EAN: 9783031539947

LONG-TERM STRUCTURAL HEALTH MONITORING BY REMOT...

This book offers an in-depth investigation into the complexities of long-term structural health
monitoring (SHM) in civil structures  specifically focusing on the challenges posed by small
data and environmental and operational changes (EOCs). Traditional contact-based sensor
networks in SHM produce large amounts of data  complicating big data management. In contrast 
synthetic aperture radar (SAR)-aided SHM often faces challenges with small datasets and limited
displacement data. Additionally  EOCs can mimic the structural damage  resulting in false
errors that can critically affect economic and safety issues. Addressing these challenges  this
book introduces seven advanced unsupervised learning methods for SHM  combining AI  data
sampling  and statistical analysis. These include techniques for managing datasets and
addressing EOCs. Methods range from nearest neighbor searching and Hamiltonian Monte Carlo
sampling to innovative offline and online learning frameworks  focusing on data augmentation
and normalization. Key approaches involve deep autoencoders for data processing and novel
algorithms for damage detection. Validated using simulated data from the I-40 Bridge  USA  and
real-world data from the Tadcaster Bridge  UK  these methods show promise in addressing
SAR-aided SHM challenges  offering practical tools for real-world applications. The book 
thereby  presents a comprehensive suite of innovative strategies to advance the field of SHM.

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