Preprint Review Version 1 This version is not peer-reviewed

Structural Damage Diagnosis and Prediction Using Machine Learning and Deep Learning Models: Comprehensive Review of Advances

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 . Preprints 2019, 2019120149 (doi: 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 (doi: 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.

Subject Areas

damage detection; machine learning; principal component analysis; composites; micromechanics of damage; continuum damage mechanics

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