A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Machine Learning Models Applied to a GNSS Sensor Network for Automated Bridge Anomaly Detection
https://orcid.org/0000-0001-7011-0940
https://orcid.org/0000-0002-0653-9213
Structural health monitoring (SHM) based on global navigation satellite systems (GNSS) is an interesting solution to provide absolute positions at different locations of a structure in a global reference frame. In particular, low-cost GNSS stations for large-scale bridge monitoring have gained increasing attention these last years because recent experiments showed the ability to achieve a subcentimeter accuracy for continuous monitoring with adequate combinations of antennas and receivers. Technical solutions now allow displacement monitoring of long bridges with a cost-effective deployment of GNSS sensing networks. In particular, the redundancy of observations within the GNSS network with various levels of correlations between the GNSS time series makes such monitoring solution a good candidate for anomaly detection based on machine learning models, using several predictive models for each sensor (based on environmental conditions, or other sensors as input data). This strategy is investigated in this paper based on GNSS time series, and an anomaly indicator is proposed to detect and locate anomalous structural behavior. The proposed concepts are applied to a cable-stayed bridge for illustration, and the comparison between multiple tools highlights recurrent neural networks (RNN) as an effective regression tool. Coupling this tool with the proposed anomaly detection strategy enables one to identify and localize both real and simulated anomalies in the considered data set.
Machine Learning Models Applied to a GNSS Sensor Network for Automated Bridge Anomaly Detection
https://orcid.org/0000-0001-7011-0940
https://orcid.org/0000-0002-0653-9213
Structural health monitoring (SHM) based on global navigation satellite systems (GNSS) is an interesting solution to provide absolute positions at different locations of a structure in a global reference frame. In particular, low-cost GNSS stations for large-scale bridge monitoring have gained increasing attention these last years because recent experiments showed the ability to achieve a subcentimeter accuracy for continuous monitoring with adequate combinations of antennas and receivers. Technical solutions now allow displacement monitoring of long bridges with a cost-effective deployment of GNSS sensing networks. In particular, the redundancy of observations within the GNSS network with various levels of correlations between the GNSS time series makes such monitoring solution a good candidate for anomaly detection based on machine learning models, using several predictive models for each sensor (based on environmental conditions, or other sensors as input data). This strategy is investigated in this paper based on GNSS time series, and an anomaly indicator is proposed to detect and locate anomalous structural behavior. The proposed concepts are applied to a cable-stayed bridge for illustration, and the comparison between multiple tools highlights recurrent neural networks (RNN) as an effective regression tool. Coupling this tool with the proposed anomaly detection strategy enables one to identify and localize both real and simulated anomalies in the considered data set.
Machine Learning Models Applied to a GNSS Sensor Network for Automated Bridge Anomaly Detection
https://orcid.org/0000-0001-7011-0940
https://orcid.org/0000-0002-0653-9213
Structural health monitoring (SHM) based on global navigation satellite systems (GNSS) is an interesting solution to provide absolute positions at different locations of a structure in a global reference frame. In particular, low-cost GNSS stations for large-scale bridge monitoring have gained increasing attention these last years because recent experiments showed the ability to achieve a subcentimeter accuracy for continuous monitoring with adequate combinations of antennas and receivers. Technical solutions now allow displacement monitoring of long bridges with a cost-effective deployment of GNSS sensing networks. In particular, the redundancy of observations within the GNSS network with various levels of correlations between the GNSS time series makes such monitoring solution a good candidate for anomaly detection based on machine learning models, using several predictive models for each sensor (based on environmental conditions, or other sensors as input data). This strategy is investigated in this paper based on GNSS time series, and an anomaly indicator is proposed to detect and locate anomalous structural behavior. The proposed concepts are applied to a cable-stayed bridge for illustration, and the comparison between multiple tools highlights recurrent neural networks (RNN) as an effective regression tool. Coupling this tool with the proposed anomaly detection strategy enables one to identify and localize both real and simulated anomalies in the considered data set.
Machine Learning Models Applied to a GNSS Sensor Network for Automated Bridge Anomaly Detection
J. Struct. Eng.
Manzini, Nicolas (author) / Orcesi, André (author) / Thom, Christian (author) / Brossault, Marc-Antoine (author) / Botton, Serge (author) / Ortiz, Miguel (author) / Dumoulin, John (author)
2022-11-01
Article (Journal)
Electronic Resource
English
Anomaly Detection in Dam Behaviour with Machine Learning Classification Models
| DOAJ | 2021
A survey of machine learning methods applied to anomaly detection on drinking-water quality data
| Taylor & Francis Verlag | 2019
Neural Network Novelty Filtering for Anomaly Detection of Tsing Ma Bridge Cables
| British Library Conference Proceedings | 1999
Models and Methods for Automated Background Density Estimation in Hyperspectral Anomaly Detection
| Online Contents | 2013
GNSS and Multi-sensor Fusion Technique Applied to an Experimental Model
| British Library Conference Proceedings | 2016