A Semi-Supervised Clustering Algorithm for Underground Disaster Monitoring and Early Warning

Due to complex geological conditions and external environmental factors, the structural safety of tunnels faces many challenges. In order to achieve real-time monitoring and early warning for tunnel safety, this paper proposes a semi-supervised clustering algorithm, named SSCME. First, in the tradit...

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Vydáno v:Electronics (Basel) Ročník 14; číslo 5; s. 965
Hlavní autoři: Gong, Qiang, Ma, Shilun, Zhang, Ning, Liu, Hongliang, Gao, Huaxin, Zhao, Yue, Jiang, Xinbo, Tu, Wenfeng, Chen, Changyuan, Yang, Fan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.03.2025
Témata:
Accuracy
Algorithms
Artificial intelligence
Classification
Clustering
Data collection
Data processing
Data transmission
Datasets
Deep learning
Deformation
Distance learning
Emergency communications systems
Geology
Landslides & mudslides
Machine learning
Methods
Monitoring
Outliers (landforms)
Parameter sensitivity
Real time
Safety and security measures
Sensors
Structural safety
Time series
Tunnels
China
ISSN:2079-9292, 2079-9292
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Shrnutí:Due to complex geological conditions and external environmental factors, the structural safety of tunnels faces many challenges. In order to achieve real-time monitoring and early warning for tunnel safety, this paper proposes a semi-supervised clustering algorithm, named SSCME. First, in the traditional concept drift detection stage, this algorithm improves traditional methods by utilizing data distribution to calculate the concept deviation and accurately identify four different types of concept drift. Second, in the incremental update stage, the EM algorithm is further optimized to remove the outlier data used for incrementally updating the classifier, thus resolving the sensitivity issue of DBSCAN in parameter selection. Finally, a large number of sensors are installed in multiple tunnels to collect data and construct datasets. The experimental results on multiple datasets demonstrate that, compared with existing baseline methods, this algorithm has higher effectiveness and reliability.
Bibliografie:ObjectType-Article-1
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics14050965