oh M-H (2012) Evaluation of liquefaction potential index along western coast of South Korea using SPT and CPT. Mar Georesour Geotech 30(3):234–260

    Article  Google Scholar 

  • Shahangian B, Pourghassem H (2016) Automatic brain hemorrhage segmentation and classification algorithm based on weighted grayscale histogram feature in a hierarchical classification structure. Biocybern Biomed Eng 36(1):217–232

    Article  Google Scholar 

  • Shahnazari H, Tutunchian M, Mashayekhi M, Amini A (2012) Application of soft computing for prediction of pavement condition index. J Transp Eng ASCE 138(12):1495–1506

    Article  Google Scholar 

  • Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Sivanandam SN, Sumathi S, Deepa SN (2007) Introduction to fuzzy logic using matlab. Springer-Verlag, Berlin Heidelberg

    Book  Google Scholar 

  • Sladen JA, D’Hollander RD, Krahn J (1985) The liquefaction of sands, a collapse surface approach. Can Geotech J 22(4):564–578

    Article  Google Scholar 

  • Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300

    Article  Google Scholar 

  • Suykens J, Gestel JV, Brabanter JD, Moor BD, Vandewalle J (2002) Least square support vector machines. World Scientific Publishing Co. Pte. Ltd., Singapore

    Book  Google Scholar 

  • Tien Bui D, Pham BT, Nguyen QP, Hoang N-D (2016a) Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of least-squares support vector machines and differential evolution optimization: a case study in Central Vietnam. Int J Digit Earth. doi:10.1080/17538947.2016.1169561

  • Tien Bui D, Tuan TA, Hoang N-D, Thanh NQ, Nguyen DB, Van Liem N, Pradhan B (2016b) Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization. Landslides. doi:10.1007/s10346-016-0711-9

  • Tran T-H, Hoang N-D (2016) Predicting colonization growth of algae on mortar surface with artificial neural network. J Comput Civil Eng. doi10.1061/(ASCE)CP.1943-5487.0000599

  • Venkatesh K, Kumar V, Tiwari RP (2013) Appraisal of liquefaction potential using neural network and neuro fuzzy approach. Appl Artif Intell 27(8):700–720

    Article  Google Scholar 

  • Xue X, Yang X (2013) Application of the adaptive neuro-fuzzy inference system for prediction of soil liquefaction. Nat Hazards 67(2):901–917

    Article  Google Scholar 

  • Xue X, Yang X (2016) Seismic liquefaction potential assessed by support vector machines approaches. Bull Eng Geol Environ 75(1):153–162

    Article  Google Scholar 

  • Yazdi JS, Kalantary F, Yazdi HS (2013) Investigation on the effect of data imbalance on prediction of liquefaction. Int J Geomech 13(4):463–466

    Article  Google Scholar 

  • Youd TL, Idriss IM, Andrus RD, Arango I, Castro G, Christian JT, Dobry R, Finn WDL, Leslie F, Harder J, Hynes ME, Ishihara K, Koester JP, Liao SSC, Marcuson WFI, Martin GR, Mitchell JK, Moriwaki Y, Power MS, Robertson PK, Seed RB, Stokoe KHI (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 127(10):817–833

    Article  Google Scholar 

  • Zhang W, Goh ATC (2016) Evaluating seismic liquefaction potential using multivariate adaptive regression splines and logistic regression. Geomech Eng 10(3):269–280

    Article  Google Scholar 

  • Zhang W, Goh ATC, Zhang Y, Chen Y, Xiao Y (2015) Assessment of soil liquefaction based on capacity energy concept and multivariate adaptive regression splines. Eng Geol 188:29–37

    Article  Google Scholar 

  • Zhao H-B, Ru Z-L, Yin S (2007) Updated support vector machine for seismic liquefaction evaluation based on the penetration tests. Mar Georesour Geotech 25(3–4):209–220

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Research and Development, Faculty of Civil Engineering, Duy Tan University, P809-K7/25 Quang Trung, Danang, Vietnam

    Nhat-Duc Hoang

  2. Geographic Information System Group, Department of Business Administration and Computer Science, University College of Southeast Norway, Hallvard Eikas Plass, 3800, Bø i, Telemark, Norway

    Dieu Tien Bui

Authors
  1. Nhat-Duc Hoang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dieu Tien Bui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nhat-Duc Hoang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hoang, ND., Bui, D.T. Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: a multi-dataset study. Bull Eng Geol Environ 77, 191–204 (2018). https://doi.org/10.1007/s10064-016-0924-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10064-016-0924-0

Keywords

Search

Navigation