Evaluating Adaptive Neuro-Fuzzy Inference System (ANFIS) To Assess Liquefaction Potential And Settlements Using CPT Test Data

Document Type : Research Note

Authors

1 Mtech Student, Dr. B.R. Ambedkar National Institute of Technology Jalandhar, Jalandhar, Punjab, India

2 Assistant Professor, Dr. B.R. Ambedkar National Institute of Technology Jalandhar, Jalandhar, Punjab, India

10.22115/scce.2022.345237.1456

Abstract

Liquefaction occurs when saturated, non-cohesive soil loses strength. This phenomenon occurs as the water pressure in the pores rises and the effective stress drops because of dynamic loading. Liquefaction potential is a ratio for the factor of safety used to figure out if the soil can be liquefied, and liquefaction-induced settlements happen when the ground loses its ability to support construction due to liquefaction. Traditionally, empirical and semi-empirical methods have been used to predict liquefaction potential and settlements that are based on historical data. In this study, MATLAB's Fuzzy Tool Adaptive Neuro-Fuzzy Inference System (ANFIS) (sub-clustering) was used to predict liquefaction potential and liquefaction-induced settlements. Using Cone Penetration Test (CPT) data, two ANFIS models were made: one to predict liquefaction potential (LP-ANFIS) and the other to predict liquefaction-induced settlements (LIS-ANFIS). The RMSE correlation for the LP-ANFIS model (input parameters: Depth, Cone penetration, Sleeve Resistance, and Effective stress; output parameters: Liquefaction Potential) and the LIS-ANFIS model (input parameters: Depth, Cone penetration, Sleeve Resistance, and Effective stress; output parameters: Settlements) was 0.0140764 and 0.00393882 respectively. The Coefficient of Determination (R2) for both the models was 0.9892 and 0.9997 respectively. Using the ANFIS 3D-Surface Diagrams were plotted to show the correlation between the CPT test parameters, the liquefaction potential, and the liquefaction-induced settlements. The ANFIS model results displayed that the considered soft computing techniques have good capabilities to determine liquefaction potential and liquefaction-induced settlements using CPT data.

Keywords

Main Subjects


[1]     Zhang G, Robertson PK, Brachman RWI. Estimating liquefaction-induced ground settlements from CPT for level ground. Can Geotech J 2002;39:1168–80. doi:10.1139/t02-047.
[2]     Park S-S, Ogunjinmi PD, Woo S-W, Lee D-E. A Simple and Sustainable Prediction Method of Liquefaction-Induced Settlement at Pohang Using an Artificial Neural Network. Sustainability 2020;12:4001. doi:10.3390/su12104001.
[3]     Tang X-W, Hu J-L, Qiu J-N. Identifying significant influence factors of seismic soil liquefaction and analyzing their structural relationship. KSCE J Civ Eng 2016;20:2655–63. doi:10.1007/s12205-016-0339-2.
[4]     Juwaied NS. APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN GEOTECHNICAL ENGINEERING 2018;13:2764–85.
[5]     Park H Il. Study for Application of Artificial Neural Networks in Geotechnical Problems. In: Hui CLP, editor. Artif. Neural Networks, Rijeka: IntechOpen; 2011. doi:10.5772/15011.
[6]     Rahman MS, Wang J. Fuzzy neural network models for liquefaction prediction 2002;22:685–94.
[7]     Kayadelen C. Expert Systems with Applications Soil liquefaction modeling by Genetic Expression Programming and Neuro-Fuzzy. Expert Syst Appl 2011;38:4080–7. doi:10.1016/j.eswa.2010.09.071.
[8]     Ramakrishnan D, Singh TN, Gupta S. Artificial neural network and liquefaction susceptibility assessment : a case study using the 2001 Bhuj earthquake data , Gujarat , India 2012. doi:10.1007/s10596-008-9088-8.
[9]     Xue X, Yang X. Application of the adaptive neuro-fuzzy inference system for prediction of soil liquefaction 2013:901–17. doi:10.1007/s11069-013-0615-0.
[10]   Ghani S, Kumari S. Evaluation of Liquefaction Based on Hybrid Soft Computing Technique and Conventional Method 2021.
[11]   Karkh B, Engineering C, Technology A, Engineering C, Technology A. Neuro-Fuzzy Technique for the Estimation of Liquefaction Potential of Soil 2014;03:617–23.
[12]   Sharafi H, Jalili S. Assessment of Cyclic Resistance Ratio ( CRR ) in Silty Sands Using Artificial Neural Networks 2014:217–28.
[13]   Venkatesh K, Kumar V, Tiwari RP. APPRAISAL OF LIQUEFACTION POTENTIAL USING NEURAL NETWORK AND NEURO FUZZY APPROACH 2013;9514. doi:10.1080/08839514.2013.823326.
[14]   Idriss IM, Boulanger RW. Semi-empirical procedures for evaluating liquefaction potential during earthquakes 2006;26:115–30. doi:10.1016/j.soildyn.2004.11.023.
[15]   Youd T, Hoose S. Historic ground failures in northern California triggered by earthquakes,. 1978.
[16]   Youd, T.L., Idriss, I.M., Andrus, R.D., Arango, I., Castro, G., Christian, J.T., Dobry, R., Finn, W.D.L., Harder Jr., L.F. Hynes, M.E., Ishihara, K., Koestor, J.P., Liao, S.S.C., Mar- cuson III, W.F., Martin, G.R., Mitchell, J.K., Moriwaki, Y., Power, M.S KH (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 Geoenvironmental En- Gineering, ASCE, 127(10), 817-833 2001.
[17]   Juang CH, Ching J, Wang L, Khoshnevisan S, Ku C-S. Simplified procedure for estimation of liquefaction-induced settlement and site-specific probabilistic settlement exceedance curve using cone penetration test (CPT). Can Geotech J 2013;50:1055–66. doi:10.1139/cgj-2012-0410.
[18]   Ishihara K, Yoshimine M. Evaluation of Settlements in Sand Deposits Following Liquefaction During Earthquakes. Soils Found 1992;32:173–88. doi:10.3208/sandf1972.32.173.
[19]   Oberhollenzer S, Marte R, Oberhollenzer S, Premstaller M, Marte R. Cone penetration test dataset Premstaller Geotechnik Cone penetration test dataset Premstaller Geotechnik. Data Br 2020;34:106618. doi:10.1016/j.dib.2020.106618.
[20]   Ghorbani B, Sadrossadat E, Bolouri Bazaz J, Rahimzadeh Oskooei P. Numerical ANFIS-Based Formulation for Prediction of the Ultimate Axial Load Bearing Capacity of Piles Through CPT Data. Geotech Geol Eng 2018;36:2057–76. doi:10.1007/s10706-018-0445-7.
[21]   Jang JSR. ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Trans Syst Man Cybern 1993;23:665–85. doi:10.1109/21.256541.
[22]   M. Samhouri , A. Al-Ghandoor , S. Alhaj Ali , I. Hinti WM a. An Intelligent Machine Condition Monitoring System Using Time-Based Analysis: Neuro-Fuzzy Versus Neural Network. Jordan J Mech Ind Eng 2009;3:294–305.
[23]   Keshavarz Z, Torkian H. Application of ANN and ANFIS Models in Determining Compressive Strength of Concrete ARTICLE INFO ABSTRACT. J Soft Comput Civ Eng 2018;2:62–70.
[24]   Emami H, Emami S. Application of whale optimization algorithm combined with adaptive neuro-fuzzy inference system for estimating suspended sediment load. J Soft Comput Civ Eng 2021;5:1–14. doi:10.22115/SCCE.2021.281972.1300.
[25]   Heddam S, Bermad A, Dechemi N. ANFIS-based modelling for coagulant dosage in drinking water treatment plant: a case study. Environ Monit Assess 2012;184:1953–71. doi:10.1007/s10661-011-2091-x.
  • Receive Date: 01 June 2022
  • Revise Date: 02 August 2022
  • Accept Date: 01 October 2022
  • First Publish Date: 01 October 2022