[1] H. B. Bisheh, G. G. Amiri, M. Nekooei, and E. Darvishan, “Damage Detection of a Cable-stayed Bridge Based on Combining Effective Intrinsic Mode Functions of Empirical Mode Decomposition using the Feature Selection Technique”,
Inverse Problems in Science and Engineering, Vol. 29, No. 6, pp. 861–881, 2021,
doi:10.1080/17415977.2020.1814280.
[2] M. Chen, W. Zhai, S. Zhu, L. Xu, and Y. Sun, “Vibration-based Damage Detection of Rail Fastener using Fully Convolutional Networks”, Vehicle System Dynamics, Vol. 60, pp. 1–20, 2021, doi:10.1080/00423114.2021.1896010.
[3] S. M. C. R. Randiligama, D. P. Thambiratnam, T. H. D. Chan, S. Fawzia, and K.-D. Nguyen, “Vibration-based Damage Detection in Hyperbolic Cooling Towers using Coupled Method”, Engineering Failure Analysis, Vol. 121, pp. 105156, 2020, doi:10.1016/j.engfailanal.2020.105156.
[4] S. Kumbhar, and E. Sudhagar, “An Integrated Approach of Adaptive Neuro-fuzzy Inference System and Dimension Theory for Diagnosis of Rolling Element Bearing”, Measurement, Vol. 166, pp. 108266, 2020, doi:10.1016/j.measurement.2020.108266.
[5] S. Kapuria, and A. Ahmed, “A Coupled Efficient Layerwise Finite Element Model for Free Vibration Analysis of Smart Piezo-bonded Laminated Shells Featuring Delaminations and Transducer Debonding”, International Journal of Mechanical Sciences, Vol. 194, pp. 106195, 2021, doi:10.1016/j.ijmecsci.2020.106195.
[6] J. C. Pineda Allen, and C. T. Ng, “Damage Detection in Composite Laminates using Nonlinear Guided Wave Mixing,”
Composite Structures, Vol. 311, pp. 116805, 2023, doi:10.1016/j.compstruct.2023.116805,
https://www.sciencedirect.com/science/article/pii/S0263822323001496.
[7] K. H. Padil, N. Bakhary, M. O. Abdulkareem, J. Li, and H. Hao, “Non-probabilistic Method to Consider Uncertainties in Frequency Response Function for Vibration-based Damage Detection using Artificial Neural Network”, Journal of Sound and Vibration, Vol. 467, pp. 115069, 2019, doi:10.1016/j.jsv.2019.115069.
[8] R. Hou, X. Wang, Q. Xia, and Y. Xia, “Sparse Bayesian Learning for Structural Damage Detection under Varying Temperature Conditions”, Mechanical Systems and Signal Processing, Vol. 145, pp. 106965, 2020, doi:10.1016/j.ymssp.2020.106965.
[9] H. Jiezhong, D.-S. Li, C. Zhang, and H.-N. Li, “Improved Kalman Filter Damage Detection Approach Based on lp Regularization”, Structural Control and Health Monitoring, Vol. 26, No. 10, pp.e2424, 2019, https://doi.org/10.1002/stc.2424.
[10] L. Huang, J. Ding, J. Lin, and Z. Luo, “Detection and Localization of Corrosion using Identical-group-velocity Lamb Wave Modes,”
Nondestructive Testing and Evaluation, Vol. 39, No. 3, pp. 594-613, 2023,
https://doi.org/10.1080/10589759.2023.2218007.
[11] M. Ramezani, and O. Bahar, “Structural Damage Identification for Elements and Connections using an Improved Genetic Algorithm”, Smart Structures and Systems, Vol. 28, pp. 643–660, 2021, doi:10.12989/sss.2021.28.5.643.
[12] Z. Niu, “Two-step Structural Damage Detection Method for Shear Frame Structures using FRF and Neumann Series Expansion”, Mechanical Systems and Signal Processing, Vol. 149, pp. 107185, 2021, doi:10.1016/j.ymssp.2020.107185.
[13] M. R. Azim, H. Zhang, and M. Gul, “Damage Detection of Railway Bridges using Operational Vibration Data: Theory and Experimental Verifications,” Structural Monitoring and Maintenance, Vol. 7, pp. 149–166, 2020, doi:10.12989/smm.2020.7.2.149.
[14] O. Alazzawi, and D. Wang, “Damage Identification using the PZT Impedance Signals and Residual Learning Algorithm”, Journal of Civil Structural Health Monitoring, Vol. 11 pp. 1–14, 2021, doi:10.1007/s13349-021-00505-9.
[15] C. C. Ngetich, J. K. Kimotho, and J. M. Kihiu, “Damage Detection of Structural Elements Based on Active Sensing and Machine Learning Approaches”,
Journal of Sustainable Research in Engineering, Vol. 5, No. 2, pp. 62–77, 2020,
https://jsre.jkuat.ac.ke/index.php/jsre/article/view/87.
[16] S. Barman, D. Maiti, and D. Maity, “Vibration-based Delamination Detection in Composite Structures Employing Mixed Unified Particle Swarm Optimization”, AIAA (American Institute of Aeronautics and Astronautics) Journal, Vol. 59, No. 1, 2021, https://doi.org/10.2514/1.J059176.
[17] D. Chowdary, and M. Alapati, “Effect of External Vibrations on Electro-mechanical Impedance Signatures in Damage Detection”, Materialstoday: Proceedings, Vol. 1, No. 45, pp. 3398-403, 2021, https://doi.org/10.1016/j.matpr.2020.12.794.
[18] X. Tong, S. Song, L. Wang, and H. Yang, “A Preliminary Research on Wireless Cantilever Beam Vibration Sensor in Bridge Health Monitoring”, Frontiers of Structural and Civil Engineering, Vol. 12, pp. 207-214, 2018, https://doi.org/10.1007/s11709-017-0406-x.
[19] H. B. Bisheh, and G. G. Amiri, “Structural Damage Detection Based on Variational Mode Decomposition and Kernel PCA-based Support Vector Machine”, Engineering Structures, Vol. 278, pp. 115565, 2023, doi:10.1016/j.engstruct.2022.115565.
[20] D. Yang, X. Zhang, T. Zhou, T. Wang, and J. Li, “A Novel Pipeline Corrosion Monitoring Method Based on Piezoelectric Active Sensing and CNN”,
Sensors, Vol. 23, No. 2, pp. 855, 2023,
https://www.mdpi.com/1424-8220/23/2/855.
[21] Z. Yuan, S. Zhu, X. Yuan, and W. Zhai, “Vibration-based Damage Detection of Rail Fastener Clip using Convolutional Neural Network: Experiment and Simulation”,
Engineering Failure Analysis, Vol. 119, 2021, pp. 104906,
https://doi.org/10.1016/j.engfailanal.2020.104906.
[22] A. Agrawal, and G. Chakraborty, “Structural Damage Detection by Integrating Short Time Fourier Transform”, Safety and Reliability - Safe Societies in a Changing World – Haugen et al. (Eds), Taylor & Francis Group, London, pp. 971–976, 2018, ISBN 978-0-8153-8682-7, https://doi.org/10.1201/9781351174664.
[23] S. Amirkhani, A. Chaibakhsh, and A. Ghaffari, “Model-based Approach for Multi-sensor Fault Identification in Power Plant Gas Turbines”, Iranian Journal of Science and Technology: Transactions of Mechanical Engineering, Vol. 21, No. 1, pp. 65–86, 2020, https://dor.isc.ac/dor/20.1001.1.16059727.2020.21.1.4.7.
[24] M. A. Bayati Nezhad, A. Mohammadi, and A. Davood Abadi, “Prediction of the Remaining Useful Life of the Rolling Element Bearings using Recurrent Neural Network”, Iranian Journal of Mechanical Engineering Transactions of the ISME, Vol. 21, No. 2, pp. 6–13, 2020, https://doi.org/10.30506/jmee.2020.46440.
[25] A. Ghiasi, M. K. Moghaddam, C. T. Ng, A. H. Sheikh, and J. Q. Shi, “Damage Classification of In-service Steel Railway Bridges using a Novel Convolutional Neural Network”, Engineering Structures, Vol. 264, pp. 114474, 2021, https://doi.org/10.1016/j.engstruct.2022.114474.
[26] R. M. Delgadillo, and J. R. Casas, “A Combined Kernel-PCA with Clustering Analysis for Bridge Damage Detection under Changing Environmental Conditions”,
In Life-cycle Civil Engineering: Innovation, Theory and Practice, 1st Edition, CRC Press, pp. 1362-1370, 2021, London, UK,
https://www.taylorfrancis.com/chapters/edit/10.1201/9780429343292-181/combined-kernel-pca-clustering-analysis-bridge-damage-detection-changing-environmental-conditions-delgadillo-casas.
[27] S. W. F. de Rezende, J. R. V. de Moura, R. M. F. Neto, C. A. Gallo, and V. Steffen, “Convolutional Neural Network and Impedance-based SHM Applied to Damage Detection”,
Engineering Research Express, Vol. 2, No. 3, pp. 035031, 2020,
https://dx.doi.org/10.1088/2631-8695/abb568.
[28] F. Lambinet, and Z. S. Khodaei, “Damage Detection & Localization on Composite Patch Repair under Different Environmental Effects”, Engineering Research Express, Vol. 2, No. 4, pp. 045032, 2020, doi: 10.1088/2631-8695/abd0d3.
[29] N. Kumar, S. Kumar, and M. R. Sunny, “A Modified Hyperbola Based Baseline Free Approach using Lamb Wave Mode Conversion for Detection of Multiple Structural Damages: A Simulation Based Study”,
Engineering Research Express, Vol. 5, No. 2, pp. 025029, 2023,
https://dx.doi.org/10.1088/2631-8695/acc516.
[30] I. Calio, A. Greco, and D. D’Urso, “Structural Models for the Evaluation of Eigen-properties in Damaged Spatial Arches: A Critical Appraisal”, Archive of Applied Mechanics, Vol. 86, pp. 1853-1867, 2016, https://doi.org/10.1007/s00419-016-1151-7.
[31] D.-G. Kim, and S.-B. Lee, “Structural Damage Identification of a Cantilever Beam using Excitation Force Level Control”,
Mechanical Systems and Signal Processing, Vol. 24, No. 6, pp. 1814–1830, 2010, doi:10.1016/j.ymssp.2010.02.007,
https://www.sciencedirect.com/science/article/pii/S0888327010000543.
[32] W. Mutlag, S. Ali, Z. Mosad, and B. H. Ghrabat, “Feature Extraction Methods: A Review”,
Journal of Physics: Conference Series,
The Fifth International Scientific Conference of Al-Khwarizmi Society (FISCAS), June 2020, Iraq, Vol. 1591, No. 1, pp. 012028, 2020, IOP Publishing, doi:10.1088/1742-6596/1591/1/012028.
[33] X. Wang, T. Ying, and W. Tian, “Spectrum Representation Based on STFT”, in 13th International Congress on Image and Signal Processing, Bio-Medical Engineering and Informatics (CISP-BMEI), Vol. 2, pp. 435–438, 17-19 Oct, 2020, Chengdu, China, doi:10.1109/CISP-BMEI51763.2020.926 was3516.
[34] P. Ray, S. S. Reddy, and T. Banerjee, “Various Dimension Reduction Techniques for High Dimensional Data Analysis: A Review”, Artificial Intelligence Review, Vol. 54, No. 5, pp. 3473–3515, 2021, https://doi.org/10.1007/s10462-020-09928-0.
[35] B. Ghojogh, M. Samad, S. Mashhadi, T. Kapoor, W. Ali, F. Karray, and M. Crowley, “Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review”, Vol. 5, 2019, https://api.semanticscholar.org/CorpusID:147704146.
[36] H. Abdi, and L. J. Williams, “Principal Component Analysis”,
WIREs Computational Statistics, Vol. 2, No. 4, pp. 433–459, 2010,
https://wires.onlinelibrary.wiley.com/doi/abs/10.1002/wics.101.
[37] L. Van der Maaten, and G. Hinton, “Visualizing Data using T-SNE”, Journal of Machine Learning Research, Vol. 9, pp. 2579–2605, 2008, https://api.semanticscholar.org/CorpusID:5855042.
[38] R. A. Johnson, and D. W. Wichern, “
Applied Multivariate Statistical Analysis”, 6th Edition, Prentice Hall, Saddle River, New Jersey, US, 2002,
Dr_Alodat_STAT_459_L01_Spring_2012-libre.pdf.
[39] R. A. Reyment, and K. J. Vreskog, “Applied Factor Analysis in the Natural Sciences”, First Paperback Edition, Cambridge University Press, UK, 1996, ISBN: 9780521575560, 0521575567.
[40] L. Van der Maaten, E. O. Postma, and H. Herik, “Dimensionality Reduction: A Comparative Review”,
Journal of Machine Learning Research, Vol. 10, No. 66-71, pp. 13, 2009,
Dimensionality-Reduction-A-Comparative-Review.pdf.
[41] J. Teenbaum, D. Silva, and J. Langford, “A Global Geometric Framework for Nonlinear Dimensionality Reduction”, Science, Vol. 290, No. 5500, pp. 2319–2323, 2000, https://doi.org/10.1126/science.290.5500.2319.
[42] J.A. Richards, “Supervised Classification Techniques. In: Remote Sensing Digital Image Analysis”, Springer, Berlin, Heidelberg, Germany, pp. 247–318, 2013, doi: 10.1007/978-3-642-30062-2_8.
[43] Y. Jiang, J. Hamer, C. Wang, X. Jiang, M. Kim, Y. Xia, N. Mohammed,
M.d N. Sadat, and S. Wang, "SecureLR: Secure Logistic Regression Model via a Hybrid Cryptographic Protocol”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 16, No. 1, pp. 113-123, 1 Jan.-Feb. 2019, https://doi.org/10.1109/TCBB.2018.2833463.
[44] M. J. Grimble, and
L. Marconi, “
Advanced Textbooks in Control and Signal Processing (C & SP)”, Springer, London, UK, 2005.
[45] B. E. Boser, I. M. Guyon, and V. N. Vapnik, “
A Training Algorithm for Optimal Margin Classifiers”, COLT '92: Proceedings of the Fifth Annual Workshop on Computational Learning Theory”, Association for Computing Machinery (ACM), pp. 144–152, New York, NY, USA, 1992,
https://doi.org/10.1145/130385.130401.
[46] Y. Elyassami, K. Benjelloun, and M. El Aroussi, “Bearing Fault Diagnosis and Classification Based on KDA and Alpha-stable Fusion”, Contemporary Engineering Sciences, Vol. 9, No. 10, pp. 453 – 465, 2016, http://dx.doi.org/10.12988/ces.2016.512328.
[47] B. R. Nayana, and P. Geethanjali, “Analysis of Statistical Time-domain Features Effectiveness in Identification of Bearing Faults from Vibration Signal”, IEEE Sensors Journal, Vol. 17, No. 17, pp. 5618-5625, 1 Sep., 2017, https://doi.org/10.1109/JSEN.2017.2727638.
[48] W. J. Wilbur, and W. Kim, “Stochastic Gradient Descent and the Prediction of Mesh for PubMed Records”,
AMIA Annual Symposium Proceedings, AMIA Symposium, Nov. 14, Vol. 2014, pp. 1198-1207, 2014,
https://pubmed.ncbi.nlm.nih.gov/25954431/.
[49] E. Lau, L. Sun, and Q. Yang, “Modelling, Prediction and Classification of Student Academic Performance using Artificial Neural Networks”, SN Applied Sciences, Vol. 1, No. 9, pp. 982, 2019, doi:10.1007/s42452-019-0884-7.
[50] S. Diab, “Optimizing Stochastic Gradient Descent in Text Classification Based on Fine-tuning Hyper-parameters Approach. A Case Study on Automatic Classification of Global Terrorist Attacks”,
International Journal of Computer Science and Information Security (IJCSIS), Vol. 16, No. 12, pp. 155-160, 2019,
https://doi.org/10.48550/arXiv.1902.06542.
[51] Z. Allen-Zhu, “Katyusha: The First Direct Acceleration of Stochastic Gradient Methods”,
Journal of Machine Learning Research, Vol. 18, No. 221, pp. 1-51, 2018,
https://doi.org/10.48550/arXiv.1603.05953.
[52] H. Mahmoudi, M. Bitaraf, M. Salkhordeh, and S. Soroushian, “A Rapid Machine Learning-based Damage Detection Algorithm for Identifying the Extent of Damage in Concrete Shear-wall Buildings”,
Structures, Vol. 47, pp. 482–499, 2023,
https://doi.org/10.1016/j.istruc.2022.11.041,
https://www.sciencedirect.com/science/article/pii/S2352012422010803.
[53] N. Zhang, L. Wu, J. Yang, and Y. Guan, “Naive Bayes Bearing Fault Diagnosis Based on Enhanced Independence of Data”, Sensors,
Vol. 18 No. 2 pp. 463 2018,
https://doi.org/10.3390/s18020463,
https://www.mdpi.com/1424-8220/18/2/463.
[54] J. Sultana, M. U. Rani, and M. A. H. Farquadand “Student’s Performance Prediction using Deep Learning and Data Mining Methods”, International Journal of Recent Technology and Engineering (IJRTE), Vol. 8, No. 1S4, June 2019, ISSN: 2277-3878.
[55] V. F. Rodriguez-Galiano, B. Ghimire, J. Rogan, M. Chica-Olmo, and J. Rigol-Sanchez, “An Assessment of the Effectiveness of a Random Forest Classifier for Land-cover Classification”, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 67, pp. 93–104, 2012, doi:10.1016/j.isprsjprs.2011.11.002.
[56] M. Pandey, and V. Sharma, “A Decision Tree Algorithm Pertaining to the Student Performance Analysis and Prediction”, International Journal of Computer Applications, Vol. 61, pp. 1–5, 2013, doi:10.5120/9985-4822.
[57] A. S. Olaniyi, S. Y. Kayode, H. M. Abiola, S.-I. T. Tosin, and A. N. Babatunde, “Students Performance Analysis using Decision Tree Algorithms”,
Annals Computer Science Series, Vol. 15, No. 1, pp. 55–62, 2017,
https://anale-informatica.tibiscus.ro/download/lucrari/15-1-07-Olaniyi.pdf.
[58] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based Learning Applied to Document Recognition”, Proceedings of the IEEE, Vol. 86 No. 11, pp. 2278–2324, 1998, https://doi.org/10.1109/5.726791.
[59] I. H. Shames, “Energy and Finite Element Methods in Structural Mechanics: SI Units”, Routledge, New York, USA, 2017, https://doi.org/10.1201/9780203757567.