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Liquefaction resistance evaluation of soils using artificial neural network for Dhaka City, Bangladesh

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Abstract

Soil liquefaction resistance evaluation is an important site investigation for seismically active areas. To minimize the loss of life and property, liquefaction hazard analysis is a prerequisite for seismic risk management. Liquefaction potential index (LPI) is widely used to determine the severity of liquefaction quantitatively and spatially. LPI is estimated from the factor of safety of liquefaction that is the ratio of cyclic resistance ratio (CRR) to cyclic stress ratio calculated applying simplified procedure. Artificial neural network (ANN) algorithm has been used in the present study to predict CRR directly from the normalized standard penetration test blow count (SPT-N) and near-surface shear wave velocity (Vs) data of Dhaka City. It is observed that ANN models have generated accurate CRR data. Three liquefaction hazard zones are identified in Dhaka City on the basis of the cumulative frequency (CF) distribution of the LPI of each geological unit. The liquefaction hazard maps have been prepared for the city using the liquefaction potential index (LPI) and its cumulative frequency (CF) distribution of each liquefaction hazard zone. The CF distribution of the SPT-N based LPI indicates that 15%, 53%, and 69% of areas, whereas the CF distribution of the Vs based LPI indicates that 11%, 48%, and 62% of areas of Zone 1, 2, and 3, respectively, show surface manifestation of liquefaction for an earthquake of moment magnitude, Mw 7.5 with a peak horizontal ground acceleration of 0.15 g.

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References

  • Agrawal G, Chameau J, Bourdeau P (1997) Assessing the Liquefaction Susceptibility at a Site Based on Information from Penetration Testing. undefined

  • Aitchison JC, Ali JR, Davis AM (2007) When and where did India and Asia collide? J Geophys Res 112:1978–2012

    Google Scholar 

  • Alam M (1989) Geology and depositional history of cenozoic sediments of the Bengal Basin of Bangladesh. Palaeogeogr Palaeoclimatol Palaeoecol 69:125–139. https://doi.org/10.1016/0031-0182(89)90159-4

    Article  Google Scholar 

  • Ali HE, Najjar YM (1998) Neuronet-based approach for assessing liquefaction potential of soils. Transport Res Rec 1633(1):3–8

    Article  Google Scholar 

  • Ambraseys NN, Douglas J (2004) Magnitude calibration of north Indian earthquakes. Geophys J Int 159:165–206. https://doi.org/10.1111/j.1365-246X.2004.02323.x

    Article  Google Scholar 

  • Andrus RD, Stokoe KH (1997) Liquefaction resistance based on shear wave velocity. In: Proceeding of NCEER workshop on evaluation of liquefaction resistance of soils. National Center for Earthquake Engineering Research, Sate University of New York, Buffalo, pp 89–128

  • Andrus BRD, Member A, Ii KHS (2000) Liquefaction resistance of soils from shear-wave velocity. J Geotech Geoenviron Eng 126:1015–1025

    Article  Google Scholar 

  • Andrus RD, Stokoe KH, Chung RM (1999) NISTIR6277 Draft guidelines for evaluating liquefaction resistance using shear wave velocity measurements and simplified procedures

  • Ateş A, Keskin I, Totiç E, Yeşil B (2014) Investigation of soil liquefaction potential around efteni lake in Duzce Turkey: Using empirical relationships between shear wave velocity and SPT blow count (N). Adv Mater Sci Eng. https://doi.org/10.1155/2014/290858

    Article  Google Scholar 

  • Beale MH, Hagan MT, Demuth HB (2017) Neural Network Toolbox TM User’s Guide.

  • Castro G, Poulos SJ, France JW, Enos JL (1982) Liquefaction induced by cyclic loading. Report by Geotechnical Engineers Inc., to the National Science Foundation, Washington, D.C

  • Castro G, Poulos SJ (1977) Factors affecting liquefaction and cyclic mobility. J Geotech Engng Div 103:501–516

    Article  Google Scholar 

  • Castro G (1969) Liquefaction of Sands. PhD Thesis. Harvard University, Cambridge.

  • Chao SJ, Hsu HM, Hwang H (2010) Soil liquefaction potential in Ilan City and Lotung Town. Taiwan J GeoEng 5:21–27. https://doi.org/10.6310/jog.2010.5(1).3

    Article  Google Scholar 

  • Chen CJ, Juang CH (2000) Calibration of SPT- and CPT-based liquefaction evaluation methods. In: Mayne P, Hryciw R (eds) Innovations and applications in geotechnical site characterization 97. Geotechnical special publication, ASCE, Reston, pp 49–64

    Chapter  Google Scholar 

  • Curray JR, Emmel FJ, Moore DG, Raitt RW (1982) Structure, tectonics, and geological history of the northeastern Indian Ocean. The ocean basins and margins. Springer, Berlin, pp 399–450

    Chapter  Google Scholar 

  • Eberhart RC, Dobbins RW, Widrow B (1990) Neural network PC tools: a practical guide. Academic Press, Cambridge, p 431

    Google Scholar 

  • Fear CE, McRoberts EC (1995) Report on liquefaction potential and catalogue of case records. Univ of Alberta, Edmonton

    Google Scholar 

  • Flood I, Kartam N (1994) Neural networks in civil engineering. i: principles and understanding. J Comput Civ Eng 8:131–148. https://doi.org/10.1061/(ASCE)0887-3801(1994)8:2(131)

    Article  Google Scholar 

  • Goda K, Kiyota T, Pokhrel RM et al (2015) The 2015 Gorkha Nepal earthquake: insights from earthquake damage survey. Front Built Environ 1:1–15. https://doi.org/10.3389/fbuil.2015.00008

    Article  Google Scholar 

  • Goh ATC (1994) Seismic Liquefaction potential assessed by neural networks. J Geotech Eng 120:1467–1480. https://doi.org/10.1061/(ASCE)0733-9410(1994)120:9(1467)

    Article  Google Scholar 

  • Goh ATC (1995) Back-propagation neural networks for modeling complex systems. Artif Intell Eng 9:143–151. https://doi.org/10.1016/0954-1810(94)00011-S

    Article  Google Scholar 

  • Goh ATC (1996) Neural-Network Modeling of CPT Seismic Liquefaction Data. Journal of Geotechnical Engineering 122:70–73. https://doi.org/10.1061/(ASCE)0733-9410(1996)122:1(70)

    Article  Google Scholar 

  • Hamada M, Isoyama R, Wakamatsu K (1995) The 1995 Hyogoken-Nanbu Kobe earthquake-Liquefaction, ground displacement, and soil condition in the Hanshin area. Waseda University, Tokyo

    Google Scholar 

  • Hammerstrom D (1993a) Neural networks at work. IEEE Spectr 30:26–32. https://doi.org/10.1109/6.214579

    Article  Google Scholar 

  • Hammerstrom D (1993b) Working with neural networks. IEEE Spectr 30:46–53. https://doi.org/10.1109/6.222230

    Article  Google Scholar 

  • Holzer TL, Bennett MJ, Noce TE et al (2006) Liquefaction hazard mapping with LPI in the greater Oakland, California, area. Earthq Spectra 22:693–708

    Article  Google Scholar 

  • Hossain MS, Kamal ASMM, Rahman MZ et al (2020) Assessment of soil liquefaction potential: a case study for Moulvibazar town, Sylhet. Bangladesh SN Appl Sci. https://doi.org/10.1007/s42452-020-2582-x

    Article  Google Scholar 

  • Idriss IM, Boulanger RW (2004) Semi-empirical procedures for evaluating liquefaction potential during earthquakes. In: 11th International Conference on Soil Dynamics and Earthquake Engineering, and 3rd International Conf. on Earthquake Geotechnical Engineering. Berkeley, pp 32–56

  • Idriss IM, Boulanger RW (2010) SPT-based liquefaction triggering procedure. University of California, Davis

    Google Scholar 

  • Iwasaki T, Tatsuoka F, Tokida K -i., Yasuda S (1978) A practical method for assessing soil liquefaction potential based on case studies at various sites in Japan. In: Proc. of 2nd International Conference on Microzonation. San Francisco, pp 885–896

  • Iwasaki T, Tokida K, Tatsuoka F, et al (1982) Microzonation for soil liquefaction potential using simplified methods. In: Proceedings of 3rd International Earthquake Microzonation Conference. pp 1319–1330

  • Juang CH, Chen CJ (1999) CPT-based liquefaction evaluation using artificial neural networks. Comput-Aided Civil Infrastruct Eng 14:221–229. https://doi.org/10.1111/0885-9507.00143

    Article  Google Scholar 

  • Juang CH, Chen CJ, Jiang T, Andrus RD (2000) Risk-based liquefaction potential evaluation using standard penetration tests. Can Geotech J 37:1195–1208. https://doi.org/10.1139/t00-064

    Article  Google Scholar 

  • Juang CH, Chen CJ, Jiang T (2001) Probabilistic framework for liquefaction potential by shear wave velocity. J Geotech Geoenviron Eng 127:670–678. https://doi.org/10.1061/(ASCE)1090-0241(2001)127:8(670)

    Article  Google Scholar 

  • Juang CH, Jiang T, Andrus RD (2002) Assessing probability-based methods for liquefaction potential evaluation. J Geotech Geoenviron Eng 128:580–589

    Article  Google Scholar 

  • 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. https://doi.org/10.1061/(ASCE)1090-0241(2003)129:1(66)

    Article  Google Scholar 

  • Kayen RE, Mitchell JK, Seed RB, et al (1992) Evaluation of SPT-, CPT-, and shear wave-based methods for liquefaction potential assessment using Loma Prieta data. In: Proceedings of 4th Japan-U.S. Workshop on Earthquake-Resistant Des. of Lifeline Fac. and Countermeasures for Soil Liquefaction. pp 177–204

  • Krogh A (2008) What are artificial neural networks? Nat Biotechnol 26:195–197

    Article  Google Scholar 

  • Ku CS, Lee DH, Wu JH (2004) Evaluation of soil liquefaction in the Chi-Chi, Taiwan earthquake using CPT. Soil Dyn Earthq Eng 24:659–673. https://doi.org/10.1016/j.soildyn.2004.06.009

    Article  Google Scholar 

  • Lee YF, Chi YY, Lee DH et al (2007) Simplified models for assessing annual liquefaction probability—a case study of the Yuanlin area. Taiwan Eng Geol 90:71–88. https://doi.org/10.1016/j.enggeo.2006.12.003

    Article  Google Scholar 

  • Lippmann RP (1987) An Introduction to computing with neural nets. IEEE ASSP Mag 4:4–22. https://doi.org/10.1109/MASSP.1987.1165576

    Article  Google Scholar 

  • Marcuson WF (1978) Definition of terms related to liquefaction. J Geotech Eng Div 104:1197–1200

    Article  Google Scholar 

  • Mayne PW, Coop MR, Springman SM, et al (2009) Geomaterial behavior and testing

  • Middlemiss CS (1885) Report on the Bengal earthquake of July 14, 1885. Rec Geologi Surv India 8(4):200–221

    Google Scholar 

  • Morgan JP, McIntire WG (1959) Quaternary geology of the Bengal Basin, East Pakistan and India. Bull Geol Soc Am 70:319–342

    Article  Google Scholar 

  • Morino M, Kamal ASMM, Muslim D et al (2011) Seismic event of the Dauki Fault in 16th century confirmed by trench investigation at Gabrakhari Village, Haluaghat, Mymensingh, Bangladesh. J Asian Earth Sci 42:492–498. https://doi.org/10.1016/j.jseaes.2011.05.002

    Article  Google Scholar 

  • Morino M, Kamal ASMM, Akhter SH et al (2014a) A paleo-seismological study of the Dauki fault at Jaflong, Sylhet, Bangladesh: historical seismic events and an attempted rupture segmentation model. J Asian Earth Sci 91:218–226. https://doi.org/10.1016/j.jseaes.2014.06.002

    Article  Google Scholar 

  • Morino M, Monsur MH, Kamal ASMM et al (2014b) Examples of paleo-liquefaction in Bangladesh. J Geol Soc Jpn. https://doi.org/10.5575/geosoc.2014.0032

    Article  Google Scholar 

  • Oldham RD (1899) Report on the great earthquake of 12th June 1897. Mem Geologi Surv India 29:1–379

    Google Scholar 

  • Olsen RS, Koester JP (1995) Prediction of liquefaction resistance using the CPT. In: Proceedings of the International Symposium on Cone Penetration Testing, CPT’95, Linkoping, Sweden, Vol. 2. SGS. pp 251–256

  • Olsen RS (1988) Using the CPT for dynamic response characterization. In: Proceedings of the Earthquake Engineering and Soil Dynamics II Conference. American Society of Civil Engineers, New York, pp 111–117

  • 1997Olsen RS (1997) Cyclic liquefaction based on the cone penetration test. In: Proceeding of NCEER workshop on evaluation of liquefaction resistance of soils. National Center for Earthquake Engineering Research, State University of New York, Buffalo, pp 225–276

  • Rahman MZ, Siddiqua S (2017a) Evaluation of liquefaction-resistance of soils using standard penetration test, cone penetration test, and shear-wave velocity data for Dhaka, Chittagong, and Sylhet cities in Bangladesh. Environ Earth Sc 76:207. https://doi.org/10.1007/s12665-017-6533-9

    Article  Google Scholar 

  • Rahman MZ, Siddiqua S (2017b) Evaluation of liquefaction-resistance of soils using standard penetration test, cone penetration test, and shear-wave velocity data for Dhaka, Chittagong, and Sylhet cities in Bangladesh. Environ Earth Sci 76:1–14. https://doi.org/10.1007/s12665-017-6533-9

    Article  Google Scholar 

  • Rahman M, Siddiqua S, Kamal A (2015) Liquefaction hazard mapping by liquefaction potential index for Dhaka City, Bangladesh. Eng Geol 188:137–147. https://doi.org/10.1016/j.enggeo.2015.01.012

    Article  Google Scholar 

  • Rahman MZ, Siddiqua S, Kamal ASMM (2020) Seismic source modeling and probabilistic seismic hazard analysis for Bangladesh. Springer, Netherlands

    Book  Google Scholar 

  • Rahman MZ, Siddiqua S, Kamal ASMM (2021) Site response analysis for deep and soft sedimentary deposits of Dhaka City. Natural Hazards, Bangladesh. https://doi.org/10.1007/s11069-021-04543-w

    Book  Google Scholar 

  • Rahman Z, Siddiqua S (2016) Liquefaction resistance evaluation of soils using standard penetration test blow count and shear wave velocity liquefaction resistance evaluation of soils using standard penetration test blow count and shear wave velocity

  • Reimann K-U (1993) Geology of Bangladesh. Gebruder Borntraeger Verlagsbuchhandlung Science Publishers, Berlin

    Google Scholar 

  • Robertson PK, Campanella RG (1985) Liquefaction potential of sands using the CPT. J Geotech Eng 111:384–403

    Article  Google Scholar 

  • Robertson PK, Wride CE (1998) Evaluating cyclic liquefaction potential using the cone penetration test. Can Geotech J 35:442–459. https://doi.org/10.1139/t98-017

    Article  Google Scholar 

  • Rumelhart DE, McClelland JL, Group the PR (1988) Parallel distributed processing, volume 1 explorations in the microstructure of cognition: foundations. Bradf Book 1:576

    Google Scholar 

  • Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature. https://doi.org/10.1038/323533a0

    Article  Google Scholar 

  • Sassa S, Takagawa T (2019) Liquefied gravity flow-induced tsunami: first evidence and comparison from the 2018 Indonesia Sulawesi earthquake and tsunami disasters. Landslides 16:195–200. https://doi.org/10.1007/s10346-018-1114-x

    Article  Google Scholar 

  • Seed HB, Idriss IM (1967) Analysis of Soil liquefaction: Niigata Earthquake. J Soil Mech Found Division 93:83–108

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Seed HB, Idriss IM (1982) Ground motions and soil liquefaction during earthquakes. Earthquake Engineering Research Institute Monograph, Oakland

    Google Scholar 

  • Seed HB, Idriss IM, Arango I (1983) Evaluation of liquefaction potential using field performance data. J Geotech Eng 109:458–482

    Article  Google Scholar 

  • Seed HB, Tokimatsu K, Harder LF, Chung RM (1985) Influence of SPT procedures in soil liquefaction resistance evaluations. J Geotech Eng 111:1425–1445. https://doi.org/10.1061/(ASCE)0733-9410(1985)111:12(1425)

    Article  Google Scholar 

  • Seed HB, de Alba P (1986) Use of SPT and CPT tests for evaluating the liquefaction resistance of sands. In: Clemence SP (ed) Use of in situ tests in geotechnical engineering. Geotechnical Special Publication 6, Houston, pp 281–302

    Google Scholar 

  • Seed HB, Tokimatsu K, Harder Jr. LF, Chung R (1984) The Influence of SPT procedures on soil liquefaction resistance evaluations. Report No. UCB/EERC-84/15, Earthquake Engineering Research Center, University of California, Berkeley

  • Sonmez H, Gokceoglu C (2005) A liquefaction severity index suggested for engineering practice. Environ Geol 48:81–91. https://doi.org/10.1007/s00254-005-1263-9

    Article  Google Scholar 

  • Stark TD, Olson SM (1995) Liquefaction resistance using CPT and field case histories. J Geotech Eng ASCE 121:856–869

    Article  Google Scholar 

  • Steckler MS, Akhter SH, Seeber L (2008) Collision of the Ganges-Brahmaputra Delta with the Burma Arc: Implications for earthquake hazard. Earth Planet Sci Lett 273:367–378. https://doi.org/10.1016/j.epsl.2008.07.009

    Article  Google Scholar 

  • Steckler MS, Mondal DR, Akhter SH et al (2016) Locked and loading megathrust linked to active subduction beneath the Indo-Burman Ranges. Nat Geosci 9:615–618. https://doi.org/10.1038/ngeo2760

    Article  Google Scholar 

  • Stuart M (1920) The Srimangal earthquake of 8th July 1918. Mem Geo Surv India 46(1):1–70

    Google Scholar 

  • Tu JV (1996) Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol 49:1225–1231

    Article  Google Scholar 

  • Wang Y, Sieh K, Tun ST et al (2014) Active tectonic and earthquake Myanmar region. J Geophys Res Solid Earth 119:3767–3822. https://doi.org/10.1002/2013JB010762.Received

    Article  Google Scholar 

  • Yeats RS, Sieh K, Allen CR (1997) The geology of earthquakes. Oxford University Press

    Google Scholar 

  • Youd BTL, Idriss IM, Andrus RD 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

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank the Comprehensive Disaster Management Programme (CDMP), Department of Disaster Science and Climate Resilience (DSCR), University of Dhaka, Bangladesh for providing the support to collect the data of this research. The authors are also thankful to the University of Dhaka for allowing them to conduct this research.

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  1. Department of Disaster Science and Climate Resilience, University of Dhaka, Dhaka, 1000, Bangladesh

    Abul Kashem Faruki Fahim, Md. Zillur Rahman, Md. Shakhawat Hossain & A. S. M. Maksud Kamal

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  1. Abul Kashem Faruki Fahim
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Fahim, A.K.F., Rahman, M.Z., Hossain, M.S. et al. Liquefaction resistance evaluation of soils using artificial neural network for Dhaka City, Bangladesh. Nat Hazards 113, 933–963 (2022). https://doi.org/10.1007/s11069-022-05331-w

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