Structural Damage Diagnosis and Prediction Using Machine Learning and Deep Learning Models: Comprehensive Review of Advances
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[62] Sandra E Nope-Rodríguez,et al. Detection of Internal Defects in Carbon Fiber Reinforced Plastic Slabs Using Background Thermal Compensation by Filtering and Support Vector Machines , 2019, Journal of Nondestructive Evaluation.
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[64] Luis Salinas,et al. A frame-based ANN for classification of hyperspectral images: assessment of mechanical damage in mushrooms , 2017, Neural Computing and Applications.
[65] Mohammad Pourgol-Mohammad,et al. Stochastic fatigue crack growth analysis of metallic structures under multiple thermal–mechanical stress levels , 2016 .
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[68] Geok Soon Hong,et al. Defect detection in selective laser melting technology by acoustic signals with deep belief networks , 2018 .
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[73] Joel P. Conte,et al. Nonlinear finite element model updating for damage identification of civil structures using batch Bayesian estimation , 2017 .
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[77] Sy Dzung Nguyen,et al. Algorithm for Estimating Online Bearing Fault Upon the Ability to Extract Meaningful Information From Big Data of Intelligent Structures , 2019, IEEE Transactions on Industrial Electronics.
[78] Guangtao Zhai,et al. Application of Deep Learning Architectures for Accurate and Rapid Detection of Internal Mechanical Damage of Blueberry Using Hyperspectral Transmittance Data , 2018, Sensors.
[79] Pong-Jeu Lu,et al. Handmade Trileaflet Valve Design and Validation for Pulmonary Valved Conduit Reconstruction Using Taguchi Method and Cascade Correlation Machine Learning Model , 2018, IEEE Access.
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[81] Miguel Garrido Izard,et al. Evaluation of Over-The-Row Harvester Damage in a Super-High-Density Olive Orchard Using On-Board Sensing Techniques , 2018, Sensors.