پیش‌بینی ظرفیت باربری جانبی شمع‌ها در خاک‌های رسی با استفاده از ماشین بردار پشتیبان

نوع مقاله : مقاله کامل پژوهشی

نویسندگان

1 عضو هیات علمی دانشگاه بین المللی امام خمینی

2 دانش آموخته کارشناسی ارشد مهندسی عمران/مکانیک خاک و پی

چکیده

پیش‌بینی ظرفیت باربری شمع‌های تحت بار جانبی یکی از مسائل اساسی در مهندسی ژئوتکنیک است و تاکنون روش‌های متفاوتی برای ارزیابی آن ارائه شده است. ماشین بردار پشتیبان (SVM) یک روش نسبتاً جدید هوش مصنوعی است که در بسیاری از مسائل ژئوتکنیکی به طور موفقیت‌آمیزی مورد استفاده قرار گرفته است. این مقاله کاربرد مدل SVM برای پیش‌بینی ظرفیت باربری جانبی شمع‌ها در خاک‌های رسی را شرح می‌دهد. از نتایج مدل‌های کوچک مقیاس آزمایشگاهی شمع‌های صلب در خاک‌های رسی با پارامترهای ورودی قطر شمع (D)، طول مدفون شمع (L)، خروج از مرکز بار (e) و مقاومت برشی زهکشی‌نشده خاک (Su) برای توسعه و ارزیابی مدل استفاده شده است. ظرفیت باربری جانبی پیش‌بینی‌شده توسط مدل پیشنهادی با نتایج حاصل از مدل شبکه عصبی مصنوعی (ANN) و همچنین روش‌های تحلیلی Broms و Hansen مقایسه شده است. نتایج نشان از کارایی بهتر مدل SVM نسبت به روش‌های مذکور دارد. این مطالعه نشان می‌دهد که روش SVM یک ابزار جایگزین برای مهندسین ژئوتکنیک به منظور پیش‌بینی ظرفیت باربری جانبی شمع‌ها ارائه می‌دهد.

کلیدواژه‌ها


عنوان مقاله [English]

Prediction of Lateral Bearing Capacity of Pile in Clay Using Support Vector Machine

نویسندگان [English]

  • Alireza Ardakani 1
  • Vahid Reza Kohestani 2
1 Faculty of Engineering and Technology, Imam Khomeini International University, Qazvin
2 Faculty of Engineering and Technology, Imam Khomeini International University, Qazvin
چکیده [English]

A number of empirical formulas were proposed so as to reduce the time and cost involved in static approach to determine the pile capacity. Although these formulas have been widely used to predict pile capacity, it is agreed that these formulas are inaccurate due to their oversimplification of the modeling of the hammer, driving system, pile, and soil (Fragaszy et al. 1985). The support vector machine (SVM) is a relatively new artificial intelligence technique which is increasingly being applied to geotechnical problems. An important feature of the SVM is that it endeavors to discover the rules (or functions) that govern a phenomenon using only a set of data (a set of measured inputs and their corresponding outputs). Hence, there is no need to incorporate any assumptions to simplify the problem as, is often the case with many traditional methods. The previous studies indicated that these methods are more accurate compared to analytical formulas. In this paper, SVM technique is presented to predict the undrained lateral load capacity of piles in clay using the diameter of pile (D), depth of pile embedment (L), eccentricity of load (e), undrained shear strength of soil (Su) as the inputs of model. The model was developed and tested using an experimental dataset. The performance of the proposed model (SVM) was compared with ANN and those of theoretical methods of Broms, and Hansen.

کلیدواژه‌ها [English]

  • support vector machine (SVM)
  • Lateral bearing capacity
  • Pile
  • Undrained shear strength (Su)
Abu-Farsakh MY, Titi HH, "Assessment of direct cone penetration test methods for predicting the ultimate capacity of friction driven piles", Journal of Geotechnical and Geoenvironmental Engineering, 2004, 130(9), 935-944.
Ardakani A, Kohestani VR, "Evaluation of liquefaction potential based on CPT results using C4. 5 decision tree", Journal of AI and Data Mining, 2015, 3(1), 85-89.
Bhushan K, Fong PT, Haley SC, "Lateral load tests on drilled piers in stiff clays", Journal of the Geotechnical Engineering Division, 1979, 105(8), 969-985.
Broms BB, "Lateral Resistance of Piles in Cohesive Soils", Journal of the Soil Mechanics and Foundations Division, 1964, 90(2), 27-64. 
Cortes C, Vapnik V, "Support-vector networks", Machine learning, 1995, 20(3), 273-297.
Cristianini N, Shawe-Taylor J, "An Introduction to Support Vector Machines and Other Kernel-based Learning Methods", 2000, Cambridge University Press.
Das SK, Basudhar PK, "Undrained lateral load capacity of piles in clay using artificial neural network", Computers and Geotechnics, 2006, 33(8), 454-459.
Dibike YB, Velickov S, Solomatine D, Abbott B, "Model induction with support vector machines: introduction and applications", Journal of Computing in Civil Engineering, 2001, 15(3), 208-216.
Georgiadis K, Georgiadis M, Anagnostopoulos C, "Lateral bearing capacity of rigid piles near clay slopes", Soils and Foundations, 2013, 53(1), 144-154.
Goh AT, Goh S, "Support vector machines: their use in geotechnical engineering as illustrated using seismic liquefaction data", Computers and Geotechnics, 2007, 34(5), 410-421.
Hansen JB, Christensen N, "The Ultimate Resistance of Rigid Piles against Transversal Forces; Model Tests with Transversally Loaded Rigid Piles in Sand", 1961, Geoteknisk Institut.
Jeanjean P, "Re-assessment of py curves for soft clays from centrifuge testing and finite element modeling", in Offshore Technology Conference, 2009, Offshore Technology Conference.
Kohestani VR, Bazargan-Lari MR, Asgari marnani J, "Prediction of maximum surface settlement caused by earth pressure balance shield tunneling using random forest", Journal of AI and Data Mining, 2016, 5(1), 127-135.
Kohestani VR, Hassanlourad M, "Modeling the Mechanical Behavior of Carbonate Sands Using Artificial Neural Networks and Support Vector Machines", International Journal of Geomechanics, 2015, 16(1), 04015038.
Kohestani VR, Hassanlourad M, Ardakani A, "Evaluation of liquefaction potential based on CPT data using random forest", Natural Hazards, 2015, 79(2), 1078-1089.
Kohestani V, Hassanlourad M, Bazargan-Lari MR, "Prediction the Ultimate Bearing Capacity of Shallow Foundations on the Cohesionless Soils Using M5P Model Tree", Journal of Civil Engineering, 2016, 27(2), 99-110. (in Persian)
Kordjazi A, Pooya Nejad F, Jaksa M, "Prediction of ultimate axial load-carrying capacity of piles using a support vector machine based on CPT data", Computers and Geotechnics, 2014, 55, 91-102.
Liu YJ, Liang SH, Wu JW, Fu N, "Prediction method of vertical ultimate bearing capacity of single pile based on support vector machine", Advanced Materials Research, 2011, 168, 2278-2282.
Matlock H, "Correlations for design of laterally loaded piles in soft clay", Offshore Technology in Civil Engineering’s Hall of Fame Papers from the Early Years, 1970, 77-94.
Meyerhof G, Mathur S, Valsangkar A, "Lateral resistance and deflection of rigid walls and piles in layered soils", Canadian Geotechnical Journal, 1981, 18(2), 159-170.
Pal M, Deswal S, "Modeling pile capacity using support vector machines and generalized regression neural network", Journal of geotechnical and geoenvironmental engineering, 2008, 134(7), 1021-1024.
Poulos HG, Davis EH, "Pile foundation analysis and design", 1980, New York: Wiley.
Rao KM, Suresh Kumar V, "Measured and predicted response of laterally loaded piles", in sixth international conference and exhibition on piling and deep foundations, 1996, India.
Reese LC, Welch RC, "Lateral loading of deep foundations in stiff clay", Journal of the Geotechnical Engineering Division, 1975, 101(7), 633-649.
Samui P, "Prediction of friction capacity of driven piles in clay using the support vector machine", Canadian Geotechnical Journal, 2008, 45(2), 288-295.
Samui P, "Prediction of pile bearing capacity using support vector machine", International Journal of Geotechnical Engineering, 2011a, 5(1), 95-102.
Samui P, "Support vector machine applied to settlement of shallow foundations on cohesionless soils", Computers and Geotechnics, 2008b, 35(3), 419-427.
Samui P, Sitharam T, Kurup PU, "OCR prediction using support vector machine based on piezocone data", Journal of Geotechnical and GeoEnvironmental engineering, 2008, 134(6), 894-898.
Shahin MA, "Intelligent computing for modeling axial capacity of pile foundations", Canadian Geotechnical Journal, 2010, 47 (2), 230-243.
Smola AJ, Schölkopf B, "A tutorial on support vector regression. Statistics and computing", 2004, 14(3), 199-222.
Stewart D, "Reduction of undrained lateral pile capacity in clay due to an adjacent slope", Aust Geomech, 1999, 34(4), 17-23.
Üstün B, Melssen WJ, Buydens LM, "Facilitating the application of support vector regression by using a universal Pearson VII function based kernel", Chemometrics and Intelligent Laboratory Systems, 2006, 81(1), 29-40.
Witten, IH, Frank E, "Data Mining: Practical machine learning tools and techniques", 2005, Morgan Kaufmann.
Zhang MY, Liang L, Song HZ, Li Y, Peng WT, "Intelligent prediction for side friction of large-diameter and super-long steel pipe pile based on support vector machine", Applied Mechanics and Materials, 2012, 170, 747-750.