Vibration feature extraction using signal processing techniques for structural health monitoring: A review
Structural health monitoring (SHM) has become an important and hot topic for decades in
various fields of civil, mechanical, automotive, and aerospace engineering, etc. Estimating …
various fields of civil, mechanical, automotive, and aerospace engineering, etc. Estimating …
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 …
[HTML][HTML] A novel double-hybrid learning method for modal frequency-based damage assessment of bridge structures under different environmental variation patterns
Monitoring of modal frequencies under an unsupervised learning framework is a practical
strategy for damage assessment of civil structures, especially bridges. However, the key …
strategy for damage assessment of civil structures, especially bridges. However, the key …
Structural health monitoring by a novel probabilistic machine learning method based on extreme value theory and mixture quantile modeling
This article proposes a novel probabilistic machine learning method based on unsupervised
novelty detection for health monitoring of civil structures. The core of this method is based on …
novelty detection for health monitoring of civil structures. The core of this method is based on …
Unsupervised learning-based damage assessment of full-scale civil structures under long-term and short-term monitoring
MH Daneshvar, H Sarmadi - Engineering Structures, 2022 - Elsevier
Abstract Machine learning has become an influential and useful tool for many civil
engineering applications, particularly structural health monitoring (SHM). For this reason …
engineering applications, particularly structural health monitoring (SHM). For this reason …
Probabilistic data self-clustering based on semi-parametric extreme value theory for structural health monitoring
Clustering is a popular and useful unsupervised learning method with various algorithms for
applying to many engineering problems. However, some practical and technical issues such …
applying to many engineering problems. However, some practical and technical issues such …
On continuous health monitoring of bridges under serious environmental variability by an innovative multi-task unsupervised learning method
Abstract Design of an automated and continuous framework is of paramount importance to
structural health monitoring (SHM). This study proposes an innovative multi-task …
structural health monitoring (SHM). This study proposes an innovative multi-task …
A review of the application of the simulated annealing algorithm in structural health monitoring (1995-2021)
In recent years, many innovative optimization algorithms have been developed. These
algorithms have been employed to solve structural damage detection problems as an …
algorithms have been employed to solve structural damage detection problems as an …
Non-parametric empirical machine learning for short-term and long-term structural health monitoring
A Entezami, H Shariatmadar… - Structural Health …, 2022 - journals.sagepub.com
Early damage detection is an initial step of structural health monitoring. Thanks to recent
advances in sensing technology, the application of data-driven methods based on the …
advances in sensing technology, the application of data-driven methods based on the …
Online structural health monitoring by model order reduction and deep learning algorithms
Within a structural health monitoring (SHM) framework, we propose a simulation-based
classification strategy to move towards online damage localization. The procedure combines …
classification strategy to move towards online damage localization. The procedure combines …