References:
1. Rafiei, M.H. and Adeli, H. "A novel machine learningbased algorithm to detect damage in high-rise building structures", Struct. Des. Tall. Spec., 26, e1400 (2017).
2. Liu, Y.F., Nie, X., Fan, J.S., et al. "Image-based crack assessment of bridge piers using unmanned aerial vehicles and three-dimensional scene reconstruction", Computer-Aided Civil and Infrastructure Engineering, 35(5), pp. 1-19 (2020).
3. Zhang, C., Chang, C.C., and Jamshidi, M. "Concrete bridge surface damage detection using a single-stage detector", Computer-Aided Civil and Infrastructure Engineering, 35(4), pp. 1-21 (2020).
4. El Hajj, B., Schoefs, F., Castanier, B., et al. "A condition-based deterioration model for the stochastic dependency of corrosion rate and crack propagation in corroded concrete structure", Computer-Aided Civil and Infrastructure Engineering, 32(1), pp. 18-33 (2017).
5. Yang, X., Li, H., Yu, Y., et al. "Automatic pixel-level crack detection and measurement using fully convolutional network", Computer-Aided Civil and Infrastructure Engineering, 33(12), pp. 1090-1109 (2018).
6. Kong, X. and Li, J. "Vision-based fatigue crack detection of steel structures using video feature tracking", Computer-Aided Civil and Infrastructure Engineering, 33(9), pp. 783-799 (2018).
7. Amezquita-Sanchez, J.P., Valtierra-Rodriguez, M., and Adeli, H. "Current efforts for prediction and assessment of natural disasters: Earthquakes, tsunamis, volcanic eruptions, hurricanes, tornados, and floods", Sci. Iran., 24(6), pp. 2645-2664 (2017).
8. Deng, J., Lu, Y., and Lee, V.C.S. "Concrete crack detection with handwriting script interferences using faster region-based convolutional neural network", Computer-Aided Civil and Infrastructure Engineering, 35(4), pp. 1-16 (2020).
9. Thons, S. "On the value of monitoring information for the structural risk and integrity management", Computer-Aided Civil and Infrastructure Engineering, 33(1), pp. 79-94 (2018).
10. Huang, Y., Beck, J.L., and Li, H. "Multitask sparse Bayesian learning with applications in structural health monitoring", Computer-Aided Civil and Infrastructure Engineering, 34(9), pp. 732-754 (2019).
11. Yuen, K.V., Kuok, S.C., and Dong, L. "Self-calibrating Bayesian real-time system identification", Computer-Aided Civil and Infrastructure Engineering, 34(9), pp. 806-821 (2019).
12. Zhang, Y., Miyamori, Y., Mikami, S., et al. "Vibrationbased structural state identification by a 1-dimensional convolutional neural network", Computer-Aided Civil and Infrastructure Engineering, 34(9), pp. 822-839 (2019).
13. Amezquita-Sanchez, J.P. and Adeli, H. "Signal processing techniques for vibration-based health monitoring of smart structures", Arch. Comput. Method. E., 23(1), pp. 1-15 (2016).
14. Amezquita-Sanchez, J.P. and Adeli, H. "Feature extraction and classification techniques for health monitoring of structures", Sci. Iran., 22(6), pp. 1931-1940 (2015).
15. Koziarski, M. and Cyganek, B. "Image recognition with deep neural networks in presence of noise -dealing with and taking advantage of distortions", Integrated Computer-Aided Engineering, 24(4), pp. 337-350 (2017).
16. Wang, P. and Bai, X. "Regional parallel structure based CNN for thermal infrared face identification", Integrated Computer-Aided Engineering, 25(3), pp. 247-260 (2018).
17. Molina-Cabello, M.A., Luque-Baena, R.M., Lopez-Rubio, E., and Thurnhofer-Hemsi, K. "Vehicle type detection by ensembles of convolutional neural networks operating on super-resolved images", Integrated Computer-Aided Engineering, 25(4), pp. 321- 333 (2018).
18. Torres, J.F., Galicia, A., Troncoso, A., Martinez-Alvarez, F. "A scalable approach based on deep learning for big data time series forecasting", Integrated Computer-Aided Engineering, 25(4), pp. 335-348 (2018).
19. Schetinin, V., Jakaite, L., and Krzanowski, W. "Bayesian learning of models for estimating uncertainty in alert systems: Application to aircraft collision avoidance", Integrated Computer-Aided Engineering, 25(3), pp. 229-245 (2018).
20. Morabito, F.C., Campolo, M., Mammone, N., et al. "Deep learning representation from electroencephalography of early-stage Creutzfeld-Jakob disease and features for differentiation from rapidly progressive dementia", Int. J. Neural Syst., 27(2), 1650039 (15pages) (2017).
21. Zhang, Y., Wang, Y., Jin, J., et al. "Sparse Bayesian learning for obtaining sparsity of EEG frequency bands based feature vectors in motor imagery classification", Int. J. Neural Syst., 27(2), 1650032 (13 pages) (2017).
22. Fernandez, A., Carmona, C.J., del Jesus M.J., and Herrera, F. "A pareto based ensemble with feature and instance selection for learning from multi-class imbalanced datasets", Int. J. Neural Syst., 27(6), 1750028 (21 pages) (2017).
23. Schetinin, V., Jakaite, L., Nyah, N., et al. "Feature extraction with GMDH-type neural networks for EEGbased person identification", Int. J. Neural Syst., 28(6), 1750064 (23 pages) (2018).
24. Rafiei, M.H. and Adeli, H. "A novel unsupervised deep learning model for global and local health condition assessment of structures", Eng. Struct., 156, pp. 598- 607 (2018).
25. Perez-Ramirez, C.A., Amezquita-Sanchez, J.P., Valtierra-Rodriguez, M., et al. "Recurrent neural network model with Bayesian training and mutual information for response prediction of large buildings", Eng. Struct., 178, pp. 603-615 (2019).
26. Ahmadlou, M., Adeli, H., and Adeli, A. "Fractality and a wavelet-chaos-neural network methodology for EEG-based diagnosis of autistic spectrum disorder", Clin. Neurophysiol., 27(5), pp. 328-333 (2010).
27. Ahmadlou, M., Adeli, H., and Adeli, A. "Fractality and a wavelet-chaos-methodology for EEG-based diagnosis of Alzheimer disease", Alz. Dis. Assoc. Dis., 25(1), pp. 85-92 (2011).
28. An, Y. and Ou, J. "Experimental and numerical studies on damage localization of simply supported beams based on curvature difference probability method of waveform fractal dimension", J. Intel. Mat. Syst. Str., 23(4), pp. 415-426 (2012).
29. Li, H., Tao, D., Huang, Y., et al. "A data-driven approach for seismic damage detection of shear-type building structures using the fractal dimension of timefrequency features", Struct. Control Hlth., 20(9), pp. 1191-1210 (2013).
30. Amezquita-Sanchez, J.P. and Adeli, H. "Synchrosqueezed wavelet transform-fractality model for locating, detecting, and quantifying damage in smart highrise building structures", Smart Mater. Struct., 24, 065034 (14 pages) (2015).
31. Ni, Y.Q., Zhou, X.T., and Ko, J.M. "Experimental investigation of seismic damage identification using PCA-compressed frequency response functions and neural networks", J. Sound Vib., 290(1-2), pp. 242- 263 (2006).
32. Huang, Y., Li, H., Wu, S., et al. "Fractal dimension based damage identification incorporating multitask sparse Bayesian learning", Smart Mater. Struct., 27(7), 075020 (2018).
33. Zhang, Y., Wang, Y., Jin, J., and Wang, X. "Sparse Bayesian Learning for obtaining sparsity of EEG frequency bands based feature vectors in motor imagery classification", International Journal of Neural Systems, 27(2), 1650032 (13 pages) (2017).
34. Castillo, E., Grande, Z., Mora, E., Xu, X., and Lo, H.K. "Proactive, backward analysis and learning in road probabilistic Bayesian network models", Computer-Aided Civil and Infrastructure Engineering, 32(10), pp. 820-835 (2017).
35. Shannon, C.E. "A mathematical theory of communication", Bell Syst. Tech. J., 27, pp. 379-423 (1948).
36. Martinez-Rodrigo, A., Garcia-Martinez, B., Alcaraz, R., et al. "Multiscale entropy analysis for recognition of visually elicited negative stress from EEG recordings", Int. J. Neural Syst., 29(2), 1850038 (17 pages) (2019).
37. Wu, Z., Zhang, Q., Wang, L., et al. "Early fault detection method for rotating machinery based on harmonic-assisted multivariate empirical mode decomposition and transfer entropy", Entropy, 20(11), 873 (22 pages) (2018).
38. Li, H., Bao, Y., and Ou, J. "Structural damage identification based on integration of information fusion and shannon entropy", Mech. Syst. Signal Pr., 22(6), pp. 1427-1440 (2008).
39. Gonzalez, A., Covian, E., Casero, M., et al. "Experimental testing of a cross-entropy algorithm to detect damage", Eng. Mat., 569, pp. 1170-1177 (2013).
40. Lin, T.K. and Liang, J.C. "Application of multi-scale (cross-) sample entropy for structural health monitoring", Smart Mater. Struct., 24(8), 085003 (2015).
41. Moreno-Gomez, A., Amezquita-Sanchez, J., Valtierra- Rodriguez, M., et al. "EMD-Shannon entropy-based methodology to detect incipient damages in a truss structure", Appl. Sci., 8(11), 2068 (2018).
42. Wu, W.H., Chen, C.C., Jhou, J.W., et al. "A rapidly convergent empirical mode decomposition method for analyzing the environmental temperature effects on stay cable force", Computer-Aided Civil and Infrastructure Engineering, 33(8), pp. 672-690 (2018).
43. Wang, F., Ho, S.C.M., and Song, G. "Monitoring of early looseness of multi-bolt connection: a new entropy-based active sensing method without saturation", Smart Mater. Struct., 28(10), 10LT01 (2019).
44. Ren, W.X. and Sun, Z.S. "Structural damage identification by using wavelet entropy", Eng. Struct., 30(10), pp. 2840-2849 (2008).
45. Amiri, M., Modarres, M., and Droguett, E.L. "AE entropy for detection of fatigue crack initiation and growth", In 2015 IEEE Conference on Prognostics and Health Management, Austin, TX, USA, pp. 1-8 (2015).
46. Meruane, V. and Ortiz-Bernardin, A. "Structural damage assessment using linear approximation with maximum entropy and transmissibility data", Mech. Syst. Signal Pr., 54, pp. 210-223 (2015).
47. Kaiser, J.F. "On a simple algorithm to calculate the "energy" of a signal", In 1990 IEEE International Conference on Acoustics, Speech, and Signal Processing, Albuquerque, NM, USA, pp. 381-384 (1990).
48. Paul, J.K., Iype, T., Dileep, R., et al. "Characterization of fibromyalgia using sleep EEG signals with nonlinear dynamical features", Comput. Biol. Med., 111, 103331 (2019).
49. Razi, P., Esmaeel, R.A., and Taheri, F. "Application of a robust vibration-based non-destructive method for detection of fatigue cracks in structures", Smart Mater. Struct., 20(11), 115017 (2011).
50. Garcia-Perez, A., Amezquita-Sanchez, J. P., Dominguez-Gonzalez, A., et al. "Fused empirical mode decomposition and wavelets for locating combined damage in a truss-type structure through vibration analysis", J. Zhejiang UNIV-SC A., 14(9),
pp. 615-630 (2013).
51. Dai, H. and Cao, Z. "A wavelet support vector machine-based neural network meta model for structural reliability assessment", Computer-Aided Civil and Infrastructure Engineering, 32(4), pp. 344-357 (2017).
52. Yuan, Q., Zhou, W., Xu, F., et al. "Epileptic EEG identification via lbp operators on wavelet coefficients", Int. J. Neural Syst., 28(8), 1850010 (16 pages) (2018).
53. Facchini, G., Bernardini, L., Atek, S., et al. "Use of the wavelet packet transform for pattern recognition in a structural health monitoring application", J. Intel. Mat. Syst. Str., 26(12), pp. 1513-1529 (2015).
54. Chang, Z., De Luca, F., and Goda, K. "Automated classification of near-fault acceleration pulses using wavelet packets", Computer-Aided Civil and Infrastructure Engineering, 34(7), pp. 569-585 (2019).
55. Pan, Y., Zhang, L., Wu, X., et al. "Structural health monitoring and assessment using wavelet packet energy spectrum", Saf. Sci., 120, pp. 652-665 (2019).
56. An, Y. and Ou, J. "A signal energy change-based damage localization approach for beam structures", Meas., 48, pp. 208-219 (2014).
57. Suarez, E., Benavent-Climent, A., Molina-Conde, R., et al. "Wavelet energy ratio index for health monitoring of hysteretic dampers", Struct. Control Hlth., 25(2), e2071 (2018).
58. Hoque, N., Bhattacharyya, D.K., and Kalita, J.K. "MIFS-ND: A mutual information-based feature selection method", Expert Syst. Appl., 41(14), pp. 6371- 6385 (2014).
59. Bazan, G.H., Scalassara, P.R., Endo, W., et al. "Stator fault analysis of three-phase induction motors using information measures and artificial neural networks", Electr. Pow. Syst. Res., 143, pp. 347-356 (2017).
60. Zaidan, M.A., Haapasilta, V., Relan, R., et al. "Exploring non-linear associations between atmospheric new-particle formation and ambient variables: a mutual information approach", Atmospheric Chem. Phys., 18(17), pp. 12699-12714 (2018).
61. Trendafilova, I., Palazzetti, R., and Zucchelli, A. "Damage assessment based on general signal correlation. Application for delamination diagnosis in composite structures", Eur. J. Mech. A-Solid., 49, pp. 197- 204 (2015).
62. Sudu-Ambegedara, A., Sun, J., Janoyan, K., et al. "Information-theoretical noninvasive damage detection in bridge structures", Chaos, 26(11), 116312 (2016).
63. Zhang, A., Wang, K.C.P., Fei, Y., et al. "Automated pixel-level pavement crack detection on 3d asphalt surfaces with a recurrent neural network", Computer- Aided Civil and Infrastructure Engineering, 34(3), pp. 213-229 (2019).
64. Nashnush, E. and Vadera, S. "Learning cost-sensitive Bayesian networks via direct and in-direct methods", Integrated Computer-Aid Engineering, 24(1), pp. 17- 26 (2017).
65. Wang, J., Liu, X.Z., and Ni, Y.Q. "A Bayesian probabilistic approach for acoustic emission based rail condition assessment", Computer-Aided Civil and Infrastructure Engineering, 33(1), pp. 21-34 (2018).
66. Babajanian-Bisheh, H., Ghodrati-Amiri, G., Nekooei, M., et al. "Damage detection of a cable-stayed bridge using feature extraction and selection methods", Struct. infrastruct. E., 15(9), pp. 1165-1177 (2019).
67. Mahalanobis, P.C., On the Generalized Distance in Statistics, National Institute of Science of India, (1936).
68. G lowacz, Z. and Kozik, J. "Feature selection of the armature winding broken coils in synchronous motor using genetic algorithm and Mahalanobis distance", Arch. Metall. Mater., 57(3), pp. 829-835 (2012).
69. Stockl, S. and Hanke, M. "Financial applications of the mahalanobis distance", Appl. Econ. Finan., 1(2), pp. 78-84 (2014).
70. Galeano, P., Joseph, E., and Lillo, R.E. "The Mahalanobis distance for functional data with applications to classification", Technometrics, 57(2), pp. 281-291 (2015).
71. Lahdhiri, H., Taouali, O., Elaissi, I., et al. "A new fault detection index based on Mahalanobis distance and kernel method", Int. J. Adv. Manuf. Tech., 91(5- 8), pp. 2799-2809 (2017).
72. Chen, J., Li, Q., Li, P., et al. "Saliency prediction by Mahalanobis distance of topological feature on deep color components", J. Vis. Commun. Image. R., 60, pp. 149-157 (2019).
73. Mosavi, A.A., Dickey, D., Seracino, R., et al. "Identifying damage locations under ambient vibrations utilizing vector autoregressive models and Mahalanobis distances", Mech. Syst. Signal Pr., 26, pp. 254-267 (2012).
74. Zhou, Y.L., Figueiredo, E., Maia, N., et al. "Damage detection in structures using a transmissibility-based Mahalanobis distance", Struct. Control Hlth., 22(10), pp. 1209-1222 (2015).
75. George, R.C., Mishra, S.K., and Dwivedi, M. "Mahalanobis distance among the phase portraits as damage feature", Struct. Health Monit., 17(4), pp. 869-887 (2018).
76. Sevcik, C. "A procedure to estimate the fractal dimension of waveforms", Complex Int., 5, pp. 1-19 (1998).
77. Wang, H., Li, J., Guo, L., et al. "Fractal complexitybased feature extraction algorithm of communication signals", Fractals, 25(4), 1740008 (2017).
78. Valtierra-Rodriguez, M. "Fractal dimension and data mining for detection of short-circuited turns in transformers from vibration signals", Meas. Sci. Technol., 31(2), 025902 (23 pages) (2020).
79. Lacasa, L., Luque, B., Ballesteros, F., et al. "From time series to complex networks: The visibility graph", Proc. Natl. Acad. Sci., 105(13), pp. 4972-4975 (2008).
80. Ahmadlou, M., Adeli, H., and Adeli, A. "New diagnostic EEG markers of the Alzheimer's disease using visibility graph", J. Neural. Transm., 117(9), pp. 1099-1109 (2010).
81. Gao, Z.K., Cai, Q., Yang, Y.X., et al. "Visibility graph from adaptive optimal kernel time-frequency representation for classification of epileptiform EEG", Int. J. Neural Syst., 27(4), 1750005 (2017).
82. Mozaffarilegha, M. and Adeli, H. "Visibility graph analysis of speech evoked auditory brainstem response in persistent developmental stuttering", Neurosci. Lett., 696, pp. 28-32 (2019).
83. Ahmadlou, M., Adeli, H., and Adeli, A. "Improved visibility graph fractality with application for the diagnosis of autism spectrum disorder", Physica A, 391(20), pp. 4720-4726 (2012).
84. Ahmadlou, M. and Adeli, H. "Visibility graph similarity: a new measure of generalized synchronization in coupled dynamic systems", Physica D, 241(4), pp. 326-332 (2012).
85. Rostaghi, M. and Azami, H. "Dispersion entropy: A measure for time-series analysis", IEEE Signal Process. Lett., 23(5), pp. 610-614 (2016).
86. Rostaghi, M., Ashory, M.R., and Azami, H. "Application of dispersion entropy to status characterization of rotary machines", J. Sound Vib., 438, pp. 291-308 (2019).
87. Azami, H., Rostaghi, M., Abasolo, D., et al. "Refined composite multiscale dispersion entropy and its application to biomedical signals", IEEE Trans. Biomed. Eng., 64(12), pp. 2872-2879 (2017).
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