Deep learning for data anomaly detection and data compression of a long‐span suspension bridge
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Deep learning for data anomaly detection and data compression of a long‐span suspension bridge
Authors
Keywords
-
Journal
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
Volume -, Issue -, Pages -
Publisher
Wiley
Online
2019-12-28
DOI
10.1111/mice.12528
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- A methodology for obtaining spatiotemporal information of the vehicles on bridges based on computer vision
- (2019) Bo Zhang et al. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
- A novel unsupervised deep learning model for global and local health condition assessment of structures
- (2018) Mohammad Hossein Rafiei et al. ENGINEERING STRUCTURES
- Structural damage identification based on autoencoder neural networks and deep learning
- (2018) Chathurdara Sri Nadith Pathirage et al. ENGINEERING STRUCTURES
- Sparse representation approach to data compression for strain-based traffic load monitoring: A comparative study
- (2018) Helder Sousa et al. MEASUREMENT
- Zernike‐moment measurement of thin‐crack width in images enabled by dual‐scale deep learning
- (2018) FuTao Ni et al. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
- Pixel-level crack delineation in images with convolutional feature fusion
- (2018) FuTao Ni et al. Structural Control & Health Monitoring
- Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring
- (2018) Zhiyi Tang et al. Structural Control & Health Monitoring
- Development of sensor validation methodologies for structural health monitoring: A comprehensive review
- (2017) Ting-Hua Yi et al. MEASUREMENT
- Compressive sensing of wireless sensors based on group sparse optimization for structural health monitoring
- (2017) Yuequan Bao et al. STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
- A New Neural Dynamic Classification Algorithm
- (2017) Mohammad Hossein Rafiei et al. IEEE Transactions on Neural Networks and Learning Systems
- Detection of Shifts in GPS Measurements for a Long-Span Bridge Using CUSUM Chart
- (2016) Ting-Hua Yi et al. International Journal of Structural Stability and Dynamics
- Canonical correlation analysis based fault diagnosis method for structural monitoring sensor networks
- (2016) Hai-Bin Huang et al. Smart Structures and Systems
- Research and practice of health monitoring for long-span bridges in the mainland of China
- (2015) Hui Li et al. Smart Structures and Systems
- Evaluation of passenger health risk assessment of sustainable indoor air quality monitoring in metro systems based on a non-Gaussian dynamic sensor validation method
- (2014) MinJeong Kim et al. JOURNAL OF HAZARDOUS MATERIALS
- Application of statistical monitoring using latent-variable techniques for detection of faults in sensor networks
- (2013) Miguel R Hernandez-Garcia et al. JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES
- SMC structural health monitoring benchmark problem using monitored data from an actual cable-stayed bridge
- (2013) Shunlong Li et al. Structural Control & Health Monitoring
- Sensor fault diagnosis with a probabilistic decision process
- (2012) Reza Sharifi et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Compressive sampling–based data loss recovery for wireless sensor networks used in civil structural health monitoring
- (2012) Yuequan Bao et al. STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started