Learning Damage Representations with Sequence-to-Sequence Models.

ABSTRACT: Natural hazards have caused damages to structures and economic losses worldwide. Post-hazard responses require accurate and fast damage detection and assessment. In many studies, the development of data-driven damage detection within the research community of structural health monitoring has emerged due to the advances in deep learning models. Most data-driven models for damage detection focus on classifying different damage states and hence damage states cannot be effectively quantified. To address such a deficiency in data-driven damage detection, we propose a sequence-to-sequence (Seq2Seq) model to quantify a probability of damage. The model was trained to learn damage representations with only undamaged signals and then quantify the probability of damage by feeding damaged signals into

SUBMITTER: Yang Q 

PROVIDER: S-EPMC8781882 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature