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Modelling Results Of Liquefaction Tests On A Nonplastic Silt

Year 2020, Volume: 5 Issue: 2, 96 - 122, 30.12.2020

Abstract

Liquefaction is simply defined as the sudden incre-ase in pore water pressure as a result of cyclic loa-ding, which causes excessive ground settlements and damage in overlying or buried structures. Many studies in literature focused on evaluation of liquefaction susceptibility. These methods can be classified as a) stress-based b) strain based c) energy based methods d) numerical modelling ba-sed analysis and e) Arias-intensity based methods. In this study, an alternative approach for assess-ment of liquefaction susceptibility is aimed by use of clustering algorithms. In this regard, results of 54 cyclic triaxial tests results on a nonplastic silt inc-luding post-liquefaction volume changes was used for evaluation and classification of liquefaction be-havior after cyclic triaxial testing. In the experimen-tal part, specimens prepared at increasing relative densities between 30% and 80%, at a step of 10% were consolidated under 100 kPa effective confi-ning pressure. The results revealed that, unsupervi-sed clustering algorithms are reliable tools in clas-sification of liquefaction state and post-liquefaction volumetric strains of nonplastic silts. In this regard, cyclic stress ratio and number of cycles are useful inputs for classification of liquefaction state. In ad-dition, cyclic stress ratio, number of cycles, pore water pressure ratio and cyclic axial strain can be used to classify the post-liquefaction volumetric strains. Use of ANFIS depending on combinations of cyclic stress ratio, number of cycles, pore water pressure ratio, initial relative density and cyclic axial strain end up with promising results in esti-mation of post-liquefaction volumetric strain, however, these parameters are far from modelling factor of safety to liquefaction.

References

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Nonplastik Silt Üzerinde Gerçekleştirilen Sıvılaşma Deneylerinin Modellenmesi

Year 2020, Volume: 5 Issue: 2, 96 - 122, 30.12.2020

Abstract

Sıvılaşma, çevrimsel yükleme sırasında boşluksu-yu basıncında meydana gelen ani yükselmesi so-nucunda zeminin kayma dayanımında kayda de-ğer düşüşlerin ve buna bağlı olarak yüksek düzey-lerde oturmalar ve üst yapıda da hasarın meydana geldiği bir olgudur. Literatürde zeminlerin sıvıla-şabilirlikleri a) gerilme tabanlı b) deformasyon ta-banlı c) energy tabanlı d) sıvılaşma bünye modelle-rinin kullanıldığı sayısal modelleme teknikleri ve e) Arias yoğunluğu yöntemleri ile belirlenebilmekte-dir. Bu çalışmada, sıvılaşabilirliğin belirlenebilme-si için sınıflandırma (clustering) algoritmaları kul-lanılmıştır. Bu amaçla, non-plastik silt üzerinde yapılmış olan 54 tane dinamik üç eksenli basınç deneyinin sonuçları kullanılmıştır. Bu deney so-nuçları sıvılaşma sonrası oturma verilerini de kap-samaktadır. Deneyler, sıkılığı 30% ile 80% arasında 10% aralıklarla değişen ve 100 kPa altında konso-lide edilmiş non-plastik silt örnekleri üzerinde ger-çekleştirilmiştir. Analiz sonuçlarına göre, eğitmen-siz sınıflandırma algoritmaları, non-plastik silt örneklerinin sıvılaşabilirliklerinin ve sıvılaşma sonrası hacimsel birim deformasyonlarının belir-lenmesinde etkili bir teknik olarak karşımıza çık-maktadır. Buna göre, çevrimsel gerilme oranı ve çevrim sayısı parametrelerinin sıvılaşma durumu-nun belirlenmesinde uygun girdiler olduğu görül-mektedir. Öte yandan, çevrimsel gerilme oranı, çev-rim sayısı, aşırı boşluksuyu basıncı oranı, çevrim-sel eksenel birim deformasyon parametrelerinin ise sıvılaşma sonrası hacimsel birim deformasyonla-rın sınıflandırılmasında etkili parametreler olduk-ları görülmektedir. Çevrimsel gerilme oranı, çevrim sayısı, aşırı boşluksuyu basıncı oranı, sıkılık ve çevrimsel eksenel birim deformasyon parametrele-rinin kombinasyonundan oluşan ANFIS’in kulla-nımı sıvılaşma sonrası hacimsel birim deformas-yonların tahmin edilmesinde kayda değer bir rol oynarken, bu parametreler sıvılaşmaya karşı güven sayısının tahmininde zayıf kalmaktadırlar.

References

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  • ASTM D4254-16, Standard Test Methods for Minimum Index Density and Unit Weight of Soils and Calculation of Relative Density, ASTM International, West Conshohocken, PA, 2016b, www.astm.org
  • Bezdek JC. (1981). Pattern recognition with fuzzy objective function algorithms. New York: Plenum.
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  • Boulanger RW. (2003). High overburden stress effects in liquefaction analyses. J. GeotechGeoenviron. 129, pp.1071–82.
  • Boulanger, R. W., & Idriss, I. M. (2014). CPT and SPT based liquefaction triggering procedures. Report No. UCD/CGM.-14, 1.
  • Carraro, JAH, Bandini, P, Salgado, R. (2003). Liquefaction resistance of clean and nonplastic silty sands based on cone penetration resistance. J. Geotech. Geoenviron. 129:965–76.
  • Chen, YC, and Liao, TS. (1999). Studies of the state parameter and liquefaction resistance of sand. In: Proceedings of the 2nd international conference on earthquake geotechnical engineering, Lisbon Portugal. p. 513–518.
  • Dobry, R., Ladd, R. S., Yokel, F. Y., Chung, R. M., & Powell, D. (1982). Prediction of pore water pressure buildup and liquefaction of sands during earthquakes by the cyclic strain method (Vol. 138). Gaithersburg, MD: National Bureau of Standards.
  • Fattahi, H. (2016). Indirect estimation of deformation modulus of an in situ rock mass: an ANFIS model based on grid partitioning, fuzzy c-means clustering and subtractive clustering. Geosciences Journal, 20(5), 681-690.
  • Gan, G., Ma, C., & Wu, J. (2007). Data clustering: theory, algorithms, and applications. Society for Industrial and Applied Mathematics.
  • Gokceoglu, C. A. N. D. A. N., Sonmez, H., & Kayabasi, A. (2003). Predicting the deformation moduli of rock masses. International Journal of Rock Mechanics and Mining Sciences, 40(5), 701-710.
  • Goktepe, A. B., Altun, S., & Sezer, A. (2005). Soil clustering by fuzzy c-means algorithm. Advances in Engineering Software, 36(10), 691-698.
  • Goktepe, A. B. , İnan Sezer, G., Sezer, A., & Ramyar, K., (2007). Determination of sulfate resistance level of cements using unsupervised clustering algorithms. Cement And Concrete World, vol.12, 44-55.
  • Hanesch, M., Scholger, R., & Dekkers, M. J. (2001). The application of fuzzy c-means cluster analysis and non-linear mapping to a soil data set for the detection of polluted sites. Physics and Chemistry of the Earth, Part A: Solid Earth and Geodesy, 26(11-12), 885-891. Haykin, S., 1999. Neural Networks. Prentice-Hall, New Jersey, NY.
  • Hot E, Popović-Bugarin V (2016) “Soil data clustering by using K-means and fuzzy K-means algorithm”, Telfor Journal, 8(1), 56-61.
  • Hyde AF, Higuchi T, Yasuhara K. (2007). Postcyclic recompression, stiffness, and consolidated cyclic strength of silt. J GeotechGeoenviron Eng. 133(4):416–23.
  • Idriss, I. M., & Boulanger, R. W. (2008). Soil liquefaction during earthquakes. Earthquake Engineering Research Institute.
  • Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
  • Jang, J. S. R., Sun, C. T., &Mizutani, E. (1997). Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]. IEEE Transactions on automatic control, 42(10), 1482-1484.
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  • JGS 0541-2000, 2000b. Method for cyclic undrained triaxial tests on soils. Japanese Geotechnical Society.
  • Jokar, M. H., & Mirasi, S. (2018). Using adaptive neuro-fuzzy inference system for modeling unsaturated soils shear strength. Soft Computing, 22(13), 4493-4510.
  • Jurko, J, Sassa, K, Fukuoka, H. (2006). Dynamic behavior of gentle silty slopes based on undrained cyclic shear test. In:Marui Hideaki, editor. Disaster mitigation of debris flows, slope failure sand landslides. Tokyo, Japan: Universal Academy Press, Inc. pp.411–420.
  • Karakan, E., Tanrınian, N., & Sezer, A. (2018a). Evaluation of Excess Pore Water Pressure Build-up During Cyclic Triaxial Tests on a Non-plastic Silt. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 6(2), 513-524.
  • Karakan, E., Sezer, A., Tanrınian, N., & Altun, S. (2018b). Behavior of a dense nonplastic silt under cyclic loading. Anadolu Üniversitesi Bilim Ve Teknoloji Dergisi-B Teorik Bilimler, 6, 183-191.
  • Karakan, E., Tanrinian, N., & Sezer, A. (2019a). Cyclic undrained behavior and post liquefaction settlement of a nonplastic silt. Soil Dynamics and Earthquake Engineering, 120, 214-227.
  • Karakan, E., Sezer, A., & Tanrinian, N. (2019b). Evaluation of effect of limited pore water pressure development on cyclic behavior of a nonplastic silt. Soils and Foundations, 59(5), 1302-1312.
  • Kayadelen, C., Günaydın, O., Fener, M., Demir, A., & Özvan, A. (2009). Modeling of the angle of shearing resistance of soils using soft computing systems. Expert Systems with Applications, 36(9), 11814-11826.
  • Kayen, R. E., & Mitchell, J. K. (1997). Assessment of liquefaction potential during earthquakes by Arias intensity. Journal of Geotechnical and Geoenvironmental Engineering, 123(12), 1162-1174.
  • Kenny TC. (1977). Residual strength of mineral textures. Proceedings of the 9th ICSMFE, Tokyo, vol. 1.; p. 155–60.
  • Kohonen, T., Oja, E., Simula, O., Visa, A., &Kangas, J. (1996). Engineering applications of the self-organizing map. Proceedings of the IEEE, 84(10), 1358-1384.
  • Kohonen T. (2001) Learning Vector Quantization. In: Self-Organizing Maps. Springer Series in Information Sciences, vol 30. Springer, Berlin, Heidelberg
  • Liu, L., Moayedi, H., Rashid, A. S. A., Rahman, S. S. A., & Nguyen, H. (2020). Optimizing an ANN model with genetic algorithm (GA) predicting load-settlement behaviours of eco-friendly raft-pile foundation (ERP) system. Engineering with Computers, 36(1), 421-433.
  • Lu, L.; Liu, C.; Li, X.; Ran, Y. (2017). Mapping the Soil Texture in the Heihe River Basin Based on Fuzzy Logic and Data Fusion. Sustainability, 9, 1246.
  • McQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (Vol. 1, No. 14, pp. 281-297).
  • Moayedi, H and Rezai A. 2020. The feasibility of PSO–ANFIS in estimating bearing capacity of strip foundations rested on cohesionless slope. Neural Computing and Applications, https://doi.org/10.1007/s00521-020-05231-9.
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There are 74 citations in total.

Details

Primary Language English
Journal Section Makaleler
Authors

Devrim Erdoğan 0000-0003-3525-9031

Nazar Tanrınıan This is me

Alper Sezer

Eyyüb Karakan

Publication Date December 30, 2020
Published in Issue Year 2020 Volume: 5 Issue: 2

Cite

APA Erdoğan, D., Tanrınıan, N., Sezer, A., Karakan, E. (2020). Modelling Results Of Liquefaction Tests On A Nonplastic Silt. Mühendislik Ve Yer Bilimleri Dergisi, 5(2), 96-122.
AMA Erdoğan D, Tanrınıan N, Sezer A, Karakan E. Modelling Results Of Liquefaction Tests On A Nonplastic Silt. MYBD - JEES. December 2020;5(2):96-122.
Chicago Erdoğan, Devrim, Nazar Tanrınıan, Alper Sezer, and Eyyüb Karakan. “Modelling Results Of Liquefaction Tests On A Nonplastic Silt”. Mühendislik Ve Yer Bilimleri Dergisi 5, no. 2 (December 2020): 96-122.
EndNote Erdoğan D, Tanrınıan N, Sezer A, Karakan E (December 1, 2020) Modelling Results Of Liquefaction Tests On A Nonplastic Silt. Mühendislik ve Yer Bilimleri Dergisi 5 2 96–122.
IEEE D. Erdoğan, N. Tanrınıan, A. Sezer, and E. Karakan, “Modelling Results Of Liquefaction Tests On A Nonplastic Silt”, MYBD - JEES, vol. 5, no. 2, pp. 96–122, 2020.
ISNAD Erdoğan, Devrim et al. “Modelling Results Of Liquefaction Tests On A Nonplastic Silt”. Mühendislik ve Yer Bilimleri Dergisi 5/2 (December 2020), 96-122.
JAMA Erdoğan D, Tanrınıan N, Sezer A, Karakan E. Modelling Results Of Liquefaction Tests On A Nonplastic Silt. MYBD - JEES. 2020;5:96–122.
MLA Erdoğan, Devrim et al. “Modelling Results Of Liquefaction Tests On A Nonplastic Silt”. Mühendislik Ve Yer Bilimleri Dergisi, vol. 5, no. 2, 2020, pp. 96-122.
Vancouver Erdoğan D, Tanrınıan N, Sezer A, Karakan E. Modelling Results Of Liquefaction Tests On A Nonplastic Silt. MYBD - JEES. 2020;5(2):96-122.