Structural Engineering and Mechanics

  • 제81권5호
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  • Pages.565-574
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  • 2022
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
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Prediction of stress intensity factor range for API 5L grade X65 steel by using GPR and MPMR

  • Murthy, A. Ramachandra (Fatigue & Fracture Laboratory, CSIR-Structural Engineering Research Centre) ;
  • Vishnuvardhan, S. (Fatigue & Fracture Laboratory, CSIR-Structural Engineering Research Centre) ;
  • Saravanan, M. (Fatigue & Fracture Laboratory, CSIR-Structural Engineering Research Centre) ;
  • Gandhi, P. (Fatigue & Fracture Laboratory, CSIR-Structural Engineering Research Centre)
  • 투고 : 2020.05.29
  • 심사 : 2021.12.07
  • 발행 : 2022.03.10
⟨ 이전 논문 다음 논문 ⟩

초록

The infrastructures such as offshore, bridges, power plant, oil and gas piping and aircraft operate in a harsh environment during their service life. Structural integrity of engineering components used in these industries is paramount for the reliability and economics of operation. Two regression models based on the concept of Gaussian process regression (GPR) and Minimax probability machine regression (MPMR) were developed to predict stress intensity factor range (𝚫K). Both GPR and MPMR are in the frame work of probability distribution. Models were developed by using the fatigue crack growth data in MATLAB by appropriately modifying the tools. Fatigue crack growth experiments were carried out on Eccentrically-loaded Single Edge notch Tension (ESE(T)) specimens made of API 5L X65 Grade steel in inert and corrosive environments (2.0% and 3.5% NaCl). The experiments were carried out under constant amplitude cyclic loading with a stress ratio of 0.1 and 5.0 Hz frequency (inert environment), 0.5 Hz frequency (corrosive environment). Crack growth rate (da/dN) and stress intensity factor range (𝚫K) values were evaluated at incremental values of loading cycle and crack length. About 70 to 75% of the data has been used for training and the remaining for validation of the models. It is observed that the predicted SIF range is in good agreement with the corresponding experimental observations. Further, the performance of the models was assessed with several statistical parameters, namely, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Efficiency (E), Root Mean Square Error to Observation's Standard Deviation Ratio (RSR), Normalized Mean Bias Error (NMBE), Performance Index (ρ) and Variance Account Factor (VAF).

키워드

과제정보

The authors thank the Director and Advisor (Management), CSIR-SERC, Chennai for the constant support and encouragement extended to them in their R&D activities. The assistance rendered by the technical staff of the Fatigue & Fracture Laboratory, CSIR-SERC in conducting the experimental investigations is gratefully acknowledged. This paper is published with the kind permission of the Director, CSIR-SERC, Chennai.

참고문헌

  1. ASTM E 1820-2011 (2011), Standard Test Method for Measurement of Fracture Toughness, ASTM International, USA.
  2. ASTM E 647-2015 (2015), Standard Test Method for Measurement of Fatigue Crack Growth Rates, ASTM International, USA.
  3. ASTM E 8M-2016a (2016), Standard Test Methods for Tension Testing of Metallic Materials [Metric], ASTM International, USA.
  4. Bertsimas, D. and Sethuraman, J. (2002), "From fluid relaxations to practical algorithms for job shop scheduling: The Makespan objective", Math. Prog., 92(1), 61-102. https://doi.org/10.1007/s101070100272.
  5. Brahim-Belhouari, S. and Bermak, A. (2004), "Gaussian process for nonstationary time series prediction", Comput. Stat. Data Anal., 47(4), 705-712. https://doi.org/10.1016/j.csda.2004.02.006.
  6. Chinnaiah, M. and Prakash, R.V. (2010), "Corrosion fatigue crack growth studies in Ni-Cr-Mn steel", Int. J. Mech. Mat. Eng., 4(12), 1402-1407.
  7. Dutta, S., Samui, P. and Kim, D. (2018), "Comparison of machine learning techniques to predict compressive strength of concrete", Comput Concrete, 21(4), 463-470. https://doi.org/10.12989/cac.2018.21.4.463.
  8. 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://doi.org/10.12989/sem.2015.53.5.939.
  9. 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://doi.org/10.12989/sem.2017.64.3.339.
  10. Gopinath, K.G.S., Pal, S. and Tambe, P. (2018). "Prediction of hardness and fracture toughness in liquid-phase-sintered alumina system using Gaussian process regression and minimax probability machine regression", Mater. Today: Proc., 5(5), 12223-12232. https://doi.org/10.1016/j.matpr.2018.02.199.
  11. Horstmann, M., Gregory, J.K. and Schwalbe, K.H. (1995), "Geometry effects on corrosion fatigue in offshore structural steels", Int. J. Fatig., 17(4), 293-299. https://doi.org/10.1016/0142-1123(95)00007-G.
  12. Kang, D.H., Lee, J.K. and Kim, T.W. (2011), "Corrosion fatigue crack propagation of high-strength steel HSB800 in a seawater environment", Proc. Eng., 10, 1170-1175. https://doi.org/10.1016/j.proeng.2011.04.195.
  13. Kaur, J. and Kaur, K. (2017), "A fuzzy approach for an IoT-based automated employee performance appraisal", Comput. Mater. Continua, 53(1), 23-36.
  14. Keprate, A. and Ratnayake, R.M.C. (2016), "Enhancing offshore process safety by selecting fatigue critical pipeline locations for inspection using Fuzzy-AHP based approach", Proc. Saf. Environ. Protec., 102, 71-84. https://doi.org/10.1016/j.psep.2016.02.013.
  15. Keprate, A., Ratnayake, R.C. and Sankararaman, S. (2017), "Adaptive Gaussian process regression as an alternative to FEM for prediction of stress intensity factor to assess fatigue degradation in offshore pipeline", Int. J. Press. Ves. Pip., 153, 45-58. https://doi.org/10.1016/j.ijpvp.2017.05.010.
  16. Kong, Y., Liu, X.W. and Zhang, S. (2009), "Minimax probability machine regression for wireless traffic short term forecasting", 2009 First UK-India International Workshop on Cognitive Wireless Systems (UKIWCWS), December. http://doi.org/10.1109/UKIWCWS.2009.5749407.
  17. Lanckriet, G., Ghaoui, L., Bhattacharyya, C. and Jordan, M. (2001), "Minimax probability machine", Part of Advances in Neural Information Processing Systems, 14 (NIPS 2001).
  18. Li, S.X. and Akid, R. (2013), "Corrosion fatigue life prediction of a steel shaft material in seawater", Eng. Fail. Anal., 34, 324-334. https://doi.org/10.1016/j.engfailanal.2013.08.004.
  19. Mansouri, I., Safa, M., Ibrahim, Z., Kisi, O., Tahir, M.M., Baharom, S. and Azimi, M. (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.
  20. Marshall, A.W. and Olkin, I. (1960), "Multivariate chebyshev inequalities", Ann. Math. Stat., 31(4), 1001-1014. https://doi.org/10.1214/aoms/1177705673
  21. Pal, M. and Deswal, S. (2010), "Modelling pile capacity using Gaussian process regression", Comput. Geotech., 37(7-8), 942-947. https://doi.org/10.1016/j.compgeo.2010.07.012.
  22. Pasolli, L., Melgani, F. and Blanzieri, E. (2010), "Gaussian process regression for estimating chlorophyll concentration in subsurface waters from remote sensing data", IEEE Geosci. Remote Sens. Lett., 7(3), 464-468. http://doi.org/10.1109/LGRS.2009.2039191.
  23. Ramachandra Murthy, A., Vishnuvardhan, S., Saravanan, M. and Gandhi, P. (2019), "Relevance vector based approach for the prediction of stress intensity factor for the pipe with circumferential crack under cyclic loading", Struct. Eng. Mech., 72(1), 31-41. https://doi.org/10.12989/sem.2019.72.1.031.
  24. Rasmussen, C.E. and Williams, C.K.I. (2006), Gaussian Processes for Machine Learning, The MIT Press, Massachusetts Institute of Technology, Cambridge, MA.
  25. Shah, V.S., Shah, H.R., Samui, P., Murthy, A.R., Merono, P., Gomez, F., ... & Liu, B. (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. Continua, 44(2), 73-84.
  26. Shantaram, P., Shreya, S., Pijush, S. and Ramachandra Murthy, A. (2014), "Prediction of fracture parameters of high strength and ultra high strength concrete beams using Gaussian process regression and Least squares support vector machine", Comput. Model. Eng. Sci., 101(2), 139-158.
  27. Sivaprasad, S., Tarafder, S., Ranganath, V.R., Tarafder, M. and Ray, K.K. (2006), "Corrosion fatigue crack growth behaviour of naval steels", Corros. Sci., 48(8), 1996-2013. https://doi.org/10.1016/j.corsci.2005.08.005.
  28. Strohmann, T. and Grudic, G. (2002), "A formulation for minimax probability machine regression'", Part of Advances in Neural Information Processing Systems 15 (NIPS 2002).
  29. Stulp, F. and Sigaud, O. (2015), "Many regression algorithms, one unified model: A review", Neur. Net., 69, 60-79. https://doi.org/10.1016/j.neunet.2015.05.005.
  30. Sun, J., Bai, Y., Luo, J. and Dang, J. (2009), "Modelling of a chaotic time series using a modified minimax probability machine regression", Chin. J. Phys., 47(4), 491-501. https://doi.org/10.6122/CJP.
  31. Yang, D.H., Yi, T.H. and Li, H.N. (2017), "Coupled fatigue-corrosion failure analysis and performance assessment of RC bridge deck slabs", J. Bridge Eng., 22(10), 04017077. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001108.
  32. Yang, L., Wang, L., Sun, Y. and Zhang, R. (2010), "Simultaneous feature selection and classification via Minimax Probability Machine", Int. J. Comput. Intell. Syst., 3(6), 754-760. https://doi.org/10.1080/18756891.2010.9727738.
  33. Yuvaraj, P., Murthy, A.R., Iyer, N.R., Samui, P. and Sekar, S.K. (2013), "Multivariate adaptive regression splines model to predict fracture characteristics of high strength and ultra high strength concrete beams", CMC: Comput. Mater. Continua, 36(1), 73-97.
  34. Yuvaraj, P., Murthy, A.R., Iyer, N.R., Samui, P. and Sekar, S.K. (2014), "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.
  35. Yuvaraj, P., Murthy, A.R., Iyer, N.R., Sekar, S.K. and Samui, P. (2014), "ANN model to predict fracture characteristics of high strength and ultra high strength concrete beams", CMC-Comput. Mater. Continua, 41(3), 193-213.
  36. Yuvaraj, P., Ramachandra Murthy, A., Nagesh R. Iyer, Pijush, S. and Sekar, S.K. (2013), "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. Zhou, Z., Wang, Z. and Sun, X. (2013), "Face recognition based on optimal kernel minimax probability machine", J. Theor. Appl. Inform. Technol., 48(3), 1645-1651.