Page 98 - Read Online
P. 98
Page 404 Wang et al. Intell Robot 2022;2(4):391-406 https://dx.doi.org/10.20517/ir.2022.25
detection on concrete bridges. IEEE Trans Ind Inf 2021;17:5485-94. DOI
11. Yamaguchi T, Mizutani T, Tarumi M, Su D. Sensitive damage detection of reinforced concrete bridge slab by “time-variant
deconvolution” of SHF-band radar signal. IEEE Trans Geosci Remote Sensing 2019;57:1478-88. DOI
12. Xu H, Su X, Wang Y, Cai H, Cui K, Chen X. Automatic bridge crack detection using a convolutional neural network. Applied
Sciences 2019;9:2867. DOI
13. Gao R, He J. Seismic performance assessment of concrete bridges with traffic-induced fatigue damage. Eng Fail Anal
2022;134:106042. DOI
14. Lon Wah W, Xia Y. Elimination of outlier measurements for damage detection of structures under changing environmental conditions.
Struct Health Monit 2022;21:320-38. DOI
15. Guo L, Li R, Jiang B. A cascade broad neural network for concrete structural crack damage automated classification. IEEE Trans
Industr Inform 2021;17:2737-42. DOI
16. Okazaki Y, Okazaki S, Asamoto S, Chun P. Applicability of machine learning to a crack model in concrete bridges. COMPUT-AIDED
CIV INF 2020;35:775-92. DOI
17. Lu Q, Zhu J, Zhang W. Quantification of fatigue damage for structural details in slender coastal bridges using machine learning-based
methods. J Bridge Eng 2020;25:04020033. DOI
18. Li G, Liu Q, Zhao S, Qiao W, Ren X. Automatic crack recognition for concrete bridges using a fully convolutional neural network and
naive Bayes data fusion based on a visual detection system. Meas Sci Technol 2020;31:075403. DOI
19. Kumar P, Sharma A, Kota SR. Automatic multiclass instance segmentation of concrete damage using deep learning model. IEEE
Access 2021;9:90330-45. DOI
20. Pathak N. Bridge health monitoring using CNN. 2020 International Conference on Convergence to Digital World - Quo Vadis
(ICCDW); 2020 Feb. 18-20; Mumbai, India: IEEE; 2020. p. 1-4. DOI
21. Attard L, Debono CJ, Valentino G, Castro MD, Masi A, Scibile L. Automatic crack detection using mask R-CNN. 2019 11th
International Symposium on Image and Signal Processing and Analysis (ISPA); 2019 Sept. 23-25 Dubrovnik, Croatia: IEEE; 2019. p.
152-157. DOI
22. Kamada S, Ichimura T, Iwasaki T. An adaptive structural learning of deep belief network for image-based crack detection in concrete
structures using SDNET2018. 2020 International Conference on Image Processing and Robotics (ICIP); 2020 Mar. 06-08 Negombo,
Sri Lanka: IEEE; 2020. p. 1-6. DOI
23. Liu T, Zhang L, Zhou G, Cai W, Cai C, Li L. BC-DUnet-based segmentation of fine cracks in bridges under a complex background.
PLoS One 2022;17:e0265258. DOI PubMed PMC
24. Simonetti F, Satow IL, Brath AJ, et al. Cryo-ultrasonic NDE: ice-cold ultrasonic waves for the detection of damage in complex-shaped
engineering components. IEEE Trans Ultrason Ferroelectr Freq Control 2018;65:638-47. DOI PubMed
25. Zhou LQ, Colston G, Myronov M, et al. Ultrasonic inspection and self-healing of ge and 3C-SiC semiconductor membranes. J
Microelectromech Syst 2020;29:370-7. DOI
26. Lee FW, Chai HK, Lim KS, Lau SH. Concrete sub-surface crack characterization by means of surface rayleigh wave method. ACI
Materials Journal 2019:116. DOI
27. Wadas SH, Tschache S, Polom U, Krawczyk CM. Ground instability of sinkhole areas indicated by elastic moduli and seismic
attributes. Geophys J Int 2020;222:289-304. DOI
28. Ghasemi MF, Bayuk IO. Application of rock physics modelling to investigate the differences between static and dynamic elastic
moduli of carbonates. Geophys J Int 2020;222:1992-2023. DOI
29. Luan X, Huang B, Sedghi S, Liu F. Probabilistic PCR based near-infrared modeling with temperature compensation. ISA Trans
2018;81:46-51. DOI PubMed
30. Zhu J, Ge Z, Song Z. Distributed parallel PCA for modeling and monitoring of large-scale plant-wide processes with big data. IEEE
Trans Ind Inf 2017;13:1877-85. DOI
31. Yu Y, Rashidi M, Samali B, Yousefi AM, Wang W. Multi-image-feature-based hierarchical concrete crack identification framework
using optimized SVM multi-classifiers and D-S fusion algorithm for bridge structures. Remote Sens 2021;13:240. DOI
32. Mei Q, Gül M, Boay M. Indirect health monitoring of bridges using Mel-frequency cepstral coefficients and principal component
analysis. Mech Syst Signal Process 2019;119:523-46. DOI
33. Dong Z, Sun X, Xu F, Liu W. A low-rank and sparse decomposition-based method of improving the accuracy of sub-pixel grayscale
centroid extraction for spot images. IEEE Sensors J 2020;20:5845-54. DOI
34. Li L, Gao X, Sun R, Lu C. Study on bridge floor crack classification method based on sparse coding. J Light Technol 2018;33:66-74.
DOI
35. Wang B, Zhang Q, Zhao W. Fast concrete crack detection method via L2 sparse representation. Electron lett 2018;54:752-4. DOI
36. Naveed K, Rehman NU. Wavelet based multivariate signal denoising using mahalanobis distance and EDF statistics. IEEE Trans
Signal Process 2020;68:5997-6010. DOI
37. Nguyen TQ, Vuong LC, Le CM, Ngo NK, Nguyen-Xuan H. A data-driven approach based on wavelet analysis and deep learning for
identification of multiple-cracked beam structures under moving load. Meas Sci Technol 2020;162:1-21. DOI
38. Nigam R, Singh SK. Crack detection in a beam using wavelet transform and photographic measurements. Structures 2020;25:436-47.
DOI
39. Wang S, Feng J, Jiang Y. Input-output method to fault detection for discrete-time fuzzy networked systems with time-varying delay