Skip to main content
SpringerLink
Log in

Failure assessment of defected pipe under strike-slip fault with data-driven models accounting for the model uncertainty

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Buried pipeline is threatened from the soil displacement due to earthquake and other causes, which leads to the formation of unexpected external forces such as bending moment. The problem can be worse with the appearance of defects, resulting in the reduction in pipe capacity. The paper focuses on the overall problem of defected pipe crossing the strike-slip fault. A full-scaled FE model can be very complicated with the large-scale and micro-scale levels corresponding to the strike-slip fault and defect on pipe problems, respectively. To ease this difficulty, the macro and micro problems are solved separately with two types of FE models and their corresponding databases. To be specific, one FE model is used for predicting external moment due to strike-slip fault and the other is for predicting the moment capacity of defected pipe. Data-driven models are consequently developed with artificial neural network (ANN) for each database generated from these types of models: ANN1 evaluating moment capacity of defected pipe (R2 is 0.9943 on test set) and ANN2 predicting both moment and axial force appeared in pipe due to strike-slip fault (R-squares are 0.9883 and 0.9929 on test set, respectively). Consequently, the stress–strength analysis for the overall problem is solved. Accounting for the unavoidable uncertainty of the models, the paper proposed an approach which assumes that the actual distribution of residual of a model is equivalent to this of the test set. The distributions of residuals on test set of these ANNs are tested to be normally distributed and generated by the conventional Monte Carlo simulation. To the end, the deterministic problem leads to the failure probability. The proposed framework has been investigated, and the final results on this selective parametric study are reasonable.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data availability

The raw/processed data required to reproduce these findings will be made available on request.

References

  1. Karamitros DK, Bouckovalas GD, Kouretzis GP (2007) Stress analysis of buried steel pipelines at strike-slip fault crossings. Soil Dyn Earthq Eng 27(3):200–211

    Google Scholar 

  2. Karamitros DK et al (2011) An analytical method for strength verification of buried steel pipelines at normal fault crossings. Soil Dyn Earthq Eng 31(11):1452–1464

    Google Scholar 

  3. Talebi F, Kiyono J (2020) Introduction of the axial force terms to governing equation for buried pipeline subjected to strike-slip fault movements. Soil Dyn Earthq Eng 133:106125

    Google Scholar 

  4. Liu X et al (2020) A refined analytical strain analysis method for offshore pipeline under strike-slip fault movement considering strain hardening effect of steel. Ships Offshore Struct 15(2):215–226

    Google Scholar 

  5. Huan DT, Tu TM, Quoc TH (2017) Analytical solutions for bending, buckling and vibration analysis of functionally graded cylindrical panel. Vietnam J Sci Technol 55(5):587

    Google Scholar 

  6. Trifonov OV (2018) The effect of variation of soil conditions along the pipeline in the fault-crossing zone. Soil Dyn Earthq Eng 104:437–448

    Google Scholar 

  7. Melissianos VE et al (2016) Numerical evaluation of the effectiveness of flexible joints in buried pipelines subjected to strike-slip fault rupture. Soil Dyn Earthq Eng 90:395–410

    Google Scholar 

  8. Melissianos VE, Gantes CJ (2017) Numerical modeling aspects of buried pipeline—fault crossing. In: Lagaros ND, Papadrakakis M, Plevris V (eds) Computational methods in earthquake engineering. Springer, Cham, pp 1–26

    Google Scholar 

  9. Vazouras P, Karamanos SA, Dakoulas P (2010) Finite element analysis of buried steel pipelines under strike-slip fault displacements. Soil Dyn Earthq Eng 30(11):1361–1376

    Google Scholar 

  10. Zhang J, Xiao Y, Liang Z (2018) Mechanical behaviors and failure mechanisms of buried polyethylene pipes crossing active strike-slip faults. Compos B Eng 154:449–466

    Google Scholar 

  11. Vazouras P, Dakoulas P, Karamanos SA (2015) Pipe–soil interaction and pipeline performance under strike—slip fault movements. Soil Dyn Earthq Eng 72:48–65

    Google Scholar 

  12. Trifonov OV, Cherniy VP (2010) A semi-analytical approach to a nonlinear stress–strain analysis of buried steel pipelines crossing active faults. Soil Dyn Earthq Eng 30(11):1298–1308

    Google Scholar 

  13. Uckan E et al (2015) A simplified analysis model for determining the seismic response of buried steel pipes at strike-slip fault crossings. Soil Dyn Earthq Eng 75:55–65

    Google Scholar 

  14. Folkman S (2018) Water main break rates in the USA and Canada: a comprehensive study. Mechanical and Aerospace Engineering Faculty Publications, Paper 174

  15. Phan HC, Dhar AS, Mondal BC (2017) Revisiting burst pressure models for corroded pipelines. Can J Civ Eng 44(7):485–494

    Google Scholar 

  16. Nunes L, Nascimento V (2011) Estimation of internal defect size by means of radial deformations in pipes subjected to internal pressure. Thin-Walled Struct 49(2):298–303

    Google Scholar 

  17. Keshtegar B, Seghier MAB (2018) Modified response surface method basis harmony search to predict the burst pressure of corroded pipelines. Eng Fail Anal 89:177–199

    Google Scholar 

  18. Amaya-Gómez R et al (2019) Reliability assessments of corroded pipelines based on internal pressure—a review. Eng Fail Anal 98:190–214

    Google Scholar 

  19. Mondal BC, Dhar AS (2019) Burst pressure of corroded pipelines considering combined axial forces and bending moments. Eng Struct 186:43–51

    Google Scholar 

  20. Liu J et al (2009) Remaining strength of corroded pipe under secondary (biaxial) loading. GL Industrial Services UK Ltd., Loughborough, UK

    Google Scholar 

  21. Peng J et al (2011) Safety assessment of pipes with multiple local wall thinning defects under pressure and bending moment. Nucl Eng Des 241(8):2758–2765

    Google Scholar 

  22. Zhao Z et al (2018) Influence of pitting corrosion on the bending capacity of thin-walled circular tubes. J Braz Soc Mech Sci Eng 40(11):548

    Google Scholar 

  23. Phan HC et al. (2020) Predicting capacity of defected pipe under bending moment with data-driven model. In: International conference on modern mechanics and applications. Lecture notes in mechanical engineering, Springer: Ho Chi Minh city, Vietnam.

  24. Phan HC, Duong HT (2021) Predicting burst pressure of defected pipeline with principal component analysis and adaptive neuro fuzzy inference system. Int J Press Vessels Pip 189:104274

    Google Scholar 

  25. Duong HT et al (2020) Optimization design of rectangular concrete-filled steel tube short columns with balancing composite motion optimization and data-driven model. in structures. Elsevier

  26. Le TT (2020) Practical machine learning-based prediction model for axial capacity of square CFST columns. Mech Adv Mat Struct 2020:1–16

    Google Scholar 

  27. Le TT, Phan HC (2020) Prediction of ultimate load of rectangular CFST columns using interpretable machine learning method. Adv Civil Eng 2020:8855069

    Google Scholar 

  28. Le T-T, Le MV (2021) Development of user-friendly kernel-based Gaussian process regression model for prediction of load-bearing capacity of square concrete-filled steel tubular members. Mater Struct 54(2):1–24

    Google Scholar 

  29. Soize C et al. (2015) Stochastic representations and statistical inverse identification for uncertainty quantification in computational mechanics. In: (Plenary Lecture) UNCECOMP 2015, 1st ECCOMAS thematic international conference on uncertainty quantification in computational sciences and engineering

  30. Phan HC, Dhar AS (2021) Predicting pipeline burst pressures with machine learning models. Int J Press Vessels Piping 191:104384

    Google Scholar 

  31. Phan HC et al (2021) An empirical model for bending capacity of defected pipe combined with axial load. Int J Press Vessel Piping 191:104368

    Google Scholar 

  32. Mondal BC, Dhar AS (2018) Improved Folias factor and burst pressure models for corroded pipelines. J Press Vessel Technol. https://doi.org/10.1115/1.4038720

    Article  Google Scholar 

  33. Shuai Y, Shuai J, Zhang X (2018) Experimental and numerical investigation of the strain response of a dented API 5L X52 pipeline subjected to continuously increasing internal pressure. J Nat Gas Sci Eng 56:81–92

    Google Scholar 

  34. Diniz J et al (2006) Stress and strain analysis of pipelines with localized metal loss. Exp Mech 46(6):765–775

    Google Scholar 

  35. Oh C-K et al (2007) Ductile failure analysis of API X65 pipes with notch-type defects using a local fracture criterion. Int J Press Vessels Pip 84(8):512–525

    Google Scholar 

  36. Ma B et al (2013) Assessment on failure pressure of high strength pipeline with corrosion defects. Eng Fail Anal 32:209–219

    Google Scholar 

  37. Zheng M et al (2004) Modified expression for estimating the limit bending moment of local corroded pipeline. Int J Press Vessels Pip 81(9):725–729

    Google Scholar 

  38. Chen Y et al (2014) Residual bending capacity for pipelines with corrosion defects. J Loss Prev Process Ind 32:70–77

    Google Scholar 

  39. Trifonov OV, Cherniy VP (2012) Elastoplastic stress–strain analysis of buried steel pipelines subjected to fault displacements with account for service loads. Soil Dyn Earthq Eng 33(1):54–62

    Google Scholar 

  40. Tahghighi H, Hajnorouzi M (2014) Numerical evaluation of the strike-slip fault effects on the steel buried pipelines. J Seismol Earthq Eng 16(4):219

    Google Scholar 

  41. Melissianos VE, Gantes CJ (2015) Failure mitigation of buried steel pipeline under strike-slip fault offset using flexible joints. In: SECED 2015 Conference: Earthquake Risk and Engineering towards a Resilient World

  42. Liu X et al (2016) A semi-empirical model for peak strain prediction of buried X80 steel pipelines under compression and bending at strike-slip fault crossings. J Nat Gas Sci Eng 32:465–475

    Google Scholar 

  43. Gas ASoCECo, Lifelines LF (1984) Guidelines for the seismic design of oil and gas pipeline systems. Am Soc Civil Eng

  44. Zolfaghari A, Izadi M (2020) Burst pressure prediction of cylindrical vessels using artificial neural network. J Press Vessel Technol 142(3):031303

    Google Scholar 

  45. Oh D et al (2020) Burst pressure prediction of API 5L X-grade dented pipelines using deep neural network. J Marine Sci Eng 8(10):766

    Google Scholar 

  46. Keshtegar B, Miri M (2014) Reliability analysis of corroded pipes using conjugate HL–RF algorithm based on average shear stress yield criterion. Eng Fail Anal 46:104–117

    Google Scholar 

  47. Keshtegar B, Meng Z (2017) A hybrid relaxed first-order reliability method for efficient structural reliability analysis. Struct Saf 66:84–93

    Google Scholar 

  48. Keshtegar B et al (2019) Reliability analysis of corroded pipelines: novel adaptive conjugate first order reliability method. J Loss Prev Process Ind 62:103986

    Google Scholar 

  49. Géron A (2019) Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: concepts, tools, and techniques to build intelligent systems. O'Reilly Media

  50. d’Agostino RB (1971) An omnibus test of normality for moderate and large size samples. Biometrika 58(2):341–348

    MathSciNet  MATH  Google Scholar 

  51. D’agostino R, Pearson ES (1973) Tests for departure from normality. empirical results for the distributions of b 2 and√ b. Biometrika 60(3):613–622

    MathSciNet  MATH  Google Scholar 

  52. Massey FJ Jr (1951) The Kolmogorov-Smirnov test for goodness of fit. J Am Stat Assoc 46(253):68–78

    MATH  Google Scholar 

Download references

Acknowledgements

This research was funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under Grant number: 107.02-2020.04.

Author information

Authors and Affiliations

  1. Le Quy Don Technical University, 236 Hoang Quoc Viet, Hanoi, 100000, Vietnam

    Hieu Chi Phan & Nang Duc Bui

Authors
  1. Hieu Chi Phan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nang Duc Bui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nang Duc Bui.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Phan, H.C., Bui, N.D. Failure assessment of defected pipe under strike-slip fault with data-driven models accounting for the model uncertainty. Neural Comput & Applic 34, 1541–1555 (2022). https://doi.org/10.1007/s00521-021-06497-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06497-3

Keywords

Search

Navigation