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Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

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Abstract

Liquefaction-induced lateral displacement is responsible for considerable damage to engineered structures during major earthquakes. Therefore, an accurate estimation of lateral displacement in liquefaction-prone regions is an essential task for geotechnical experts for sustainable development. This paper presents a novel probabilistic framework for evaluating liquefaction-induced lateral displacement using the Bayesian belief network (BBN) approach based on an interpretive structural modeling technique. The BBN models are trained and tested using a wide-range case-history records database. The two BBN models are proposed to predict lateral displacements for free-face and sloping ground conditions. The predictive performance results of the proposed BBN models are compared with those of frequently used multiple linear regression and genetic programming models. The results reveal that the BBN models are able to learn complex relationships between lateral displacement and its influencing factors as cause-effect relationships, with reasonable precision. This study also presents a sensitivity analysis to evaluate the impacts of input factors on the lateral displacement.

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References

  1. Rezania M, Faramarzi A, Javadi A A. An evolutionary based approach for assessment of earthquake-induced soil liquefaction and lateral displacement. Engineering Applications of Artificial Intelligence, 2011, 24(1): 142–153

    Article  Google Scholar 

  2. Al Bawwab W M K. Probabilistic assessment of liquefaction-induced lateral ground deformations. Dissertation for the Doctoral Degree. Ankara: Middle East Technical University, 2005

    Google Scholar 

  3. Newmark N M. Effects of earthquakes on dams and embankments. Geotechnique, 1965, 15(2): 139–160

    Article  Google Scholar 

  4. Olson S M, Johnson C I. Analyzing liquefaction-induced lateral spreads using strength ratios. Journal of Geotechnical and Geoenvironmental Engineering, 2008, 134(8): 1035–1049

    Article  Google Scholar 

  5. Yegian M, Marciano E, Ghahraman V G. Earthquake-induced permanent deformations: Probabilistic approach. Journal of Geotechnical Engineering, 1991, 117(1): 35–50

    Article  Google Scholar 

  6. Baziar M H. Engineering evaluation of permanent ground deformations due to seismically induced liquefaction. Dissertation for the Doctoral Degree. New York: Rensselaer Polytechnic Institute, 1991

    Google Scholar 

  7. Towhata I, Sasaki Y, Tokida K I, Matsumoto H, Tamar Y, Yamada K. Prediction of permanent displacement of liquefied ground by means of minimum energy principle. Soil and Foundation, 1992, 32(3): 97–116

    Article  Google Scholar 

  8. Byrne P M, Beaty M. Liquefaction induced displacements, Seismic behaviour of ground and geotechnical structures. In: Proceeding of Discussion Special Technical Session on Earthquake Geotechnical Engineering during International Conference on Soil Mechanics and Foundation Engineering. Rotterdam: A.A. Balkema, 1997, 185–195

    Google Scholar 

  9. Hamada M, Sato H, Kawakami T. A Consideration of the Mechanism for Liquefaction-Related Large Ground Displacement. Technical Report NCEER-94–0026. 1994

  10. Soroush A, Koohi S. Liquefaction-induced lateral spreading—An overview and numerical analysis. International Journal of Civil Engineering, 2004, 2(4): 232–245

    Google Scholar 

  11. Finn W, Ledbetter R H, Wu G. Liquefaction in silty soils: Design and analysis. In: Ground failures under seismic conditions. Noe York: American Society of Civil Engineers Geotechnical, 1994

    Google Scholar 

  12. Baziar M, Ghorbani A. Evaluation of lateral spreading using artificial neural networks. Soil Dynamics and Earthquake Engineering, 2005, 25(1): 1–9

    Article  Google Scholar 

  13. Wang J, Rahman M A. Neural network model for liquefaction-induced horizontal ground displacement. Soil Dynamics and Earthquake Engineering, 1999, 18(8): 555–568

    Article  Google Scholar 

  14. Javadi A A, Rezania M, Nezhad M M. Evaluation of liquefaction induced lateral displacements using genetic programming. Computers and Geotechnics, 2006, 33(4–5): 222–233

    Article  Google Scholar 

  15. Javdanian H. Field data-based modeling of lateral ground surface deformations due to earthquake-induced liquefaction. European Physical Journal Plus, 2019, 134(6): 297

    Article  Google Scholar 

  16. Jafarian Y, Nasri E. Evaluation of uncertainties in the available empirical models and probabilistic prediction of liquefaction induced lateral spreading. Amirkabir Journal of Civil and Environmental Engineering, 2016, 48(3): 275–290

    Google Scholar 

  17. Youd T L, Hansen C M, Bartlett S F. Revised multilinear regression equations for prediction of lateral spread displacement. Journal of Geotechnical and Geoenvironmental Engineering, 2002, 128(12): 1007–1017

    Article  Google Scholar 

  18. Youd T L, Perkins D M. Mapping of liquefaction severity index. Journal of Geotechnical Engineering, 1987, 113(11): 1374–1392

    Article  Google Scholar 

  19. Rauch A F. EPOLLS: An empirical method for prediciting surface displacements due to liquefaction-induced lateral spreading in earthquakes. Dissertation for the Doctoral Degree. Blacksburg: Virginia Tech, 1997

    Google Scholar 

  20. Bardet J, Mace N, Tobita T. Liquefaction-induced Ground Deformation and Failure. A Report to PEER/PG&E. Task 4A-Phase 1. Los Angeles: University of Southern California, 1999

    Google Scholar 

  21. Hamada M, Yasuda S, Isoyama R, Emoto K. Study on Liquefaction Induced Permanent Ground Displacements. Report of Association for the Development of Earthquake Prediction. 1986

  22. 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

    Article  Google Scholar 

  23. 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

    Article  Google Scholar 

  24. Bardet J P, Liu F. Motions of gently sloping ground during earthquakes. Journal of Geophysical Research. Earth Surface, 2009, 114(F2): 1–13

    Google Scholar 

  25. Bartlett S F, Youd T L. Empirical Prediction of Lateral Spread Displacement. Technical Report NCEER, 1, 92-0019. 1992

  26. Bartlett S F, Youd T L. Empirical prediction of liquefaction-induced lateral spread. Journal of Geotechnical Engineering, 1995, 121(4): 316–329

    Article  Google Scholar 

  27. Chu D B, Stewart J P, Youd T L, Chu B. Liquefaction-induced lateral spreading in near-fault regions during the 1999 Chi-Chi, Taiwan earthquake. Journal of Geotechnical and Geoenvironmental Engineering, 2006, 132(12): 1549–1565

    Article  Google Scholar 

  28. Cetin K O, Youd T L, Seed R B, Bray J D, Stewart J P, Durgunoglu H T, Lettis W, Yilmaz M T. Liquefaction-induced lateral spreading at Izmit Bay during the Kocaeli (Izmit)-Turkey earthquake. Journal of Geotechnical and Geoenvironmental Engineering, 2004, 130(12): 1300–1313

    Article  Google Scholar 

  29. Youd T L, DeDen D W, Bray J D, Sancio R, Cetin K O, Gerber T M. Zero-displacement lateral spreads, 1999 Kocaeli, Turkey, earthquake. Journal of Geotechnical and Geoenvironmental Engineering, 2009, 135(1): 46–61

    Article  Google Scholar 

  30. Kanibir A. Investigation of the Lateral Spreading at Sapanca and Suggestion of Empirical Relationships for Predicting Lateral Spreading. Turkey: Department of Geological Engineering, Hacettepe University, 2003

    Google Scholar 

  31. Baziar M, Saeedi Azizkandi A. Evaluation of lateral spreading utilizing artificial neural network and genetic programming. International Journal of Civil Engineering Transaction B. Geotechnical Engineering, 2013, 11(2): 100–111

    Google Scholar 

  32. Pearl J. Probabilistic Reasoning in Intelligent Systems: Representation & Reasoning. San Mateo, CA: Morgan Kaufmann Publishers, 1988

    Google Scholar 

  33. Warfield J N. Developing interconnection matrices in structural modeling. IEEE Transactions on Systems, Man, and Cybernetics, 1974, SMC-4(1): 81–87

    Article  MathSciNet  Google Scholar 

  34. Mathiyazhagan K, Govindan K, NoorulHaq A, Geng Y. An ISM approach for the barrier analysis in implementing green supply chain management. Journal of Cleaner Production, 2013, 47: 283–297

    Article  Google Scholar 

  35. Sushil S. Interpreting the interpretive structural model. Global Journal of Flexible Systems Management, 2012, 13(2): 87–106

    Article  Google Scholar 

  36. Sadigh K, Chang C Y, Egan J, Makdisi F, Youngs R. Attenuation relationships for shallow crustal earthquakes based on California strong motion data. Seismological Research Letters, 1997, 68(1): 180–189

    Article  Google Scholar 

  37. Tang X W, Bai X, Hu J L, Qiu J N. Assessment of liquefaction-induced hazards using Bayesian networks based on standard penetration test data. Natural Hazards and Earth System Sciences, 2018, 18(5): 1451–1468

    Article  Google Scholar 

  38. 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

    Google Scholar 

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

    Article  Google Scholar 

  40. 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

    Google Scholar 

  41. Pfohl H C, Gallus P, Thomas D. Interpretive structural modeling of supply chain risks. International Journal of Physical Distribution & Logistics Management, 2011, 41(9): 839–859

    Article  Google Scholar 

  42. Saxena J, Sushil, Vrat P. Impact of indirect relationships in classification of variables—A micmac analysis for energy conservation. Systems Research, 1990, 7(4): 245–253

    Article  Google Scholar 

  43. Kuhn M, Johnson K. Applied Predictive Modeling. New York: Springer, 2013

    Book  MATH  Google Scholar 

  44. Landis J R, Koch G G. An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics, 1977, 33(2): 363–374

    Article  MATH  Google Scholar 

  45. Sakiyama Y, Yuki H, Moriya T, Hattori K, Suzuki M, Shimada K, Honma T. Predicting human liver microsomal stability with machine learning techniques. Journal of Molecular Graphics & Modelling, 2008, 26(6): 907–915

    Article  Google Scholar 

  46. Congalton R G, Green K. Assessing the accuracy of remotely sensed data: principles and practices. Boca Raton: CRC press, 2008

    Book  Google Scholar 

  47. Vu-Bac N, Lahmer T, Zhuang X, Nguyen-Thoi T, Rabczuk T. A software framework for probabilistic sensitivity analysis for computationally expensive models. Advances in Engineering Software, 2016, 100: 19–31

    Article  Google Scholar 

  48. 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

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

This study was part of research work sponsored by the National Key Research & Development Plan of China (Nos. 2018YFC 1505300-5.3 and 2016YFE0200100) and the Key Program of the National Natural Science Foundation of China (Grant No. 51639002).

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Authors and Affiliations

  1. State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116024, China

    Mahmood Ahmad & Xiao-Wei Tang

  2. Department of Civil Engineering, University of Engineering and Technology Peshawar, Bannu Campus, Bannu, 28100, Pakistan

    Mahmood Ahmad

  3. Faculty of Management and Economics, Dalian University of Technology, Dalian, 116024, China

    Jiang-Nan Qiu

  4. Department of Civil Engineering, Abasyn University, Peshawar, 25000, Pakistan

    Feezan Ahmad

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  1. Mahmood Ahmad
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  2. Xiao-Wei Tang
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  4. Feezan Ahmad
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Correspondence to Jiang-Nan Qiu.

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Ahmad, M., Tang, XW., Qiu, JN. et al. Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks. Front. Struct. Civ. Eng. 15, 80–98 (2021). https://doi.org/10.1007/s11709-021-0682-3

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