A Novel Methodology to Classify Soil Liquefaction Using Deep Learning
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ORIGINAL PAPER
A Novel Methodology to Classify Soil Liquefaction Using Deep Learning Deepak Kumar
. Pijush Samui . Dookie Kim . Anshuman Singh
Received: 14 November 2018 / Accepted: 28 August 2020 Ó Springer Nature Switzerland AG 2020
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 D. Kumar (&) A. Singh Department of Civil Engineering, National Institute of Technology Patna, Ashok Raj Path, Patna 800005, India e-mail: [email protected] A. Singh e-mail: [email protected] P. Samui Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam e-mail: [email protected] P. Samui Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam D. Kim Department of Civil Engineering, Kunsan National University, Kunsan, Jeonbuk, South Korea e-mail: [email protected]
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. Keywords Liquefaction Deep learning Emotional backpropagation neural network Classification
1 Introduction Seismic hazards can cause enormous social and economic loss due to action of ground shaking, landslides, structural hazards, liquefaction, retaining structure failures, lifeline hazards and tsunamis. Amongst all aforementioned process, liquification of soil induced by earthquake has major contribution to loss both for human life and infrastructure. In geotechnical engineering, the assessment of soil liquefaction due to earthquake is an imperative task. The phenomena of liquefaction frequently found in alluvial filled terrain consisting favorable groundwater
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condition induced by earthquake due to monotonic shock loading action. Increased pore water pressure transform soil to viscous state due to cohesionless and negligible shear resistance lead to liquefaction process (Firoozi et al. 2016, 2017a, b;
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