Structural Engineering and Mechanics

  • 제72권1호
  • /
  • Pages.31-41
  • /
  • 2019
  • /
  • 1225-4568(pISSN)
  • /
  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Relevance vector based approach for the prediction of stress intensity factor for the pipe with circumferential crack under cyclic loading

  • Ramachandra Murthy, A. (CSIR-Structural Engineering Research Centre) ;
  • Vishnuvardhan, S. (CSIR-Structural Engineering Research Centre) ;
  • Saravanan, M. (CSIR-Structural Engineering Research Centre) ;
  • Gandhic, P. (CSIR-Structural Engineering Research Centre)
  • 투고 : 2019.04.06
  • 심사 : 2019.05.02
  • 발행 : 2019.10.10
⟨ 이전 논문 다음 논문 ⟩

초록

Structural integrity assessment of piping components is of paramount important for remaining life prediction, residual strength evaluation and for in-service inspection planning. For accurate prediction of these, a reliable fracture parameter is essential. One of the fracture parameters is stress intensity factor (SIF), which is generally preferred for high strength materials, can be evaluated by using linear elastic fracture mechanics principles. To employ available analytical and numerical procedures for fracture analysis of piping components, it takes considerable amount of time and effort. In view of this, an alternative approach to analytical and finite element analysis, a model based on relevance vector machine (RVM) is developed to predict SIF of part through crack of a piping component under fatigue loading. RVM is based on probabilistic approach and regression and it is established based on Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Model for SIF prediction is developed by using MATLAB software wherein 70% of the data has been used for the development of RVM model and rest of the data is used for validation. The predicted SIF is found to be in good agreement with the corresponding analytical solution, and can be used for damage tolerant analysis of structural components.

키워드

참고문헌

  1. Bergman, M. and Brickstad, B. (1991), "Stress intensity factors for circumferential cracks in pipes analyzed by FEM using line spring elements", Int J Fract, 47(1), R17-R19. https://doi.org/10.1002/mawe.19870180710.
  2. Bhargava, R.Y., Bhasin, V. and Kushwaha, H.S. (1998), "Assuring It is safe", International conference of Integrating Structural Integrity, Inspection, Monitoring, Safety and Risk Assessment, Institution of Mechanical Engineers, United Kingdom., May.
  3. Brennan, F.P., Ngiam, S.S. and Lee, C.W. (2008), "An experimental and analytical study of fatigue crack shape control by cold working", Eng Fract Mech, 75(3-4), 355-363. https://doi.org/10.1016/j.engfracmech.2007.03.033.
  4. Dawei, H., Ian, C. and Weiping, K. (2012), "Flow modelling using Relevance Vector Machine (RVM)", Proceedings of the Fifth International Conference on Hydroinformatics, 1429-1435, Cardiff, United Kingdom.
  5. Engin, S., Ozturk, O. and Okay, F. (2015), "Estimation of ultimate torque capacity of the SFRC beams using ANN", Struct Eng Mech, 53(5), 939-956. http://dx.doi.org/10.12989/sem.2015.53.5.939.
  6. Erdem, H. (2017). "Predicting the moment capacity of RC slabs with insulation materials exposed to fire by ANN", Struct Eng Mech, 64(3), 339-346. http://dx.doi.org/10.12989/sem.2017.64.3.339.
  7. Jaideep, K. and Kamaljit, K. (2017), "A Fuzzy Approach for an IoT-based Automated Employee Performance Appraisal", Comput Mater Con, 53(1), 23-36. https://doi.org/10.3970/cmc.2017.053.024.
  8. John, F., Todd, K.M., Mac, M. and Gunther, J.H. (2010), "Application of the relevance vector machine to canal flow prediction in the Sevier River Basin", Agric Water Manage, 97(2), 208-214. https://doi.org/10.1016/j.agwat.2009.09.010.
  9. Kefei, L. and Zhisheng, X. (2011), "Traffic Flow Prediction of Highway Based on Wavelet Relevance Vector Machine", J Inf Comput Sci. 8(9), 1641-1647.
  10. Keprate, A. and Ratnayake, R.M.C. (2017), "Enhancing offshore process safety by selecting fatigue critical pipeline locations for inspection using Fuzzy-AHP based approach", Process Saf Environ, 106, 34-51. https://doi.org/10.1016/j.psep.2016.02.013.
  11. Kumar, V., German, M.D. and Schumacher, B.I. (1985), "Analysis of elastic surface cracks in cylinders using the line spring model and shell finite element method", ASME J Press Vessel Technol, 107(4), 403-411. https://doi.org/10.1115/1.3264474.
  12. MacKay, D. J. C. (1994), Bayesian Methods for Back Propagation Networks", Models of Neural Networks III. Physics of Neural Networks. Springer, New York, USA.
  13. Mansouri, I., Safa, M., Ibrahim, Z., Kisi, O., ahir, M.M., Baharom, S. and Azimi. (2016), "Strength prediction of rotary brace damper using MLR and MARS", Struct Eng Mech, 60(3), 471-488. https://doi.org/10.12989/sem.2016.60.3.471.
  14. Marie, S., Chapuliot, S., Kayser, Y., Lacier, M.H., Drubay, B., Barthelet, B., Le, P.D., Rougier, V., Naudin, C., Gilles, P. and Triay, M. (2007), "French RSE-M and RCC-MR code appendices for flaw analysis: Presentation of the fracture calculation-Part III: cracked pipes", Int J Pressure Vessel Piping, 84(10-11), 614-658. https://doi.org/10.1016/j.ijpvp.2007.05.005.
  15. Miyazaki, M. and Mochizuki, M. (2011). "The effects of residual stress distribution and component geometry on the stress intensity factor of surface cracks". ASME J Press Vessel Technol,133(1). 011701-7. https://doi.org/10.1115/PVP2005-71462.
  16. Neal, R. M. (1996), Bayesian Learning for Neural Networks, Springer, New York., USA.
  17. Nurcihan, C. (2014), "Application of support vector machines and relevance vector machines in predicting uniaxial compressive strength of volcanic rocks", J Afr Earth Sci, 100, 634-644. https://doi.org/10.1016/j.jafrearsci.2014.08.006.
  18. Prasanna, P.K., Murthy, A.R. and Srinivasu, K. (2018), "prediction of compressive strength of GGBS based concrete using RVM", Struct Eng Mech, 68(6), 691-700. http://dx.doi.org/10.12989/sem.2018.68.6.691.
  19. Rooke, D.P. (1976), Cartwright DJ. Compendium of stress intensity factors. London, UK.
  20. Sarat, K.D. and Pijush, S. (2008), "Prediction of Liquefaction Potential Based on CPT Data: A Relevance Vector Machine Approach", 12th International Conference of International Association for Computer Methods and Advances in Geomechanics (IACMAG), Goa, India.
  21. Shantaram, P., Shreya, S., Pijush, S., Murthy, A.R. (2014), "Prediction of fracture parameters of high strength and ultra high strength concrete beams using Gaussian process regression and Least squares support vector machine", Comp Model Eng, 101(2), 139-158. https://doi.org/10.3970/cmes.2014.101.139.
  22. Sheng-wei, F. and Yong, H. (2015), "Wind speed prediction using the hybrid model of wavelet decomposition and artificial bee colony algorithm-based relevance vector machine", Int J Elec Power, 73, 625-631. https://doi.org/10.1016/j.ijepes.2015.04.019.
  23. Sih, G.C. (1973), Handbook of stress intensity factors", Institute of Fracture and Solid Mechanics, Leigh University, USA.
  24. Singh, P.K., Vaze, K.K., Bhasin, V., Kushwaha, H.S., Gandhi, P. and Ramachandra Murthy, D.S. (2003), "Crack initiation and growth behavior of circumferentially cracked pipes under cyclic and monotonic loading", Int J Pressure Vessel Piping, 80, 629-640. https://doi.org/10.1016/S0308-0161(03)00132-7.
  25. Susom, D., Murthy, A.R., Dookie, K. and Pijush, S. (2017), "Prediction of Compressive Strength of Self-Compacting Concrete Using Intelligent Computational Modelling", Comput Mater Con, 53(2), 157-174. https://doi.org/10.3970/cmc.2017.053.167.
  26. Tada, H.P., Paris, P.C. and Irwin, G.R. (1973), The stress analysis of cracks handbook, Del Research Corporation, Pennsylvania, USA.
  27. Tipping, M.E. (2001), "Sparse Bayesian learning and the relevance vector machine", J Mach Learn Res, 1, 211-244. https://doi.org/10.1162/15324430152748236.
  28. Vishal, S.S., Henyl, R.S., Pijush, S. and Murthy, A.R. (2014), "Prediction of Fracture Parameters of High Strength and Ultra-High Strength Concrete Beams using Minimax Probability Machine Regression and Extreme Learning Machine", Comput Mater Con, 44 (2), pp. 73-84. https://doi.org/10.3970/cmc.2014.044.073.
  29. Wahyu, C., Achmad, W. and Bo-Suk, Y. (2009), "Application of relevance vector machine and logistic regression for machine degradation assessment", Mech Sys Signal Pr, 24, 1161-1171. https://doi.org/10.1016/j.ymssp.2009.10.011.
  30. Widodo, A., Kim, E. Y., Son, J. D., Yang, B. S., Tan, A. C., Gu, D. S., Choi, B.K. and Joseph, M. (2009), "Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine", Expert Syst Appl, 36, 7252-7261. https://doi.org/10.1016/j.eswa.2008.09.033.
  31. Xiaodong, W., Meiying, Y. and Duanmu, C.J. (2009), "Classification of data from electronic nose using relevance vector machines", Sens Actuators B Chem, 140, 143-148. https://doi.org/10.1016/j.snb.2009.04.030.
  32. Yaguo, L. (2017), Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery, Butterworth-Heinemann, Elsevier Inc., Oxford, United Kingdom.
  33. Yuvaraj, P., Murthy, A.R., Nagesh, R.I., Pijush, S. and Sekar, S.K. (2013a), "Multivariate adaptive regression splines model to predict fracture characteristics of high strength and ultra high strength concrete beams", Comput Mater Con, 36(1), 73-97. https://doi.org/10.3970/cmc.2013.036.073.
  34. Yuvaraj, P., Murthy, A.R., Nagesh, R.I., Pijush, S. and Sekar, S.K. (2014a), "ANN model to predict fracture characteristics of high strength and ultra high strength concrete beams", Comput Mater Con, 41(3), 193-213. https://doi.org/10.3970/cmc.2014.041.193.
  35. Yuvaraj, P., Murthy, A.R., Nagesh, R.I., Pijush, S. and Sekar, S.K. (2014b), "Prediction of fracture characteristics of high strength and ultra high strength concrete beams based on relevance vector machine", Int J Damage Mech, 23(7), 979-1004. https://doi.org/10.1177/1056789514520796.
  36. Yuvaraj, P., Murthy, A.R., Nagesh, R.I., Pijush, S. and Sekar, S.K. (2013a), "Support vector regression based models to predict fracture characteristics of high strength and ultra high strength concrete beams", Eng Fract Mech, 98, 29-43. https://doi.org/10.1016/j.engfracmech.2012.11.014.
  37. Zahoor, A. (1985). "Closed-form expressions for fracture mechanics analysis of cracked pipes". ASME J Press Vessel Technol, 107(2), 203-205. https://doi.org/10.1115/1.3264435.
  38. Zareei, A. and Nabavi, S.M. (2016), "Calculation of stress intensity factors for circumferential semi-elliptical cracks with high aspect ratio in pipes", Int J Press Vessel Pip, 146, 32-38. https://doi.org/10.1016/j.ijpvp.2016.05.008.