Open Access
Issue
MATEC Web Conf.
Volume 413, 2025
International Conference on Measurement, AI, Quality and Sustainability (MAIQS 2025)
Article Number 01003
Number of page(s) 9
Section Advanced Measurement
DOI https://doi.org/10.1051/matecconf/202541301003
Published online 01 October 2025
  1. H. Lai, D. Fan, K. Liu, The Effect of Welding Defects on the Long-Term Performance of HDPE Pipes. Polymers 14, 3936 (2022). [Google Scholar]
  2. X. L. Meng, Study on Safety Status Assessment System of Polyethylene Gas Pipes in Urban Areas. Master Thesis, China University of Petroleum (Beijing), China, 2017. [Google Scholar]
  3. H. Nie et al. Preliminary study on terahertz nondestructive testing for defect detection in hot melt joints of polyethylene pipes. Infrared Phys. Technol. 139, 105-300 (2024). [Google Scholar]
  4. H. S. Lai, N. N. Tun, K. B. Yoon, S. H. Kil, Effects of defects on failure of butt fusion welded polyethylene pipe. Int. J. Pressure Vessels Pip. 139, 117-122 (2016). [Google Scholar]
  5. R. K. Burdick, C. M. Borror, D. C. Montgomery, A Review of Methods for Measurement Systems Capability Analysis. J. Qual. Technol. 35 (4), 342-354 (2003). [Google Scholar]
  6. Z. He, Y. Zhao, F. Zou, Research and Application of Methods for Attribute Measurement System Analysis. Industrial Engineering Journal. 82–85+121 (2008). [Google Scholar]
  7. M. S. Alavijeh, R. Scott, F. Seviaryn, R. G. Maev, Using machine learning to automate ultrasoundbased classification of butt-fused joints in medium-density polyethylene gas pipes. J. Acoust. Soc. Am. 150, 561-572 (2021). [Google Scholar]
  8. R. Kafieh, T. Lotfi, R. Amirfattahi, Automatic detection of defects on polyethylene pipe welding using thermal infrared imaging. Infrared Phys. Technol. 54, 317-325 (2011). [Google Scholar]
  9. S. Park et al., Early detection of steel tube welded joint failure using SPC-I nonlinear ultrasonic technique. Struct Health Monit. 24, 148-163 (2025). [Google Scholar]
  10. F. Zuo, J. Liu, X. Zhao, L. Chen, L. Wang, An X- Ray-Based Automatic Welding Defect Detection Method for Special Equipment System. IEEE- ASME Trans. Mechatron. 29, 2241-2252 (2024). [Google Scholar]
  11. Y. Li, M. Hu, T. Wang, Visual inspection of weld surface quality. J. Intell. Fuzzy Syst. 39, 5075–5084 (2020). [Google Scholar]
  12. E. Mirmahdi, D. Afshari, M. A. Karimi Ivanaki, A Review of Ultrasonic Testing Applications in Spot Welding: Defect Evaluation in Experimental and Simulation Results. Trans. Indian Inst. Met. 76, 1381-1392 (2023). [Google Scholar]
  13. W. Qian, S. Dong, L. Chen, Q. Ren, Image enhancement method for low-light pipeline weld X-ray radiographs based on weakly supervised deep learning. NDT E Int. 143, 103049 (2024). [Google Scholar]
  14. Y. Zhang, Y.-M. Cheung, Graph-Based Dissimilarity Measurement for Cluster Analysis of Any-Type-Attributed Data. IEEE Trans. Neural Netw. Learn. Syst. 34, 6530-6544 (2023). [Google Scholar]
  15. R. Xiang, Y. Chen, J. W. Shen, S. Hu, Method for assessing the quality of data used in evaluating the performance of recognition algorithms for fruits and vegetables. Biosyst. Eng. 156, 27-37 (2017). [Google Scholar]
  16. K. Zhu et al., Robotic MAG welding defects and quality assessment with a defect threshold decision model-driven method. Mech Syst. Signal Proc. 224, 112056 (2025). [Google Scholar]
  17. J. R. Landis, G. G. Koch, The measurement of observer agreement for categorical data. Biometrics 33, 159-174 (1977). [CrossRef] [Google Scholar]
  18. S. Senn, Review of Fleiss, statistical methods for rates and proportions. Res. Synth. Methods 2, 221-222 (2011). [Google Scholar]
  19. S. Burt, L. Punnett, Evaluation of interrater reliability for posture observations in a field study. Appl. Ergon. 30, 121-135 (1999). [Google Scholar]
  20. Automotive Industry Action Group (AIAG), Measurement Systems Analysis Reference Manual, 4th edition, Troy, Mich: Chrysler, Ford, General Motors Supplier Quality Requirements Task Force, 2010. [Google Scholar]
  21. J. R. Baldwin, A. Reuben, J. B. Newbury, A. Danese, Agreement Between Prospective and Retrospective Measures of Childhood Maltreatment: A Systematic Review and Metaanalysis. JAMA Psychiatry 76, 584-593 (2019). [Google Scholar]
  22. J. Yang, V. M. Chinchilli, Fixed-effects modeling of Cohen’s weighted kappa for bivariate multinomial data. Comput. Stat. Data Anal. 55, 1061-1070 (2011). [Google Scholar]
  23. T. Yu, Z. Chen, H. Yin, N. Yi, M. Zhao, Statistical issues on evaluating agreement between THC and 11-OH-THC analysis in hair samples by Cohen’s kappa. Forensic Sci. Int. 343, 111496 (2023). [Google Scholar]
  24. M. H. Lerchbaumer et al., Point-of-care lung ultrasound in COVID-19 patients: inter- and intraobserver agreement in a prospective observational study. Sci Rep. 11, 10678 (2021). [Google Scholar]
  25. K. L. Gwet, Large-Sample Variance of Fleiss Generalized Kappa. Educ. Psychol. Meas. 81, 781-790 (2021). [Google Scholar]
  26. H. Brenner, U. Kliebsch, Dependence of weighted kappa coefficients on the number of categories. Epidemiology 7, 199-202 (1996). [CrossRef] [Google Scholar]
  27. N. Moradzadeh, M. Ganjali, T. Baghfalaki, Weighted kappa as a function of unweighted kappas. Commun. Stat.-Simul. Comput. 46, 3769-3780 (2017). [Google Scholar]
  28. Q. Wang, H. Zhou, J. Xie, X. Xu, Nonlinear ultrasonic evaluation of high-density polyethylene natural gas pipe thermal butt fusion joint aging behavior. Int. J. Pressure Vessels Pip. 189, 104272 (2021). [Google Scholar]
  29. Q. Bao, B. Wang, M. Li, C. Li, J. Gao, The failure mechanism of field polyethylene gas pipeline with gas leakage at electrofusion joint. Anti-Corros. Methods Mater. 71, 676-685 (2024). [Google Scholar]
  30. S. Deveci, N. Antony, S. Nugroho, B. Eryigit, Effect of carbon black distribution on the properties of polyethylene pipes part 2: Degradation of butt fusion joint integrity. Polym. Degrad. Stabil. 162, 138-147 (2019). [Google Scholar]

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