Review of artificial intelligence-based bridge damage detection
Bridges are often located in harsh environments and are thus extremely susceptible to
damage. If the initial damage is not detected in time, it can develop further causing safety …
damage. If the initial damage is not detected in time, it can develop further causing safety …
[HTML][HTML] A systematic review of data fusion techniques for optimized structural health monitoring
Advancements in structural health monitoring (SHM) techniques have spiked in the past few
decades due to the rapid evolution of novel sensing and data transfer technologies. This …
decades due to the rapid evolution of novel sensing and data transfer technologies. This …
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 …
[HTML][HTML] Long-term health monitoring of concrete and steel bridges under large and missing data by unsupervised meta learning
Long-term monitoring brings an important benefit for health monitoring of civil structures due
to covering all possible unpredictable variations in measured vibration data and providing …
to covering all possible unpredictable variations in measured vibration data and providing …
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 …
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 …
Review of machine-learning techniques applied to structural health monitoring systems for building and bridge structures
A Gomez-Cabrera, PJ Escamilla-Ambrosio - Applied Sciences, 2022 - mdpi.com
This review identifies current machine-learning algorithms implemented in building
structural health monitoring systems and their success in determining the level of damage in …
structural health monitoring systems and their success in determining the level of damage in …
A locally unsupervised hybrid learning method for removing environmental effects under different measurement periods
Environmental effects induce deceptive variability in unlabeled vibration data for structural
health monitoring (SHM). Although unsupervised learning is an effective solution to this …
health monitoring (SHM). Although unsupervised learning is an effective solution to this …
Investigation of temperature effects into long-span bridges via hybrid sensing and supervised regression models
Temperature is an important environmental factor for long-span bridges because it induces
thermal loads on structural components that cause considerable displacements, stresses …
thermal loads on structural components that cause considerable displacements, stresses …
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 …