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 …
Recent advancements and future trends in indirect bridge health monitoring
Bridges hold an imperative role in the transportation network and infrastructure. Continuous
monitoring of their condition is crucial for the efficient operation of transportation facilities …
monitoring of their condition is crucial for the efficient operation of transportation facilities …
Looseness monitoring of multiple M1 bolt joints using multivariate intrinsic multiscale entropy analysis and Lorentz signal-enhanced piezoelectric active sensing
Bolts are widely used in the fields of mechanical, civil, and aerospace engineering. The
condition of bolt joints has a significant impact on the safe and reliable operation of the …
condition of bolt joints has a significant impact on the safe and reliable operation of the …
Multiclass damage identification in a full-scale bridge using optimally tuned one-dimensional convolutional neural network
In this paper, a novel method is proposed based on a windowed one-dimensional
convolutional neural network (1D CNN) for multiclass damage identification using vibration …
convolutional neural network (1D CNN) for multiclass damage identification using vibration …
Reconstruction of long-term strain data for structural health monitoring with a hybrid deep-learning and autoregressive model considering thermal effects
Complete data are essential for implementing reliable structural health monitoring (SHM);
however, data loss due to equipment malfunction or other potential factors is unavoidable …
however, data loss due to equipment malfunction or other potential factors is unavoidable …
[HTML][HTML] Structural damage detection and localization via an unsupervised anomaly detection method
This study introduces an unsupervised machine learning framework for damage detection
and localization in Structural Health Monitoring (SHM), leveraging dynamic graph …
and localization in Structural Health Monitoring (SHM), leveraging dynamic graph …
Comparative study of damage modeling techniques for beam-like structures and their application in vehicle-bridge-interaction-based structural health monitoring
J Zhou, Z Zhou, Z **, S Liu… - Journal of Vibration and …, 2024 - journals.sagepub.com
Damage modeling techniques are essential tools for revealing the impact pattern of damage
on structural responses. Currently, different damage level parameters are defined for …
on structural responses. Currently, different damage level parameters are defined for …
Identifying the dynamic characteristics of super tall buildings by multivariate empirical mode decomposition
In this study, multivariate empirical mode decomposition (MEMD) is used to evaluate the
dynamic characteristics of super‐tall buildings. Two super‐tall buildings, including Milad …
dynamic characteristics of super‐tall buildings. Two super‐tall buildings, including Milad …
A wavelet-based dynamic mode decomposition for modeling mechanical systems from partial observations
Dynamic mode decomposition (DMD) has emerged as a popular data-driven modeling
approach to identifying spatio-temporal coherent structures in dynamical systems, owing to …
approach to identifying spatio-temporal coherent structures in dynamical systems, owing to …
Damage identification in concrete structures using a hybrid time–frequency decomposition of acoustic emission responses
Concrete structures are subjected to various levels of damage during their life cycle due to
exposure to different environmental and loading conditions. Hence, damage severity …
exposure to different environmental and loading conditions. Hence, damage severity …