Structural Damage Diagnosis and prediction using Machine Learning and Deep Learning Models: Comprehensive Review of Advances

EasyChair Preprint 2251

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. Beside 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.

Keyphrases: Composites, Continuum Damage Mechanics, Principal Component Analysis, damage detection, deep learning, machine learning, micromechanics of damage

@booklet{EasyChair:2251,
  author    = {Amir Mosavi},
  title     = {Structural Damage Diagnosis and prediction using Machine Learning and Deep Learning Models: Comprehensive Review of Advances},
  howpublished = {EasyChair Preprint 2251},
  year      = {EasyChair, 2019}}