Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD

PDF(1198 KB)
PDF(1198 KB)
Front. Struct. Civ. Eng. ›› 2021, Vol. 15 ›› Issue (1) : 80-98. DOI: 10.1007/s11709-021-0682-3
TRANSDISCIPLINARY INSIGHT
TRANSDISCIPLINARY INSIGHT

Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

Author information +
History +

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.

Keywords

Bayesian belief network / seismically induced soil liquefaction / interpretive structural modeling / lateral displacement

Cite this article

Download citation ▾
Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD. Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks. Front. Struct. Civ. Eng., 2021, 15(1): 80‒98 https://doi.org/10.1007/s11709-021-0682-3

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
CrossRef 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
[3]
Newmark N M. Effects of earthquakes on dams and embankments. Geotechnique, 1965, 15(2): 139–160
CrossRef 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
CrossRef 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
CrossRef 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
[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
CrossRef 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
[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
[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
[12]
Baziar M, Ghorbani A. Evaluation of lateral spreading using artificial neural networks. Soil Dynamics and Earthquake Engineering, 2005, 25(1): 1–9
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
[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
CrossRef Google scholar
[18]
Youd T L, Perkins D M. Mapping of liquefaction severity index. Journal of Geotechnical Engineering, 1987, 113(11): 1374–1392
CrossRef 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
[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
[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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
[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
[32]
Pearl J. Probabilistic Reasoning in Intelligent Systems: Representation & Reasoning. San Mateo, CA: Morgan Kaufmann Publishers, 1988
[33]
Warfield J N. Developing interconnection matrices in structural modeling. IEEE Transactions on Systems, Man, and Cybernetics, 1974, SMC-4(1): 81–87
CrossRef 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
CrossRef Google scholar
[35]
Sushil S. Interpreting the interpretive structural model. Global Journal of Flexible Systems Management, 2012, 13(2): 87–106
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef Google scholar
[43]
Kuhn M, Johnson K. Applied Predictive Modeling. New York: Springer, 2013
[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
CrossRef 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
CrossRef Google scholar
[46]
Congalton R G, Green K. Assessing the accuracy of remotely sensed data: principles and practices. Boca Raton: CRC press, 2008
[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
CrossRef 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
CrossRef Google scholar

Acknowledgments

This study was part of research work sponsored by the National Key Research & Development Plan of China (Nos. 2018YFC1505300-5.3 and 2016YFE0200100) and the Key Program of the National Natural Science Foundation of China (Grant No. 51639002).

RIGHTS & PERMISSIONS

2021 Higher Education Press
AI Summary AI Mindmap
PDF(1198 KB)

Accesses

Citations

Detail

Sections
Recommended

/