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A Novel Methodology to Classify Soil Liquefaction Using Deep Learning

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Abstract

In this research, deep learning (DL) model is proposed to classify the soil reliability for liquefaction. The applicability of the DL model is tested in comparison with emotional backpropagation neural network (EmBP). The database encompassing cone penetration test of Chi–Chi earthquake. This study uses cone resistance (qc) and peck ground acceleration as inputs for prediction of liquefaction susceptibility of soil. The performance of developed models has been assessed by using various parameters (receiver operating characteristic, sensitivity, specificity, Phi correlation coefficient, Precision–Recall F measure). The performance of DL is excellent. Consistent results obtained from the proposed deep learning model, compared to the EmBP, indicate the robustness of the methodology used in this study. In addition, both the developed model was also tested on global earthquake data. During validation on global data, both the models shows good results based on fitness parameters. The developed classification models a simple, but also efficient decision-making tool in engineering design to quantitatively assess the liquefaction potential. The finding of this paper can be further used to capture the relationship between soil and earthquake parameters.

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References

  • Amodei D et al (2006) Deep speech 2: end-to-end speech recognition in english and mandarin. In: International conference on machine learning, 2016, pp 173–182

  • Boulanger RW, Idriss I (2015) CPT-based liquefaction triggering procedure. J Geotech Geoenviron Eng 142:04015065

    Article  Google Scholar 

  • Çetin KÖ (2000) Reliability-based assessment of seismic soil liquefaction initiation hazard, vol 2. University of California, Berkeley

    Google Scholar 

  • Chollet F (2017) Deep learning with python. Manning Publications Co., New York

    Google Scholar 

  • Das SK, Muduli PK (2011) Evaluation of liquefaction potential of soil using genetic programming. In: Proceedings of the golden jubilee indian geotechnical conference, Kochi, India, 2011, pp 827–830

  • Elkahky AM, Song Y, He X (2015) A multi-view deep learning approach for cross domain user modeling in recommendation systems. In: Proceedings of the 24th international conference on world wide web, 2015, pp 278–288

  • Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27:861–874

    Article  Google Scholar 

  • Firoozi AA, Firoozi AA, Baghini MS (2016) A Review of Clayey Soils. Asian J Appl Sci 4

  • Firoozi AA, Firoozi AA, Baghini MS (2017a) A review of physical and chemical clayey. J Civ Eng Urban 6:64–71

    Google Scholar 

  • Firoozi AA, Olgun CG, Firoozi AA, Baghini MS (2017b) Fundamentals of soil stabilization. Int J Geo Eng 8:26

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    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 27:521–540

    Article  Google Scholar 

  • Hirschberg J, Manning CD (2015) Advances in natural language processing. Science 349:261–266

    Article  Google Scholar 

  • Idriss I, Boulanger R (2010) SPT-based liquefaction triggering procedures Rep UCD/CGM-10 2

  • Ishii Y, Tsuchida H, Furube T (1963) Study on earth pressures and pore-water pressures of saturated sands during vibration. Report of Port and Harbour Research Institute Google Scholar

  • Iwasaki T (1978) A practical method for assessing soil liquefaction potential based on case studies at various sites in Japan. In: Proceedings of the second international conference on microzonation safer construction research application, 1978, 1978, pp 885–896

  • Iwasaki T, Tokida K, Tatsuoka F, Watanabe S, Yasuda S, Sato H (1982) Microzonation for soil liquefaction potential using simplified methods. In: Proceedings of the 3rd international conference on microzonation, Seattle, 1982, pp 1310–1330

  • Ji S, Xu W, Yang M, Yu K (2012) 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35:221–231

    Article  Google Scholar 

  • Jiang GQ, Xu J, Wei J (2018) A deep learning algorithm of neural network for the parameterization of typhoon-ocean feedback in typhoon forecast models. Geophys Res Lett 45:3706–3716

    Article  Google Scholar 

  • Juang CH, Ching J, Luo Z, Ku C-S (2012) New models for probability of liquefaction using standard penetration tests based on an updated database of case histories. Eng Geol 133:85–93

    Article  Google Scholar 

  • Kecman V (2001) Learning and soft computing: support vector machines, neural networks, and fuzzy logic models. MIT press

  • Khashman A (2008) A modified backpropagation learning algorithm with added emotional coefficients. IEEE Trans Neural Netw 19:1896–1909

    Article  Google Scholar 

  • Kishida H (1966) Damage to reinforced concrete buildings in Niigata city with special reference to foundation engineering. Soils Found 6:71–88

    Article  Google Scholar 

  • Koizumi Y (1966) Changes in density of sand subsoil caused by the Niigata earthquake. Soils Found 6:38–44

    Article  Google Scholar 

  • Ku C-S, Lee D-H, Wu J-H (2004) Evaluation of soil liquefaction in the Chi-Chi, Taiwan earthquake using CPT. Soil Dyn Earthq Eng 24:659–673

    Article  Google Scholar 

  • Kumar D, Singh A, Samui P, Jha RK (2019) Forecasting monthly precipitation using sequential modelling. Hydrol Sci J 64:690–700

    Article  Google Scholar 

  • Liao SS, Veneziano D, Whitman RV (1988) Regression models for evaluating liquefaction probability. J Geotech Eng 114:389–411

    Article  Google Scholar 

  • Lv Y, Duan Y, Kang W, Li Z, Wang F-Y (2014) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 16:865–873

    Google Scholar 

  • Mogami T, Kubo K (1953) The behavior of soil during vibration procedure. In: Proceedings of the 3rd international conference on soil mechanical and foundation. Zurich 1953, pp 152–153

  • Mollamahmutoglu M, Kayabali K, Beyaz T (1999) Kolay E (2003) Liquefaction-related building damage in Adapazari during the Turkey earthquake of August 17. Eng Geol 67:297–307

    Article  Google Scholar 

  • Njock PGA, Shen S-L, Zhou A, Lyu H-M (2020) Evaluation of soil liquefaction using AI technology incorporating a coupled ENN/t-SNE model. Soil Dyn Earthq Eng 130:105988

    Article  Google Scholar 

  • Ohsaki Y (1966) Niigata earthquakes, 1964 building damage and soil condition. Soils Found 6:14–37

    Article  Google Scholar 

  • Park D, Rilett LR (1999) Forecasting freeway link travel times with a multilayer feedforward neural network. Comput-Aided Civil Infrastruct Eng 14:357–367

    Article  Google Scholar 

  • Robertson P, Wride C (1998) Evaluating cyclic liquefaction potential using the cone penetration test. Can Geotech J 35:442–459

    Article  Google Scholar 

  • Rumelhart DE, Hinton GE, Williams RJ (1985) Learning internal representations by error propagation. California Univ San Diego La Jolla Inst for Cognitive Science

  • Sahoo BB, Jha R, Singh A, Kumar D (2019) Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series forecasting. Acta Geophys 67:1471–1481

    Article  Google Scholar 

  • Samui P, Sitharam T (2011) Machine learning modelling for predicting soil liquefaction susceptibility. Nat Hazards Earth Syst Sci 11:1–9

    Article  Google Scholar 

  • Scher S (2018) Toward data-driven weather and climate forecasting: approximating a simple general circulation model with deep learning. Geophys Res Lett 45:12, 616–612, 622

  • Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Article  Google Scholar 

  • Seed HB (1982) Ground motions and soil liquefaction during earthquakes. Earthquake engineering research institute

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

    Article  Google Scholar 

  • Seed HB, Idriss IM (1971) Simplified procedure for evaluating soil liquefaction potential. J Soil Mech Found Div

  • Seed HB, Peacock WH (1971) Test procedures for measuring soil liquefaction characteristics. J Soil Mech Found Div

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

    Article  Google Scholar 

  • Sladen J, D’hollander R, Krahn J (1985) The liquefaction of sands, a collapse surface approach. Canad Geotech J 22:564–578

    Article  Google Scholar 

  • Taha M, Firoozi A (2012) Estimating the clay cohesion by means of artificial intelligence technique. J Asian Sci Res 2:651–657

    Google Scholar 

  • Tsuchida H, Hayashi S (1972) Estimation of liquefaction potential of sandy soils. Publication of: Mcgraw Hill Book Company 14

  • Ye L, Gao L, Marcos-Martinez R, Mallants D, Bryan BA (2019) Projecting Australia’s forest cover dynamics and exploring influential factors using deep learning. Environ Model Softw 119:407–417

    Article  Google Scholar 

  • Yilmaz I, Bagci A (2006) Soil liquefaction susceptibility and hazard map** in the residential area of Kütahya (Turkey). Environ Geol 49:708–719

    Article  Google Scholar 

  • Youd TL, Idriss IM (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 127:297–313

    Article  Google Scholar 

  • Zhang N, Shen S-L, Zhou A, Xu Y-S (2019) Investigation on performance of neural networks using quadratic relative error cost function IEEE. Access 7:106642–106652

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Civil Engineering, National Institute of Technology Patna, Ashok Raj Path, Patna, 800005, India

    Deepak Kumar & Anshuman Singh

  2. Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Pijush Samui

  3. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Pijush Samui

  4. Department of Civil Engineering, Kunsan National University, Kunsan, Jeonbuk, South Korea

    Dookie Kim

Authors
  1. Deepak Kumar
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  2. Pijush Samui
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  3. Dookie Kim
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  4. Anshuman Singh
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Correspondence to Deepak Kumar.

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Kumar, D., Samui, P., Kim, D. et al. A Novel Methodology to Classify Soil Liquefaction Using Deep Learning. Geotech Geol Eng 39, 1049–1058 (2021). https://doi.org/10.1007/s10706-020-01544-7

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