1 |
Li, G., Ma, B., He, S.H., Ren, X.L. and Liu, Q.W. (2020), "Automatic tunnel crack detection based on u-net and a convolutional neural network with alternately updated clique", Sensors, 20(3), 717. https://doi.org/10.3390/s20030717
DOI
|
2 |
Liu, Q. (2019), U-Net Implementation in PyTorch. Retrieved from https://github.com/Qiuyan918/Unet_Implementation_PyTorch/b lob/master/Unet_Implementation_PyTorch.ipynb
|
3 |
Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S. and Zhang, C. (2017), "Learning efficient convolutional networks through network slimming", Proceedings of 2017 IEEE International Conference on Computer Vision, Venice, Italy. (CD-ROM) https://doi.org/10.1109/ICCV.2017.298
DOI
|
4 |
Ni, Y.Q., Ye, X.W. and Ko, J.M. (2012), "Modeling of stress spectrum using long-term monitoring data and finite mixture distributions", J. Eng. Mech., 138(2), 175-183. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000313
DOI
|
5 |
Ryu, E., Kang, J., Lee, J., Shin, Y. and Kim, H. (2020), "Automated detection of surface cracks and numerical correlation with thermal-structural behaviors of fire damaged concrete beams", Int. J. Concrete Struct. Mater., 14(1), 12. https://doi.org/10.1186/s40069-019-0387-3
DOI
|
6 |
Spencer Jr., B.F., Hoskere, V. and Narazaki, Y. (2019), "Advances in computer vision-based civil infrastructure inspection and monitoring", Engineering, 5(2), 199-222. https://doi.org/10.1016/j.eng.2018.11.030
DOI
|
7 |
Mondal, T.G. and Jahanshahi, M.R. (2020), "Autonomous vision-based damage chronology for spatiotemporal condition assessment of civil infrastructure using unmanned aerial vehicle", Smart Struct. Syst., Int. J., 25(6), 733-749. https://doi.org/10.12989/sss.2020.25.6.733
DOI
|
8 |
Ni, Y.Q., Ye, X.W. and Ko, J.M. (2010), "Monitoring-based fatigue reliability assessment of steel bridges: analytical model and application", J. Struct. Eng., 136(12), 1563-1573. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000250
DOI
|
9 |
Li, S.Y. and Zhao, X.F. (2019), "Image-based concrete crack detection using convolutional neural network and exhaustive search technique", Adv. Civil Eng., 2019. https://doi.org/10.1155/2019/6520620
DOI
|
10 |
Tang, Z.Y., Chen, Z.C., Bao, Y.Q. and Li, H. (2019), "Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring", Struct. Control. Health Monitor., 26(1), e2296. https://doi.org/10.1002/stc.2296
DOI
|
11 |
Xu, Y., Wei, S.Y., Bao, Y.Q. and Li, H. (2019), "Automatic seismic damage identification of reinforced concrete columns from images by a region-based deep convolutional neural network", Struct. Control. Health Monitor., 26(3), e2313. https://doi.org/10.1002/stc.2313
DOI
|
12 |
Ye, X.W., Ni, Y.Q., Wong, K.Y. and Ko, J.M. (2012), "Statistical analysis of stress spectra for fatigue life assessment of steel bridges with structural health monitoring data", Eng. Struct., 45, 166-176. https://doi.org/10.1016/j.engstruct.2012.06.016
DOI
|
13 |
Ye, X.W., Ni, Y.Q., Wai, T.T., Wong, K.Y., Zhang, X.M. and Xu, F. (2013), "A vision-based system for dynamic displacement measurement of long-span bridges: algorithm and verification", Smart Struct. Syst., Int. J., 12(3-4), 363-379. https://doi.org/10.12989/sss.2013.12.3_4.363
DOI
|
14 |
Ronneberger, O., Fischer, P. and Brox, T. (2015), "U-net: convolutional networks for biomedical image segmentation", Proceedings of the 18th International Conference on Medical Image Computing and Computer Assisted Intervention, Munich, Germany. (CD-ROM) https://doi.org/10.1007/978-3-319-24574-4_28
DOI
|
15 |
Ye, X.W., Jin, T. and Yun, C.B. (2019a), "A review on deep learning-based structural health monitoring of civil infrastructures", Smart Struct. Syst., Int. J., 24(5), 567-585. https://doi.org/10.12989/sss.2019.24.5.567
DOI
|
16 |
Ye, X.W., Jin, T. and Chen, P.Y. (2019b), "Structural crack detection using deep learning-based fully convolutional networks", Adv. Struct. Eng., 22(16), 3412-3419. https://doi.org/10.1177/1369433219836292
DOI
|
17 |
Ye, X.W., Jin, T., Ang, P.P., Bian, X.C. and Chen, Y.M. (2021), "Computer vision-based monitoring of the 3-D structural deformation of an ancient structure induced by shield tunneling construction", Struct. Control. Health Monitor., 28(4), e2702. https://doi.org/10.1002/stc.2702
DOI
|
18 |
Bao, Y.Q., Tang, Z.Y. and Li, H. (2019), "Compressive-sensing data reconstruction for structural health monitoring: a machine-learning approach", Struct. Health Monitor., 19(1), 293-304. https://doi.org/10.1177/1475921719844039
DOI
|
19 |
Abdel-Qader, I., Abudayyeh, O. and Kelly, M.E. (2003), "Analysis of edge-detection techniques for crack identification in bridges", J. Comput. Civil Eng., 17(4), 255-263. https://doi.org/10.1061/(ASCE)0887-3801(2003)17:4(255)
DOI
|
20 |
Alipour, M., Harris, D.K. and Miller, G.R. (2019), "Robust pixel-level crack detection using deep fully convolutional neural networks", J. Comput. Civil Eng., 33(6), 04019040. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000854
DOI
|
21 |
Fujita, Y. and Hamamoto, Y. (2011), "A robust automatic crack detection method from noisy concrete surfaces", Mach. Vision Appl., 22(2), 245-254. https://doi.org/10.1007/s00138-009-0244-5
DOI
|
22 |
Gresil, M., Yu, L., Shen, Y. and Giurgiutiu, V. (2013), "Predictive model of fatigue crack detection in thick bridge steel structures with piezoelectric wafer active sensors", Smart Struct. Syst., Int. J., 12(2), 97-119. https://doi.org/10.12989/sss.2013.12.2.097
DOI
|
23 |
Li, S.Y. and Zhao, X.F. (2020), "Automatic crack detection and measurement of concrete structure using convolutional encoder-decoder network", IEEE Access, 8, 134602-134618. https://doi.org/10.1109/ACCESS.2020.3011106
DOI
|
24 |
Hakim, S.J.S. and Razak, H.A. (2014), "Modal parameters based structural damage detection using artificial neural networks - a review", Smart Struct. Syst., Int. J., 14(2), 159-189. https://doi.org/10.12989/sss.2014.14.2.159
DOI
|
25 |
Jang, J., Shin, M., Lim, S., Park, J. and Paik, J. (2019), "Intelligent image-based railway inspection system using deep learning-based object detection and weber contrast-based image comparison", Sensors, 19(21), 4738. https://doi.org/10.3390/s19214738
DOI
|
26 |
Kingma, D.P. and Ba, J.L. (2015), "Adam: a method for stochastic optimization", Proceedings of the 3rd International Conference on Learning Representations, San Diego, CA, USA. (CD-ROM)
|