Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

Abstract

This study investigates the performance of four machine learning (ML) algorithms to evaluate the earthquake-induced liquefaction potential of soil based on the cone penetration test field case history records using the Bayesian belief network (BBN) learning software Netica. The BBN structures that were developed by ML algorithms-K2, hill climbing (HC), tree augmented naive (TAN) Bayes, and Tabu search were adopted to perform parameter learning in Netica, thereby fixing the BBN models. The performance measure indexes, namely, overall accuracy (OA), precision, recall, F-measure, and area under the receiver operating characteristic curve, were used to evaluate the training and testing BBN models’ performance and highlight the capability of the K2 and TAN Bayes models over the Tabu search and HC models. The sensitivity analysis results showed that the cone tip resistance and vertical effective stress are the most sensitive factors, whereas the mean grain size is the least sensitive factor in the prediction of seismic soil liquefaction potential. The results of this study can provide theoretical support for researchers in selecting appropriate ML algorithms and improving the predictive performance of seismic soil liquefaction potential models.

Graphical abstract

Keywords

seismic soil liquefaction / Bayesian belief network / cone penetration test / parameter learning / structural learning

References

[1] Seed H B, Idriss I M. Simplified procedure for evaluating soil liquefaction potential. Journal of the Soil Mechanics and Foundations Division, 1971, 97(9): 1249–1273

[2] Daftari A. New approach in prediction of soil liquefaction. Dissertation for the Doctoral Degree. Freiberg, New York: Technische Universität Bergakademie Freiberg, 2015

[3] Hamdia K M, Ghasemi H, Zhuang X, Alajlan N, Rabczuk T. Computational machine learning representation for the flexoelectricity effect in truncated pyramid structures. Computers, Materials & Continua, 2019, 59(1): 79–87

[4] Anitescu C, Atroshchenko E, Alajlan N, Rabczuk T. Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials & Continua, 2019, 59(1): 345–359

[5] Guo H, Zhuang X, Rabczuk T. A deep collocation method for the bending analysis of Kirchhoff plate. Computers, Materials & Continua, 2019, 59(2): 433–456

[6] Goh A T. Neural-network modeling of CPT seismic liquefaction data. Journal of Geotechnical Engineering, 1996, 122(1): 70–73

[7] Goh A T. Seismic liquefaction potential assessed by neural networks. Journal of Geotechnical Engineering, 1994, 120(9): 1467–1480

[8] Pal M. Support vector machines-based modelling of seismic liquefaction potential. International Journal for Numerical and Analytical Methods in Geomechanics, 2006, 30(10): 983–996

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

[10] Kohestani V, Hassanlourad M, Ardakani A. Evaluation of liquefaction potential based on CPT data using random forest. Natural Hazards, 2015, 79(2): 1079–1089

[11] Hu J L, Tang X W, Qiu J N. A Bayesian network approach for predicting seismic liquefaction based on interpretive structural modeling. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 2015, 9(3): 200–217

[12] Kaveh A, Hamze-Ziabari S, Bakhshpoori T. Patient rule-induction method for liquefaction potential assessment based on CPT data. Bulletin of Engineering Geology and the Environment, 2018, 77(2): 849–865

[13] Hoang N D, 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. Bulletin of Engineering Geology and the Environment, 2018, 77(1): 191–204

[14] Zhou J, Li E, Wang M, Chen X, Shi X, Jiang L. Feasibility of stochastic gradient boosting approach for evaluating seismic liquefaction potential based on SPT and CPT case histories. Journal of Performance of Constructed Facilities, 2019, 33(3): 04019024

[15] Kecman V. Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models. Cambridge, MA: MIT press, 2001

[16] Park D, Rilett L R. Forecasting freeway link travel times with a multilayer feedforward neural network. Computer-Aided Civil and Infrastructure Engineering, 1999, 14(5): 357–367

[17] Juang C H, Yuan H, Lee D H, Lin P S. Simplified cone penetration test-based method for evaluating liquefaction resistance of soils. Journal of Geotechnical and Geoenvironmental Engineering, 2003, 129(1): 66–80

[18] Moss R, Seed R B, Kayen R E, Stewart J P, Der Kiureghian A, Cetin K O. CPT-based probabilistic and deterministic assessment of in situ seismic soil liquefaction potential. Journal of Geotechnical and Geoenvironmental Engineering, 2006, 132(8): 1032–1051

[19] Pearl J. Probabilistic reasoning in intelligent systems: Representation & reasoning. San Mateo, CA: Morgan Kaufmann Publishers, 1988

[20] Cooper G F, Herskovits E. A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 1992, 9(4): 309–347

[21] Buntine W. A guide to the literature on learning probabilistic networks from data. IEEE Transactions on Knowledge and Data Engineering, 1996, 8(2): 195–210

[22] Bouckaert R R. Bayesian belief networks: From construction to inference. Dissertation for the Doctoral Degree. Utrecht: Utrecht University, 1995

[23] Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers. Machine Learning, 1997, 29(2–3): 131–163

[24] Chow C, Liu C. Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory, 1968, 14(3): 462–467

[25] Spiegelhalter D J, Lauritzen S L. Sequential updating of conditional probabilities on directed graphical structures. Networks, 1990, 20(5): 579–605

[26] Lauritzen S L. The EM algorithm for graphical association models with missing data. Computational Statistics & Data Analysis, 1995, 19(2): 191–201

[27] Stark T D, Olson S M. Liquefaction resistance using CPT and field case histories. Journal of Geotechnical Engineering, 1995, 121(12): 856–869

[28] Tranfield D, Denyer D, Smart P. Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 2003, 14(3): 207–222

[29] Okoli C, Schabram K. A guide to conducting a systematic literature review of information systems research. Sprouts: Working Papers on Information Systems, 2010, 10(26): 1–49

[30] Zhang L. Predicting seismic liquefaction potential of sands by optimum seeking method. Soil Dynamics and Earthquake Engineering, 1998, 17(4): 219–226

[31] Hu J L, Tang X W, Qiu J N. Assessment of seismic liquefaction potential based on Bayesian network constructed from domain knowledge and history data. Soil Dynamics and Earthquake Engineering, 2016, 89(10): 49–60

[32] Ahmad M, Tang X W, Qiu J N, Ahmad F. Interpretive structural modeling and MICMAC analysis for identifying and benchmarking significant factors of seismic soil liquefaction. Applied Sciences (Basel, Switzerland), 2019, 9(2): 233

[33] Ahmad M, Tang X W, Qiu J N, Ahmad F. Evaluating seismic soil liquefaction potential using bayesian belief network and C4. 5 decision tree approaches. Applied Sciences (Basel, Switzerland), 2019, 9(20): 4226

[34] Ahmad M, Tang X, Qiu J, Gu W, Ahmad F. A hybrid approach for evaluating CPT-based seismic soil liquefaction potential using Bayesian belief networks. Journal of Central South University, 2020, 27(2): 500–516

[35] Li L, Wang J, Leung H, Jiang C. Assessment of catastrophic risk using Bayesian network constructed from domain knowledge and spatial data. Risk Analysis: An International Journal, 2010, 30(7): 1157–1175

[36] Tesfamariam S, Liu Z. Seismic risk analysis using Bayesian belief networks. In: Handbook of Seismic Risk Analysis and Management of Civil Infrastructure Systems. Cambridge, UK: Woodhead Publishing Limited, 2013, 175–208

[37] Witten I H, Frank E, Hall M. Data Mining: Practical Machine Learning Tools and Techniques. Burlington, MA: Elsevier, 2005

[38] Bradley A P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 1997, 30(7): 1145–1159

[39] Oommen T, Baise L G, Vogel R M. Sampling bias and class imbalance in maximum-likelihood logistic regression. Mathematical Geosciences, 2011, 43(1): 99–120

[40] Hamdia K M, Marino M, Zhuang X, Wriggers P, Rabczuk T. Sensitivity analysis for the mechanics of tendons and ligaments: Investigation on the effects of collagen structural properties via a multiscale modelling approach. International Journal for Numerical Methods in Biomedical Engineering, 2019, 35(8): e3209

[41] Ayad F, Bekkouche A, Houmadi Y. Sensitivity analysis of soil liquefaction potential. Earth-Science Reviews, 2014, 3(1): 14

[42] Pirhadi N, Tang X, Yang Q, Kang F. A new equation to evaluate liquefaction triggering using the response surface method and parametric sensitivity analysis. Sustainability, 2019, 11(1): 112

[43] Cheng J, Greiner R, Kelly J, Bell D, Liu W. Learning Bayesian networks from data: An information-theory based approach. Artificial Intelligence, 2002, 137(1–2): 43–90

Acknowledgements

The work presented in this paper was part of research sponsored by the National Key Research & Development Plan of China (Nos. 2018YFC1505305 and 2016YFE0200100) and the Key Program of the National Natural Science Foundation of China (No. 51639002).

RIGHTS & PERMISSIONS

2021 Higher Education Press