M. Radzieński, Ł. Doliński, M. Krawczuk, M. Palacz, Damage localisation in a stiffened plate structure using a propagating wave, Mechanical Systems and Signal Processing, 39(1-2) (2013) 388-395.
 N.T.S. Board, U.S.N.T.S. Board, Collapse of I-35W Highway Bridge, Minneapolis, Minnesota, August 1, 2007, Createspace Independent Pub, 2008.
 M. Kunishima, Collapse of the Korea seoul seongsu bridge JST Failure Knowledge Database/100 Selected Cases, (1994).
 T.D. Stark, R. Benekohal, L.A. Fahnestock, J.M. LaFave, J. He, C. Wittenkeller, I-5 Skagit River bridge collapse review, Journal of performance of constructed facilities, 30(6) (2016) 04016061.
 B. Dawson, Vibration condition monitoring techniques for rotating machinery, The shock and vibration digest, 8(12) (1976) 3.
 M.J. Whelan, M.V. Gangone, K.D. Janoyan, R. Jha, Real-time wireless vibration monitoring for operational modal analysis of an integral abutment highway bridge, Engineering Structures, 31(10) (2009) 2224-2235.
 D. Goyal, B. Pabla, The vibration monitoring methods and signal processing techniques for structural health monitoring: a review, Archives of Computational Methods in Engineering, 23(4) (2016) 585-594.
 J.P. Amezquita-Sanchez, H. Adeli, Signal processing techniques for vibration-based health monitoring of smart structures, Archives of Computational Methods in Engineering, 23(1) (2016) 1-15.
 F. Kopsaftopoulos, S. Fassois, Vibration based health monitoring for a lightweight truss structure: experimental assessment of several statistical time series methods, Mechanical Systems and Signal Processing, 24(7) (2010) 1977-1997.
 R.V. Farahani, D. Penumadu, Full‐scale bridge damage identification using time series analysis of a dense array of geophones excited by drop weight, Structural Control and Health Monitoring, 23(7) (2016) 982-997.
 X. Zhu, M. Cao, W. Ostachowicz, W. Xu, Damage Identification in Bridges by Processing Dynamic Responses to Moving Loads: Features and Evaluation, Sensors, 19(3) (2019) 463.
 L. Qiao, A. Esmaeily, H.G. Melhem, Signal pattern recognition for damage diagnosis in structures, Computer‐Aided Civil and Infrastructure Engineering, 27(9) (2012) 699-710.
 P. Wang, Q. Shi, Damage Identification in Structures Based on Energy Curvature Difference of Wavelet Packet Transform, Shock and Vibration, 2018 (2018).
 Z.-D. Xu, Z. Wu, Energy damage detection strategy based on acceleration responses for long-span bridge structures, Engineering Structures, 29(4) (2007) 609-617.
 Y. Xin, H. Hao, J. Li, Operational modal identification of structures based on improved empirical wavelet transform, Structural Control and Health Monitoring, 26(3) (2019) e2323.
 J.P. Amezquita-Sanchez, H.S. Park, H. Adeli, A novel methodology for modal parameters identification of large smart structures using MUSIC, empirical wavelet transform, and Hilbert transform, Engineering Structures, 147 (2017) 148-159.
 N. Liu, J. Xi, X. Zhang, Z. Liu, Damage detection of simply supported reinforced concrete beam by S transform, in: IOP Conference Series: Earth and Environmental Science, IOP Publishing, 2017, pp. 012133.
 J.-L. Liu, Z.-C. Wang, W.-X. Ren, X.-X. Li, Structural time-varying damage detection using synchrosqueezing wavelet transform, Smart Structures and Systems, 15(1) (2015) 119-133.
 E. Darvishan, Low cost damage detection of cable-stayed bridges using signal processing and machine learning, Amirkabir Journal of Civil Engineering, (2018), (in Persian).
 A. Neves, I. González, J. Leander, R. Karoumi, Structural health monitoring of bridges: a model-free ANN-based approach to damage detection, Journal of Civil Structural Health Monitoring, 7(5) (2017) 689-702.
 M. Chandrashekhar, R. Ganguli, Structural damage detection using modal curvature and fuzzy logic, Structural Health Monitoring, 8(4) (2009) 267-282.
 R. Ghiasi, M. Ghasemi, M. Sohrabi, Structural damage detection using frequency response function index and surrogate model based on optimized extreme learning machine algorithm, Computational Methods in Engineering, 36(1) (2017) 1-17, (in Persian).
 P. Ghaderi and Y. Shabani, Damage Detection based on Modal Parameters and Dynamic Responses by using Enhanced Grey Wolf Optimization, Amirkabir Journal of Civil Engineering, (2019), (in Persian).
 H. Babajanian Bisheh, G. Ghodrati Amiri, M. Nekooei, E. Darvishan, Damage detection of a cable-stayed bridge using feature extraction and selection methods, Structure and Infrastructure Engineering, 15(9) (2019) 1165-1177.
 M. Gul, F.N. Catbas, Structural health monitoring and damage assessment using a novel time series analysis methodology with sensor clustering, Journal of Sound and Vibration, 330(6) (2011) 1196-1210.
 S. Li, H. Li, Y. Liu, C. Lan, W. Zhou, J. Ou, SMC structural health monitoring benchmark problem using monitored data from an actual cable‐stayed bridge, Structural Control and Health Monitoring, 21(2) (2014) 156-172.
 M.R. Kaloop, J.W. Hu, Stayed-cable bridge damage detection and localization based on accelerometer health monitoring measurements, Shock and Vibration, 2015 (2015).
 ANCRiSST SHM benchmark problem, Harbin: Center of Structural Monitoring and Control of the Harbin Institute of Technology, http://smc.hit.edu.cn.
 Y.z. Lin, Z.h. Nie, H.w. Ma, Structural damage detection with automatic feature‐extraction through deep learning, Computer‐Aided Civil and Infrastructure Engineering, 32(12) (2017) 1025-1046.
 A. Lerch, An introduction to audio content analysis: Applications in signal processing and music informatics, Wiley-IEEE Press, 2012.
 J. Gilles, Empirical wavelet transform, IEEE transactions on signal processing, 61(16) (2013) 3999-4010.
 S. Nezamivand Chegini, F. Zarif, A. Bagheri, M. AliTavoli, Noise Removal from the Vibration Signals of the Rotating Machinery Using the Empirical Wavelet Transform and the Conventional Thresholding Methods, Journal of Solid and Fluid Mechanics, 9(1) (2019) 111-124 (in Persian).
 J. Toivola, J. Hollmén, Feature extraction and selection from vibration measurements for structural health monitoring, in: International Symposium on Intelligent Data Analysis, Springer, 2009, pp. 213-224.
 Guyon, A. Elisseeff, An introduction to variable and feature selection, Journal of machine learning research, 3(Mar) (2003) 1157-1182.
 E. Alpaydin, Introduction to machine learning, MIT press, 2009.
 H. Vafaie, K. De Jong, Genetic algorithms as a tool for feature selection in machine learning, in: Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI'92, IEEE, 1992, pp. 200-203.
 N.-T. Nguyen, H.-H. Lee, J.-M. Kwon, Optimal feature selection using genetic algorithm for mechanical fault detection of induction motor, Journal of Mechanical Science and Technology, 22(3) (2008) 490-496.
 L. Breiman, Random forests, Machine learning, 45(1) (2001) 5-32.
 H. Kawakubo, H. Yoshida, Rapid feature selection based on random forests for high-dimensional data, in: Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA), The Steering Committee of The World Congress in Computer Science, Computer …, 2012, pp. 1.
 B.E. Boser, I.M. Guyon, V.N. Vapnik, A training algorithm for optimal margin classifiers, in: Proceedings of the fifth annual workshop on Computational learning theory, ACM, 1992, pp. 144-152.
 S. Yin, X. Gao, H.R. Karimi, X. Zhu, Study on support vector machine-based fault detection in tennessee eastman process, in: Abstract and Applied Analysis, Hindawi, 2014.
 H. Wang, P. Chen, A feature extraction method based on information theory for fault diagnosis of reciprocating machinery, Sensors, 9(4) (2009) 2415-2436.