[1] An, Y., Chatzi, E., Sim, S.H., Laflamme, S., Blachowski, B., Ou, J., Recent progress and future trends on damage identification methods for bridge structures, Structural Control and Health Monitoring, 26(10), 2019, e2416.
[2] Sun, L., Shang, Z., Xia, Y., Bhowmick, S., Nagarajaiah, S., Review of Bridge Structural Health Monitoring Aided by Big Data and Artificial Intelligence: From Condition Assessment to Damage Detection, Journal of Structural Engineering, 146(5), 2020, 04020073.
[3] Diao, Y., Lv, J., Wang, Q., Li, X., Xu, J., Structural damage identification based on variational mode decomposition–Hilbert transform and CNN, Journal of Civil Structural Health Monitoring, 13, 2023, 1415-1429.
[4] Kumar, K., Biswas, P.K., Dhang, N., Time series-based SHM using PCA with application to ASCE benchmark structure, Journal of Civil Structural Health Monitoring, 10(5), 2020, 899-911.
[5] Azim, M.R., Gül, M., Data-driven damage identification technique for steel truss railroad bridges utilizing principal component analysis of strain response, Structure and Infrastructure Engineering, 17(8), 2021, 1019-1035.
[6] Jin, S.-S., Jung, H.-J., Vibration-based damage detection using online learning algorithm for output-only structural health monitoring, Structural Health Monitoring, 17(4), 2018, 727-746.
[7] Sen, D., Erazo, K., Zhang, W., Nagarajaiah, S., Sun, L., On the effectiveness of principal component analysis for decoupling structural damage and environmental effects in bridge structures, Journal of Sound and Vibration, 457, 2019, 280-298.
[8] Tibaduiza, D.A., Mujica, L.E., Rodellar, J., Güemes, A., Structural damage detection using principal component analysis and damage indices, Journal of Intelligent Material Systems and Structures, 27(2), 2016, 233-248.
[9] Zhu, Y., Ni, Y.-Q., Jin, H., Inaudi, D., Laory, I., A temperature-driven MPCA method for structural anomaly detection, Engineering Structures, 190, 2019, 447-458.
[10] Posenato, D., Kripakaran, P., Inaudi, D., Smith, I.F.C., Methodologies for model-free data interpretation of civil engineering structures, Computers and Structures, 88(7-8), 2010, 467-482.
[11] Laory, I., Trinh, T.N., Posenato, D., Smith, I.F.C., Combined model-free data-interpretation methodologies for damage detection during continuous monitoring of structures, Journal of Computing in Civil Engineering, 27(6), 2013, 657-666.
[12] Laory, I., Trinh, T.N., Smith, I.F.C., Evaluating two model-free data interpretation methods for measurements that are influenced by temperature, Advanced Engineering Informatics, 25(3), 2011, 495-506.
[13] Nie, Z., Guo, E., Li, J., Hao, H., Ma, H., Jiang, H., Bridge condition monitoring using fixed moving principal component analysis, Structural Control and Health Monitoring, 27(6), 2020, e2535.
[14] Malekzadeh, M., Gul, M., Kwon, I.B., Catbas, N., An integrated approach for structural health monitoring using an in-house built fiber optic system and non-parametric data analysis, Smart Structures and Systems, 14(5), 2014, 917-942.
[15] Zhang, G., Tang, L., Liu, Z., Zhou, L., Sun, S., Enhanced features in principal component analysis with spatial and temporal windows for damage identification, Inverse Problems in Science and Engineering, 29(13), 2021, 2877-2894.
[16] Zhang, G., Tang, L., Zhou, L., Liu, Z., Liu, Y., Jiang, Z., Principal Component Analysis Method with Space and Time Windows for Damage Detection, Sensors, 19(11), 2019, 2521.
[17] Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M., Gabbouj, M., Inman, D.J., A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications, Mechanical Systems and Signal Processing, 147, 2021, 107077.
[18] Hou, R.X., Yong, Review on the new development of vibration-based damage identification for civil engineering structures: 2010-2019, Journal of Sound and Vibration, 491(1), 2021, 115741.
[19] Tian, Y., Xu, Y., Zhang, D., Li, H., Relationship modeling between vehicle-induced girder vertical deflection and cable tension by BiLSTM using field monitoring data of a cable-stayed bridge, Structural Control and Health Monitoring, 28(2), 2021, e2667.
[20] Ruiz, D.V., Bragança, C.S.C.D., Poncetti, B.L., Bittencourt, T.N., Futai, M.M., Vibration-based structural damage detection strategy using FRFs and machine learning classifiers, Structures, 59, 2024, 105753.
[21] Le-Xuan, T., Bui-Tien, T., Tran-Ngoc, H., A novel approach model design for signal data using 1DCNN combing with LSTM and ResNet for damaged detection problem, Structures, 59, 2024, 105784.
[22] Zhou, W., Xu, Y.F., Damage identification for plate structures using physics-informed neural networks, Mechanical Systems and Signal Processing, 209, 2024, 111111.
[23] Chen, G., Yan, Z., Teng, S., Cui, F., Bassir, D., A Bridge Vibration Measurement Method by UAVs based on CNNs and Bayesian Optimization, Journal of Applied and Computational Mechanics, 9(3), 2023, 749-762.
[24] Zhang, G., Liqunliu, Zejiazhou, Lichengliu, Yipingjiang, Zhenyu, Machine-learning-based damage identification methods with features derived from moving principal component analysis, Mechanics of Advanced Materials and Structures, 27(10), 2020, 1789-1802.
[25] Li, H., Spencer, B.F., Mao, J., Wang, H., Spencer, B.F., Toward data anomaly detection for automated structural health monitoring: Exploiting generative adversarial nets and autoencoders, Structural Health Monitoring, 20(4), 2021, 1609-1626.
[26] Lei, X., Xia, Y., Wang, A., Jian, X., Zhong, H., Sun, L., Mutual information based anomaly detection of monitoring data with attention mechanism and residual learning, Mechanical Systems and Signal Processing, 182, 2023, 109607.
[27] Tang, Z., Chen, Z., Bao, Y., Li, H., Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring, Structural Control and Health Monitoring, 26(1), 2019, e2296.
[28] Silva, M., Santos, A., Santos, R., Figueiredo, E., Sales, C., Costa, J.C., Deep principal component analysis: An enhanced approach for structural damage identification, Structural Health Monitoring, 18(5-6), 2019, 1444-1463.
[29] Nguyen, V.T., Tran, T.H., Ha, N.A., Ngo, V.L., Pham, B.T., GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam, Sustainability, 11(24), 2019, 7118.
[30] Hong, H., Liu, J., Zhu, A.X., Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble, Science of The Total Environment, 718, 2020, 137231.
[31] Song, J., Wang, Y., Fang, Z., Peng, L., Hong, H., Potential of Ensemble Learning to Improve Tree-Based Classifiers for Landslide Susceptibility Mapping, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 2020, 4642-4662.
[32] Hoang, N.-D., Tran, V.-D., Tran, X.-L., Image processing-based classification of pavement fatigue severity using extremely randomized trees, deep neural network, and convolutional neural network, International Journal of Pavement Engineering, 24(1), 2023, 2201902.
[33] Leon-Medina, J.X., Anaya, M., Parés, N., Tibaduiza, D.A., Pozo, F., Structural Damage Classification in a Jacket-Type Wind-Turbine Foundation Using Principal Component Analysis and Extreme Gradient Boosting, Sensors, 21(8), 2021, 2748.
[34] Qi, W., Xu, C., Xu, X., AutoGluon: A revolutionary framework for landslide hazard analysis, Natural Hazards Research, 1(3), 2021, 103-108.
[35] Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Smola, A. AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data, 7th ICML Workshop on Automated Machine Learning, 2020.
[36] Song, Y.-Y., Ying, L., Decision tree methods: applications for classification and prediction, Shanghai Archives of Psychiatry, 27(2), 2015, 130.
[37] Zhao, Y. Chapter 4 - Decision Trees and Random Forest. R and Data Mining: Academic Press; 2013. p. 27-40.
[38] Al-Ameri, S., Alawady, A.A., Yousof, M.F.M., Ahmad, H., Salem, A.A., Talib, M.A., Frequency response analysis for transformer tap changer damage detection, International Journal of Power Electronics and Drive Systems, 11(1), 2020, 350-380.