Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends.

ABSTRACT: Environmental effects may lead to cracking, stiffness loss, brace damage, and other damages in bridges, frame structures, buildings, etc. Structural Health Monitoring (SHM) technology could prevent catastrophic events by detecting damage early. In recent years, Deep Learning (DL) has developed rapidly and has been applied to SHM to detect, localize, and evaluate diverse damages through efficient feature extraction. This paper analyzes 337 articles through a systematic literature review to investigate the application of DL for SHM in the operation and maintenance phase of facilities from three perspectives: data, DL algorithms, and applications. Firstly, the data types in SHM and the corresponding collection methods are summarized and analyzed. The most common data types are vibration signa

SUBMITTER: Jia J 

PROVIDER: S-EPMC10650096 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature