Application of Monte Carlo Simulation Technique for Slopes Stabilized with Piles

Yesim Tuskan; Yusuf Erzin

doi:10.35414/akufemubid.1287644

Research Article

Kazıklarla Güçlendirilmiş Şevlerde Monte Carlo Simülasyon Yöntemi Uygulaması

Year 2024, Volume: 24 Issue: 1, 117 - 125, 27.02.2024

Yesim Tuskan , Yusuf Erzin

https://doi.org/10.35414/akufemubid.1287644

Abstract

Bu çalışmada, kazıklarla güçlendirilmiş bir şevin simülasyon modeli geliştirilmiştir. Manisa-İzmir Devlet Yolu (Türkiye) doğu kesiminde yer alan hasar görmüş bir şevin güvenlik sayısının (FS) kemerlenme etkisi göz önüne alınarak değerlendirilmesi için iki boyutlu sonlu elemanlar yöntemi (2D-FEM) kullanılmıştır. Ek olarak, güvenilirliğe dayalı bir tasarım yöntemi olan Monte Carlo Simülasyonu (MCS), deprem sırasında güçlendirilmiş şevlerin stabilitesini ve güçlendirilmiş şevlerin kayma olasılığını doğru bir şekilde tahmin etmek için kullanılmıştır. Olasılık ve istatistiksel teori bilgisi, önerilen problemi çözmek ve herhangi bir fiziksel test olmaksızın sayısal çözümler üretmek için deterministik çalışmalarda kullanılmaktadır. Geliştirilen MCS ve FEM modellerinin tahmin kapasitesini değerlendirmek için güvenilirlik indeksi ve yenilme olasılığı hesaplanmıştır. Son olarak, hesaplanan indisler hem geliştirilmiş MCS'nin hem de FEM'in heyelanın FS değerlerini oldukça verimli bir şekilde tahmin edebildiğini açıkça ortaya koymaktadır.

Keywords

şev stabilitesi, monte carlo simülasyonu, Güvenirlik Değerlendirmesi, sonlu elemanlar yöntemi

References

Al Bouder, W., 2010. Development of Design and Analysis Method for Slope Stabilization Using Drilled Shafts. Ph.D. Dissertation, University of Akron, Ohio, 215.
Blake, T.F., Hollingsworth, R.A. and Stewart, J.P., 2003. A Screen Analysis Procedure for Seismic Slope Stability, Earthquake Spectra, 19(3),697–712.
Caballero, W.L. and Rahman, A., 2014. Application of Monte Carlo simulation technique for flood estimation for two catchments in New South Wales, Australia, Natural Hazards, 74(3), 1475–1488.
Chen, Q., Wang, C. and Juang, C.H., 2016. Probabilistic and spatial assessment of liquefaction-induced settlements through multi-scale random field models, Engineering Geology, 211, 135-149.
Ching, J. and Wang, J.S., 2016. Application of the transitional Markov chain Monte Carlo algorithm to probabilistic site characterization, Engineering Geology, 203, 151-167.
Cui, L. and Sheng, D., 2005. Genetic algorithms in probabilistic finite element analysis of geotechnical problems, Computers and Geotechnics, 32(8), 555-563.
Dagli, B.Y., Tuskan, Y. and Gökkuş, Ü., 2018. Evaluation of offshore wind turbine tower dynamics with numerical analysis, Advances in Civil Engineering, 1-11.
Dagli, B.Y., Uncu, D. and Tuskan, Y., 2019. Deniz Boru Hattı Dinamik Davranışının Sonlu Elemanlar Yöntemi ile Analizi, Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 23(2), 404-410.
Erzin, Y. and Tuskan, Y., 2016. Prediction of Standard Penetration Test (SPT) Value in Izmir, Turkey using General Regression Neural Network. International Conference on Agricultural, Civil and Environmental Engineering (ACEE-16) April, 18-19.
Erzin, Y. and Tuskan, Y., 2017. Prediction of standard penetration test (SPT) value in Izmir, Turkey using radial basis neural network, Celal Bayar University Journal of Science, 13(2), 433-439.
Erzin, Y. and Tuskan, Y., 2019. The use of neural networks for predicting the factor of safety of soil against liquefaction, Scientia Iranica, 26(5), 2615-2623.
Jiang, S.H., Li, D.Q., Zhang, L.M. and Zhou, C.B., 2014. Slope reliability analysis considering spatially variable shear strength parameters using a non-intrusive stochastic finite element method, Engineering Geology, 168, 120-128.
Kulhawy, F.H. and Mayne, P.W., 1990. Manual on estimating soil properties for foundation design, Electric Power Research Institute (EPRI) Palo Alto, CA (USA); Cornell Univ., Ithaca, NY (USA). Geotechnical Engineering Group, 25-36.
Liang, R. and Zeng, S., 2002. Numerical study of soil arching mechanism in drilled shafts for slope stabilization, Soils and Foundations, 42(2), 83-92.
Liang, R.Y., Joorabchi, A.E. and Li, L., 2014. Analysis and design method for slope stabilization using a row of drilled shafts, Journal of Geotechnical and Geo-environmental Engineering, 140(5), 1–12.
Li, T.L., Long, J.H. and Li, X.S., 2007. Types of loess landslides and methods for their movement forecast, Engineering Geology, 15(4), 500–506.
Li, S., Zhao, H.B. and Ru, Z., 2013. Slope reliability analysis by updated support vector machine and Monte Carlo simulation, Natural Hazards, 65(1), 707–722
Li, T.L., Wang, C.Y. and Li, P., 2013. Loess deposit and loess landslides on the Chinese loess plateau, Progress of geo-disaster mitigation technology in Asia, 235-261
Li, Z., Huang, H. and Xue, Y., 2014. Cut-slope versus shallow tunnel: Risk-based decision-making framework for alternative selection, Engineering Geology, 176, 11–23.
Li, J.H., Zhou, Y., Zhang, L.L., Tian, Y., Cassidy, M.J. and Zhang, L.M., 2016. Random finite element method for spud can foundations in spatially variable soils, Engineering Geology, 205, 146-155.
MathWorks, Neural Network Toolbox 7.0., 2010. MathWorks Announces Release 2010a of the MATLAB and Simulink Product Families, MathWorks Inc.
Mohammadi, S. and Taiebat, H., 2016. Finite element simulation of an excavation-triggered landslide using large deformation theory, Engineering Geology, 205, 62-72.
Murthy, K., 2000. Monte Carlo: Basics, Monte Carlo: Basics. arXiv preprint cond-mat/0104215, Chapter, 9. Nowak, A. and Collins, K., 2000. Reliability of Structures First edition, McGraw Hill Higher Education, USA. Phoon, K.K., 2017. Role of reliability calculations in geotechnical design. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 11(1), 4-21.
Saghafian, B., Golian, S., Elmi, M. and Akhtari, R., 2013. Monte Carlo analysis of the effect of spatial distribution of storms on prioritization of flood source areas, Natural Hazards, 66(2), 1059–1071.
U.S. EPA., 1997. Ecological risk assessment guidance for Superfund: process for designing and conducting ecological risk assessments, Interim Final. Washington, DC: Office of Solid Waste and Emergency Response. EPA.
Uzielli, M., Lacasse, S., Nadim, F. and Lunne, T., 2006. Uncertainty-based Characterization of Troll Marine Clay, Characterization and Engineering Properties of Natural Soils, Eds. T. S. Tan, K. K. Phoon, D. W. Hight & S. Leroueil, Taylor & Francis, Leiden, 4, 2753-2782.
Vanmarcke, E.H., 1977. Probabilistic modeling of soil profiles, Journal of the Geotechnical Engineering Division, 103(11), 1227–1246.
Wang, J.P., Lin, C.W., Taheri, H. and Chan, W.S., 2012. Impact of fault parameter uncertainties on earthquake recurrence probability examined by Monte Carlo simulation an example in Central Taiwan, Engineering Geology, 126, 67-74.
Wang, X.G., Jia, Z.X., Chen, Z.Y. and Xu, Y., 2016. Determination of discontinuity persistent ratio by Monte-Carlo simulation and dynamic programming, Engineering Geology, 203, 83-98.
Xiao, J., Luo, Z., Martin II, J.R., Gong, W. and Wang, L., 2016. Probabilistic geotechnical analysis of energy piles in granular soils, Engineering Geology, 209, 119–127.
Yamin, M.M., 2007. Landslide stabilization using a single row of rock-socketed drilled shafts and analysis of laterally loaded shafts using shaft deflection data." Ph.D. Dissertation, University of Akron, Ohio, 335.
Yazdani, A. and Kowsari, M., 2017. A probabilistic procedure for scenario-based seismic hazard maps of Greater Tehran, Engineering Geology, 218, 162-172.
Yildizel, S.A., Tuskan, Y. and Kaplan, G., 2017. Prediction of skid resistance value of glass fiber-reinforced tiling materials, Advances in Civil Engineering, 2017.
Zhou, G., Esaki, T., Mitani, Y., Xie, M. and Mori, J., 2003. Spatial probabilistic modeling of slope failure using an integrated GIS Monte Carlo simulation approach, Engineering Geology, 68, 373-386