Unsupervised learning methods for data-driven vibration-based structural health monitoring: a review
Structural damage detection using unsupervised learning methods has been a trending
topic in the structural health monitoring (SHM) research community during the past decades …
topic in the structural health monitoring (SHM) research community during the past decades …
Review of bridge structural health monitoring aided by big data and artificial intelligence: From condition assessment to damage detection
Structural health monitoring (SHM) techniques have been widely used in long-span bridges.
However, due to limitations of computational ability and data analysis methods, the …
However, due to limitations of computational ability and data analysis methods, the …
Vibration-based damage detection for bridges by deep convolutional denoising autoencoder
One of the main challenges for structural damage detection using monitoring data is to
acquire features that are sensitive to damages but insensitive to noise (eg sensor …
acquire features that are sensitive to damages but insensitive to noise (eg sensor …
A feature extraction & selection benchmark for structural health monitoring
T Buckley, B Ghosh, V Pakrashi - Structural Health …, 2023 - journals.sagepub.com
There are a large number of time domain, frequency domain and time-frequency signal
processing methods available for univariate feature extraction. However, there is no …
processing methods available for univariate feature extraction. However, there is no …
Sensitivity analysis of subspace-based damage indicators under changes in ambient excitation covariance, severity and location of damage
It is common practice in Structural Health Monitoring (SHM) to perform damage detection by
detecting changes in subspace-based damage indicators. They are computed by comparing …
detecting changes in subspace-based damage indicators. They are computed by comparing …
A reliability-based approach to determine the minimum detectable damage for statistical damage detection
This paper derives a formula to determine the minimum detectable damage based on
ambient vibration data. It is a key element to analyze which damage scenarios can be …
ambient vibration data. It is a key element to analyze which damage scenarios can be …
Performance assessment of prestressed concrete bridge girders using fiber optic sensors and artificial neural networks
Structural health monitoring (SHM) activities are essential for achieving a realistic
characterisation of bridge structural performance levels throughout the service life. These …
characterisation of bridge structural performance levels throughout the service life. These …
Towards robust statistical damage localization via model-based sensitivity clustering
Damage diagnosis is a fundamental task for structural health monitoring (SHM). With the
statistical sensitivity-based damage localization approach, a residual vector is computed …
statistical sensitivity-based damage localization approach, a residual vector is computed …
Subspace‐based Mahalanobis damage detection robust to changes in excitation covariance
In the context of detecting changes in structural systems, several vibration‐based damage
detection methods have been proposed and successfully applied to both mechanical and …
detection methods have been proposed and successfully applied to both mechanical and …
Subspace features and statistical indicators for neural network-based damage detection
The late opportunities prompted by artificial intelligence have motivated the current research
about structural damage detection strategies based on damage-sensitive subspace-based …
about structural damage detection strategies based on damage-sensitive subspace-based …