Computational deep learning algorithm (vision/frequency response)-based damage detection in engineering structure

DOI10.1007/S00707-023-03709-WzbMath1526.74074MaRDI QIDQ6058597

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Publication date: 1 November 2023

Published in: Acta Mechanica (Search for Journal in Brave)

zbMATH Keywords

finite element methodregression modelconvolution neural networkdamage location identification

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05) Brittle fracture (74R10) Vibrations in dynamical problems in solid mechanics (74H45) Finite element methods applied to problems in solid mechanics (74S05) Thin bodies, structures (74K99) Brittle damage (74R05) Numerical and other methods in solid mechanics (74S99)

Cites Work

• A four-node discrete singular convolution for geometric transformation and its application to numerical solution of vibration problem of arbitrary straight-sided quadrilateral plates
• Evaluating pavement cracks with bidimensional empirical mode decomposition
• Machine learning regression approaches for predicting the ultimate buckling load of variable-stiffness composite cylinders
• Advances in Neural Networks – ISNN 2005
• Free vibration and nonlinear dynamic behaviors of the imperfect smart electric magnetic FG-laminated composite panel in a hygrothermal environments
• Frequency analysis of smart sandwich cylindrical panels with nanocomposite core and piezoelectric face sheets
• On the vibration and buckling behaviors of porous FG beams resting on variable elastic foundation utilizing higher-order shear deformation theory

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