Development of fragility curve for railway embankment

Document Type : Research Paper

Authors

1 National Institute of Technology (NIT) Patna, Bihar, India.

2 Department of Civil and Environmental Engineering, University of Massachusetts Lowell, Lowell, United States.

10.22059/ijmge.2024.346021.594985

Abstract

For the construction of railway embankments, geotechnical engineers pay special attention to slope stability studies. The factor of safety values plays a crucial part in assessing the safe design of slopes. The factor of safety values is used to determine how close or far slopes are from failing due to natural or man-made causes. The factor of safety is a numeric value to indicate the relative stability, it doesn’t tell about the actual risk level of any structure, but the reliability index and probability of failure quantify the risk level. The present study discusses the findings of a study to determine the factor of safety of an embankment of height 12.3 m by using Geo-studio 2012 software. In this article, the fragility curve for six different types of cross-sections was also developed i.e. the graph between the probability of failure ( ) and horizontal seismic coefficient ( ), for various values of  (i.e. 0.1, 0.12, 0.144, 0.18, 0.2, 0.3, 0.4 and 0.5). It is observed from the developed fragility curve, as the  value increases  value decreases. A fragility curve can be used to calculate failure probability over a range of seismic zones, and for design purposes, a given seismic zone and probability of failure a unique reliable side slope is selected. Further, two machine learning (ML) models namely, Deep Neural Network (DNN) and Support Vector Regression (SVR) have been developed for the prediction of the factor of safety for different sides slope. Obtained correlation values (R) for SVR and DNN are approximately 0.95 and 0.82 respectively. From the help of the predicted factor of safety fragility curve against horizontal seismic coefficient is drawn for both SVR and DNN models, that for reducing the time of calculation and ease in working best result giving model will be suggested for further analysis of railway embankment.

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[1]      Porter K. A beginner’s guide to fragility, vulnerability, and risk. University of Colorado Boulder, 119 pp 2019.
[2]      Martinović K, Reale C, Gavin K. Fragility curves for rainfall-induced shallow landslides on transport networks. Can Geotech J 2018;55:852–61. https://doi.org/10.1139/cgj-2016-0565.
[3]      Wu XZ. Development of fragility functions for slope instability analysis: Fragility functions for slope instability analysis. Landslides 2015;12:165–75. https://doi.org/10.1007/s10346-014-0536-3.
[4]      Kennedy RP, Cornell CA, Campbell RD, Kaplan S, Perla HF. Probabilistic seismic safety study of an existing nuclear power plant. Nucl Eng Des 1980;59:315–38.
[5]      Gardoni P, Der Kiureghian A, Mosalam KM. Probabilistic capacity models and fragility estimates for reinforced concrete columns based on experimental observations. J Eng Mech 2002;128:1024–38.
[6]      Jeong S-H, Elnashai AS. Probabilistic fragility analysis parameterized by fundamental response quantities. Eng Struct 2007;29:1238–51.
[7]      Ebeling RM, Fong MT, Chase A, Arredondo E. Fragility analysis of a concrete gravity dam and its system response curve computed by the analytical program GDLAD_Sloping_Base 2008.
[8]      Vorogushyn S, Merz B, Apel H. Development of dike fragility curves for piping and micro-instability breach mechanisms. Nat Hazards Earth Syst Sci 2009;9:1383–401.
[9]      Schultz MT, Gouldby BP, Simm J. Beyond the factor of safety developing fragility curves to characterize system reliability 2010.
[10]    Kennedy RP, Cornell CA, Campbell RD, Kaplan S, Perla HF. Probabilistic seismic safety study of an existing nuclear power plant. Nucl Eng Des 1980;59:315–38. https://doi.org/10.1016/0029-5493(80)90203-4.
[11]    Jeong SH, Elnashai AS. Probabilistic fragility analysis parameterized by fundamental response quantities. Eng Struct 2007;29:1238–51. https://doi.org/10.1016/j.engstruct.2006.06.026.
[12]    Schultz MT, Gouldby BP, Simm JD, Wibowo JL. Beyond the Factor of Safety : Developing Fragility Curves to Characterize System Reliability-US Army Corps of Engineers 2010:51.
[13]    Fotopoulou SD, Pitilakis KD. Vulnerability assessment of reinforced concrete buildings subjected to seismically triggered slow-moving earth slides. Landslides 2013;10:563–82.
[14]    Wu XZ. Probabilistic slope stability analysis by a copula-based sampling method. Comput Geosci 2013;17:739–55.
[15]    Hasofer AM, Lind NC. Exact and invariant second-moment code format. J Eng Mech Div 1974;100:111–21.
[16]    Zahn JJ. Empirical failure criteria with correlated resistance variables. J Struct Eng 1990;116:3122–37.
[17]    Low BK, Tang WH. Efficient spreadsheet algorithm for first-order reliability method. J Eng Mech 2007;133:1378–87.
[18]    Dash, S.R. and Jain SK. IITK-GSDMA Guidelines for seismic design of buried pipelines: provisions with commentary and explanatory examples. National Information Center of Earthquake Engineering, Kanpur, India. Indian Inst Technol Kanpur 2007.
[19]    Frangopol DM. Probability concepts in engineering: emphasis on applications to civil and environmental engineering. vol. 4. John Wiley & Sons Incorporated; 2008. https://doi.org/10.1080/15732470802027894.
[20]    Chowdhury R, Flentje P. Role of slope reliability analysis in landslide risk management. Bull Eng Geol Environ 2003;62:41–6. https://doi.org/10.1007/s10064-002-0166-1.
[21]    Kumar DR, Samui P, Burman A, Kumar S. Seismically Induced Liquefaction Potential Assessment by Different Artificial Intelligence Procedures. Transp Infrastruct Geotechnol 2023:1–22.
[22]    Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep learning [http://www. deeplearningbook. org]. MIT Press Cambridge, MA 2016.
[23]    Kumar R, Kumar A, Ranjan Kumar D. Buckling response of CNT based hybrid FG plates using finite element method and machine learning method. Compos Struct 2023;319:117204. https://doi.org/10.1016/j.compstruct.2023.117204.
[24]    Kumar DR, Samui P, Wipulanusat W, Keawsawasvong S, Sangjinda K, Jitchaijaroen W. Soft Computing Techniques for Predicting Penetration and Uplift Resistances of Dual Pipelines in Cohesive Soils. Eng Sci 2023. https://doi.org/10.30919/es897.
[25]    Kumar DR, Samui P, Burman A. Suitability assessment of the best liquefaction analysis procedure based on SPT data. Multiscale Multidiscip Model Exp Des 2023:1–11.
[26]    Kumar DR, Samui P, Wipulanusat W, Keawsawasvong S, Sangjinda K, Jitchaijaroen W. Soft-Computing Techniques for Predicting Seismic Bearing Capacity of Strip Footings in Slopes. Buildings 2023;13. https://doi.org/10.3390/buildings13061371.
[27]    Kumar DR, Samui P, Wipulanusat W, Keawsawasvong S, Sangjinda K, Jitchaijaroen W. Bearing Capacity of Eccentrically Loaded Footings on Rock Masses Using Soft Computing Techniques. Eng Sci 2023;24:929.
[28]    Kumar DR, Samui P, Burman A, Wipulanusat W, Keawsawasvong S. Liquefaction susceptibility using machine learning based on SPT data. Intell Syst with Appl 2023;20:200281. https://doi.org/10.1016/j.iswa.2023.200281.
[29]    Kumar M, Biswas R, Kumar DR, Samui P, Kaloop MR, Eldessouki M. Soft computing-based prediction models for compressive strength of concrete. Case Stud Constr Mater 2023;19:e02321.
[30]    Kumar M, Biswas R, Kumar DR, Pradeep T, Samui P. Metaheuristic models for the prediction of bearing capacity of pile foundation. Geomech Eng 2022;31:129–47. https://doi.org/10.12989/gae.2022.31.2.129.