Version 1
: Received: 10 December 2019 / Approved: 11 December 2019 / Online: 11 December 2019 (04:50:48 CET)
How to cite:
Mosavi, A. Structural Damage Diagnosis and Prediction Using Machine Learning and Deep Learning Models: Comprehensive Review of Advances. Preprints2019, 2019120149. https://doi.org/10.20944/preprints201912.0149.v1
Mosavi, A. Structural Damage Diagnosis and Prediction Using Machine Learning and Deep Learning Models: Comprehensive Review of Advances . Preprints 2019, 2019120149. https://doi.org/10.20944/preprints201912.0149.v1
Mosavi, A. Structural Damage Diagnosis and Prediction Using Machine Learning and Deep Learning Models: Comprehensive Review of Advances. Preprints2019, 2019120149. https://doi.org/10.20944/preprints201912.0149.v1
APA Style
Mosavi, A. (2019). Structural Damage Diagnosis and Prediction Using Machine Learning and Deep Learning Models: Comprehensive Review of Advances<strong> </strong>. Preprints. https://doi.org/10.20944/preprints201912.0149.v1
Chicago/Turabian Style
Mosavi, A. 2019 "Structural Damage Diagnosis and Prediction Using Machine Learning and Deep Learning Models: Comprehensive Review of Advances<strong> </strong>" Preprints. https://doi.org/10.20944/preprints201912.0149.v1
Abstract
The loss of integrity and adverse effect on mechanical properties can be concluded as attributing miro/macro-mechanics damage in structures, especially in composite structures. Damage as a progressive degradation of material continuity in engineering predictions for any aspects of initiation and propagation requires to be identified by a trustworthy mechanism to guarantee the safety of structures. Besides the materials design, structural integrity and health are usually prone to be monitored clearly. One of the most powerful methods for the detection of damage is machine learning (ML). This paper presents the state of the art of ML methods and their applications in structural damage and prediction. Popular ML methods are identified and the performance and future trends are discussed.
Keywords
damage detection; machine learning; principal component analysis; composites; micromechanics of damage; continuum damage mechanics
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
