Derin Kazıların Olasılıksal Analizi Üzerinde Düşey Konumsal Değişkenliğin Etkisi: Bir Vaka Çalışması

Yıl 2023, Cilt: 26 Sayı: 3, 1233 - 1242, 01.10.2023

Dursun Enes Korkut , Sami Oğuzhan Akbaş

https://doi.org/10.2339/politeknik.1324358

Cited By: 1

Öz

Bu çalışmada, derin kazıların olasılıksal analizinde kilin efektif içsel sürtünme açısının düşey konumsal değişkenliğinin dahil edilmesinin etkisi araştırılmaktadır. Önerilen metodoloji, Türkiye'nin Ankara ilinde bulunan enstrümantasyonlu bir derin kazı projesinin rastgele sonlu eleman modellemesi (RFEM) kullanılarak örneklendirilmiş ve doğrulanmıştır. Kazı, 20 metre derinliğe sahiptir ve altı seviyede öngermeli zemin ankrajları ile desteklenmektedir. Kilin efektif içsel sürtünme açısının düşey konumsal değişkenliğini benzeştirmek için rastgele alan teorisi ve Monte Carlo simülasyonu kullanılmıştır. Benzeştirme ile üretilen parametreler daha sonra Python yazılım dili aracılığıyla sonlu eleman modeline yerleştirilerek, fore kazık iksa yapısında yanal deformasyonlar ve eğilme momentlerinin olasılıksal dağılımı incelenmiştir. Monte Carlo simülasyonlarından elde edilen sonuçlar, konumsal değişkenliğin göz önünde bulundurulmasının ve değerinin ortaya çıkan yanal deformasyonlar, eğilme momentleri ve sistem yenilme olasılığı üzerinde etkisinin olduğunu göstermektedir.

Anahtar Kelimeler

Monte Carlo Simulasyonu, Rasgele Alan Teorisi, Sonlu Elemanlar Yöntemi

The Effect of Incorporating Vertical Spatial Variability on the Probabilistic Analysis of a Deep Excavation: A Case Study

Öz

This study explores the impact of including the vertical spatial variability in effective stress friction angle of clay on the probabilistic analysis of deep excavations. The proposed methodology is demonstrated and verified by conducting random finite element modeling (RFEM) of an instrumented deep excavation project situated in Ankara, Turkey. The excavation has a depth of 20 meters and is supported by six levels of pre-stressed ground anchors. To simulate the vertical spatial variability of effective stress friction angle in the clay, Monte Carlo simulation method and the random field theory are employed. The simulated parameters are then inserted into the finite element model via Python programming language to analyze the probabilistic distribution of lateral deflections and bending moments in the drilled shaft wall. The results obtained from the Monte Carlo simulations reveal that the incorporation and selected value of spatial variability significantly impacts the resulting lateral movements, bending moments, and the probability of failure of the system.

Anahtar Kelimeler

Monte Carlo Simulation, Random Field Theory, Finite Element Method

Kaynakça

• [1] Phoon KK and Kulhawy FH, “Characterization of geotechnical variability”, Canadian Geotechnical Journal, 36(4), 612-624, (1999a).
• [2] Uzielli M., Lacasse S., Nadim F and Phoon KK, “Soil variability analysis for geotechnical practice. Characterisation and engineering properties of natural soils”, 3(4), 1653-1752, (2007).
• [3] Schweiger HF and Peschl GM, “Reliability analysis in geotechnics with the random set finite element method”, Computers and Geotechnics, 32, 422-435, (2005).
• [4] Lacasse S and Nadim F., “Risk and reliability in geotechnical engineering”, St. Louis, 1172-1192, (1998).
• [5] Yu X., Cheng J., Cao C., Li E., and Feng J., "Probabilistic analysis of tunnel liner performance using random field theory." Advances in Civil Engineering, (2019).
• [6] Zhang S., Li Y., Li J., and Liu L., "Reliability analysis of layered soil slopes considering different spatial autocorrelation structures." Applied Sciences, 10(11), 4029, (2020).
• [7] Chen L., Zhang W., Chen F., Gu D., Wang L., and Wang, Z., "Probabilistic assessment of slope failure considering anisotropic spatial variability of soil properties." Geoscience Frontiers, 13(3), 101371, (2022).
• [8] Pană P., "Implementation of spatial variability in PLAXIS.", Master of Science in Applied Earth Sciences, Delft University of Technology, (2022).
• [9] Goldsworthy JS, Jaksa MB, Kaggwa WS, Fenton GA, Griffiths DV, and Poulos HG, "Reliability of site investigations using different reduction techniques for foundation design.", In 9th International conference on structural safety and reliability, pp. 901-908, (2005).
• [10] Wu G., Zhao H., Zhao M., and Zhu Z, "Stochastic analysis of dual tunnels in spatially random soil." Computers and Geotechnics, 129, 103861 (2021) .
• [11] Zhu H., Zhang L.M. ” Characterizing geotechnical anisotropic spatial variations using random field theory”, Canadian Geotechnical Journal, 50(7), 723-734, (2013).
• [12] Sert S., Luo Z., Xiao J., Gong W and Juang CH, “Probabilistic analysis of responses of cantilever wall-supported excavations in sands considering vertical spatial variability”, Computers and Geotechnics, 75, 182-191, (2016) .
• [13] Luo N and Bathurst RJ, “Probabilistic analysis of reinforced slopes using RFEM and considering spatial variability of frictional soil properties due to compaction”, Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 12(2), 87-108, (2018).
• [14] Nguyen TS and Likitlersuang S., “Influence of the spatial variability of soil shear strength on deep excavation: A case study of a Bangkok underground MRT station”, International Journal of Geomechanics, 21(2), 04020248, (2021).
• [15] Python Software Foundation. Python Language Reference, version 3.10.4. Available at http://www.python.org
• [16] Çalışan O., “Ankara Kilinde Yapılan 20 m Derinliğinde Bir Kazının Geri Analizi”, Prof. İsmet Ordemir'i Anma Toplantısı ve 5. Odtü Geoteknik Mühendisliği Sempozyumu, Ankara : METU, İnşaat Mühendisliği Bölümü, 1-12, (2009).
• [17] PLAXIS 2D (Software). (2017 ). PLAXIS BV. Delft, The Netherlands: P.O. Box 572, 2600 AN.
• [18] Brinkgrave RBJ, “Course notes: international course for experienced Plaxis users(English)”, The Netherlands, (2005).
• [19] Harr ME, “Reliability-based design in civil engineering”, 1984 Henry M. Shaw Lecture. Raleigh, N.C.: Department of Civil Engineering, North Carolina State University, (1984).
• [20] Bozkurt S and Akbas SO, “Finite Element-based Geotechnical Risk Analysis For Anchor–supported Deep Excavations”, Arabian Journal of Geosciences, (2023).
• [21] Phoon KK and Ching J., “Risk and Reliability In Geotechnical Engineering”, CRC Press Taylor ve Francis Group, (2015).
• [22] Akbas SO and Kulhawy FH, “Characterization and estimation of geotechnical variability in Ankara Clay: A case history”, Geotechnical and Geological Engineering, 28(5), 619-631, (2010).
• [23] Sorensen KK and Okkels N., “Proceedings of the 18th international conference on soil mechanics and geotechnical engineering. Correlation between drained shear strength and plasticity index of undisturbed overconsolidated clays”, Paris, 423-428, (2013).
• [24] Akademi Etüt Proje, “DHMİ Esenboğa Havalimanı Jeolojik ve Jeoteknik Raporu”, Ankara, (2014).
• [25] Vanmarcke E., “Random fields: analysis and synthesis”, World scientific, (2010).
• [26] Fenton AG, “Data Analysis/Geostatistics”, in Probabilistic Methods in Geotechnical Engineering, Fenton AG. Ed., GeoLogan'97 Logan, Utah, USA, 14-38, (1997).
• [27] Johansen AM, “Monte Carlo Methods” in International encyclopedia of education, Elsevier Ltd., (2010).
• [28] Long M., “Database for Retaining Wall and Ground Movements due to Deep Excavations”, Journal of Geotechnical and Geoenviromental Engineering, mar; 127(3): 203-224, (2001).
• [29] Lazarte CA, Robinson H., Gomez JE, Baxter A., Cadden A., Berg R., “Soil Nail Walls Reference Manuel”, U.S.: U.S. Department of Transportation Federal Highway Administration, (2015).
• [30] Fenton, GA and Griffiths, DV., “Three-Dimensional Probabilistic Foundation Settlement.” Journal of Geotechnical and Geoenvironmental Engineering, 131(2), 232–239. (2005).