PSI - Issue 80

Yuhang Pan et al. / Procedia Structural Integrity 80 (2026) 43–52 YuhangPan / Structural Integrity Procedia 00 (2023) 000–000

52

10

References

Yue, N., Khodaei, Z.S., Aliabadi, M.H., 2021. Damage detection in large composite sti ff ened panels based on a novel SHM building block philosophy. Smart Mater. Struct. 30, 045004. Pitarresi, G., Ricotta, M., Meneghetti, G., 2019. Investigation of the crack tip stress field in a stainless steel SENT specimen by means of thermoelastic stress analysis. Procedia Struct. Integr. 18, 330–346. Xu, J., Zhou, X., Giglio, M., Sbarufatti, C., Dong, L., 2024. Variational neural network embedded with digital twins for probabilistic structural damage quantification. AIAA J. 0, 1–13. Vanniamparambil, P.A., Bartoli, I., Hazeli, K., Cuadra, J., Schwartz, E., Saralaya, R., Kontsos, A., 2012. An integrated structural health monitoring approach for crack growth monitoring. J. Intell. Mater. Syst. Struct. 23, 1563–1573. Pan, H., Azimi, M., Yan, F., Lin, Z., 2018. Time-frequency-based data-driven structural diagnosis and damage detection for cable-stayed bridges. Int. J. Bridge Eng. 23, 04018033. Golodov, V.A., Maltseva, A.A., 2022. Approach to weld segmentation and defect classification in radiographic images of pipe welds. NDT & E Int. 127, 102597. Marques, D.E.T., Vandepitte, D., Tita, V., 2021. Damage detection and fatigue life estimation under random loads: A new structural health monitoring methodology in the frequency domain. Fatigue Fract. Eng. Mater. Struct. 44, 1622–1636. Chen, J., Yuan, S., Jin, X., 2019. On-line prognosis of fatigue cracking via a regularized particle filter and guided wave monitoring. Mech. Syst. Signal Process. 131, 1–17. Karimian, S.F., Modarres, M., Bruck, H.A., 2020. A new method for detecting fatigue crack initiation in aluminum alloy using acoustic emission waveform information entropy. Eng. Fract. Mech. 223, 106771. Chai, M., Hou, X., Zhang, Z., Duan, Q., 2022. Identification and prediction of fatigue crack growth under di ff erent stress ratios using acoustic emission data. Int. J. Fatigue 160, 106860. Wu, Z., Qing, X.P., Chang, F.K., 2009. Damage detection for composite laminate plates with a distributed hybrid PZT / FBG sensor network. J. Intell. Mater. Syst. Struct. 20, 1069–1077. Ostachowicz, W., Kudela, P., Malinowski, P., Wandowski, T., 2009. Damage localisation in plate-like structures based on PZT sensors. Mech. Syst. Signal Process. 23, 1805–1829. Sampath, S., Sohn, H., 2022. Detection and localization of fatigue crack using nonlinear ultrasonic three-wave mixing technique. Int. J. Fatigue 155, 106582. Zhao, F., Zhou, X., Wang, C., Dong, L., Atluri, S.N., 2023. Setting adaptive inspection intervals in helicopter components, based on a digital twin. AIAA J. 61, 2675–2688. Pan, Y., Khodaei, Z.S., Aliabadi, M.H.F., 2024. A robust hybrid method for damage detection under varying environmental conditions. Struct. Health Monit. 1–16. Pan, Y., Khodaei, Z.S., Aliabadi, M.H.F., 2025. In-service fatigue crack monitoring through baseline-free automated detection and physics-informed neural network quantification. NDT & E Int. 153, 103360. Paris, P.C., Erdogan, F., 1963. A critical analysis of crack propagation laws. J. Basic Eng. 85, 528–533. Chen, J., Yuan, S., Sbarufatti, C., Jin, X., 2021. Dual crack growth prognosis by using a mixture proposal particle filter and on-line crack monitoring. Reliab. Eng. Syst. Saf. 215, 107758. Wang, S., Tang, S., He, C., Wang, Q., 2023. Cyclic deformation and fatigue failure mechanisms of thermoplastic polyurethane in high cycle fatigue. Polymers 15, 899. https: // doi.org / 10.3390 / polym15040899. Yue, N., Khodaei, Z.S., Aliabadi, M.H., 2018. An innovative secondary bonding of sensors to composite structures for SHM application. Key Eng. Mater. 774, 516–522. https: // doi.org / 10.4028 / www.scientific.net / KEM.774.516. Zhou, X., Dong, L., Dziendzikowski, M., Dragan, K., Giglio, M., Sbarufatti, C., 2025. Real-time in-service load tracking toward airframe digital twins. AIAA J. 1–14. https: // doi.org / 10.2514 / 1.J0XXXXX Xu, J., Zhou, X., Giglio, M., Sbarufatti, C., Dong, L., 2024. Variational neural network embedded with digital twins for probabilistic structural damage quantification. AIAA J. 0, 1–13. https: // doi.org / 10.2514 / 1.J064808 Zhao, F., Zhou, X., Wang, C., Dong, L., Atluri, S.N., 2023. Setting adaptive inspection intervals in helicopter components, based on a digital twin. AIAA J. 61, 2675–2688. https: // doi.org / 10.2514 / 1.J062222 Zhou, X., He, S., Dong, L., Atluri, S.N., 2022. Real-time prediction of probabilistic crack growth with a helicopter component digital twin. AIAA J. 60, 2555–2567. https: // doi.org / 10.2514 / 1.J060890