과제정보
This research is financially supported by the Ministry of Science and Technology of China (Grant No. 2019YFE0112400).
참고문헌
- Abdeljaber, O., Avci, O., Kiranyaz, M.S., Gabbouj, M. and Inman, D.J. (2017), "Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks", J. Sound Vib., 388, 154-170. https://doi.org/10.1016/j.jsv.2016.10.043
- Abdeljaber, O., Avci, O., Kiranyaz, M.S., Boashash, B., Sodano, H., and Inman, D.J. (2018), "1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data", Neurocomputing, 275, 1308-1317. https://doi.org/10.1016/j.neucom.2017.09.069
- Aswal, N., Sen, S. and Mevel, L. (2021), "Estimation of local failure in tensegrity using Interacting Particle-Ensemble Kalman Filter", Mech. Syst. Signal Process., 160, 107824. https://doi.org/10.1016/j.ymssp.2021.107824
- Cao, M., Radzienski, M., Xu, W. and Ostachowicz, W. (2014), "Identification of multiple damage in beams based on robust curvature mode shapes", Mech. Syst. Signal Process, 46(2), 468-480. https://doi.org/10.1016/j.ymssp.2014.01.004
- Cha, Y.-J., Choi, C. and Buyukozturk, O. (2017), "Deep learning-based crack damage detection using convolutional neural networks", Comput.-Aided Civil Infrastr. Eng., 32(5), 361-378. https://doi.org/10.1111/mice.12263
- Huynh, T.-C., Park, J.-H., Jung, H.-J. and Kim, J.-T. (2019), "Quasi-autonomous bolt-loosening detection method using vision-based deep learning and image processing", Automat. Constr., 105, 102844. https://doi.org/10.1016/j.autcon.2019.102844
- Islam, M.M. and Kim, J.-H. (2019), "Vision-based autonomous crack detection of concrete structures using a fully convolutional encoder-decoder network", Sensors, 19(19), 4251. https://doi.org/10.3390/s19194251
- Kim, C.-W., Zhang, Y., Wang, Z.R., Oshima, Y. and Morita, T. (2018), "Long-term bridge health monitoring and performance assessment based on a Bayesian approach", Struct. Infrastr. Eng., 14(7), 883-894. https://doi.org/10.1080/15732479.2018.1436572
- Kordestani, H., Xiang, Y.-Q. and Ye, X.-W. (2018), "Output-only damage detection of steel beam using moving average filter", Shock and Vibration, 2018, 1-13. https://doi.org/10.1155/2018/2067680
- Kordestani, H., Zhang, C.W., Masri, S.F. and Shadabfar, M. (2021), "An empirical time-domain trend line-based bridge signal decomposing algorithm using Savitzky-Golay filter", Struct. Control Health Monitor., 28(7), e2750. https://doi.org/10.1002/stc.2750
- Lee, S., Park, S., Kim, T., Lieu, Q.X. and Lee, J. (2021), "Damage quantification in truss structures by limited sensor-based surrogate model", Appl. Acoust., 172, 107547. https://doi.org/10.1016/j.apacoust.2020.107547
- Lin, Y.Z., Ni, Z.H. and Ma, H.W. (2017), "Structural damage detection with automatic feature-extraction through deep learning", Comput.-Aided Civil Infrastr. Eng., 32(12), 1025-1046. https://doi.org/10.1111/mice.12313
- Maaten, L. and Hinton, G. (2008), "Visualizing data using t-SNE", J. Mach. Learn. Res., 9(86), 2579-2605.
- Nick, H. and Aziminejad, A. (2021), "Vibration-based damage identification in steel girder bridges using artificial neural network under noisy conditions", J. Nondestr. Eval., 40(1). https://doi.org/10.1007/s10921-020-00744-8
- Paz, M. and Kim, Y.H. (2019), Structural Dynamics: Theory and Computation, Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-94743-3
- Santos, F.L.M. dos, Peeters, B., Van der Auweraer, H., Goes, L.C.S. and Desmet, W. (2016), "Vibration-based damage detection for a composite helicopter main rotor blade", Case Stud. Mech. Syst. Signal Process., 3, 22-27. https://doi.org/10.1016/j.csmssp.2016.01.001
- Sharma, S. and Sen, S. (2021), "Bridge damage detection in presence of varying temperature using two-step neural network approach", J. Bridge Eng., 26(6). https://doi.org/10.1061/(ASCE)BE.1943-5592.0001708
- Spencer, B.F., Hoskere, V. and Narazaki, Y. (2019), "Advances in computer vision-based civil infrastructure inspection and monitoring", Eng., 5(2), 199-222. https://doi.org/10.1016/j.eng.2018.11.030
- Sun, L.M., Shang, Z.Q., Xia, Y., Bhowmick, S. and Nagarajaiah, S. (2020), "Review of bridge structural health monitoring aided by big data and artificial intelligence: From condition assessment to damage detection", J. Struct. Eng., 146(5), 04020073. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002535
- Talebpour, M.H., Goudarzi, Y. and Sharifnezhad, M. (2020), "Clustering elements of truss structures for damage identification by CBO", Periodica Polytechnica Civil Engineering, October. https://doi.org/10.3311/PPci.16636
- Teng, S., Chen, G.F., Gong, P.P., Liu, G. and Cui, F.S. (2020), "Structural damage detection using convolutional neural networks combining strain energy and dynamic response", Meccanica, 55(4), 945-959. https://doi.org/10.1007/s11012-019-01052-w
- Wang, S.Q. and Xu, M.Q. (2019), "Modal strain energy-based structural damage identification: a review and comparative study", Struct. Eng. Int., 29(2), 234-248. https://doi.org/10.1080/10168664.2018.1507607
- Wang, R.H., Chencho, An, S., Li, J., Li, L., Hao, H. and Liu, W.Q. (2020), "Deep residual network framework for structural health monitoring", Struct. Health Monitor., 20(4), 147592172091837. https://doi.org/10.1177/1475921720918378
- Xu, J., Hao, J.J., Li, H.N., Luo, M.Z., Guo, W. and Li, W.J. (2017), "Experimental damage identification of a model reticulated shell", Appl. Sci., 7(4), 362. https://doi.org/10.3390/app7040362
- Yu, Y., Wang, C.Y., Gu, X.Y. and Li, J.C. (2019), "A novel deep learning-based method for damage identification of smart building structures", Struct. Health Monitor., 18(1), 143-163. https://doi.org/10.1177/1475921718804132
- Yu, Y., Rashidi, M., Samali, B., Mohammadi, M., Nguyen, T.N. and Zhou, X.X. (2022), "Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm", Struct. Health Monitor., p. 14759217211053546. https://doi.org/10.1177/14759217211053546
- Zhou, Z., Wegner, L.D. and Sparling, B.F. (2021), "Data quality indicators for vibration-based damage detection and localization", Eng. Struct., 230, 111703. https://doi.org/10.1016/j.engstruct.2020.111703