Artificial Intelligence Monitoring and Early Warning in Rock Engineering
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".
Deadline for manuscript submissions: closed (20 August 2024) | Viewed by 3670
Special Issue Editors
Interests: artificial intelligence; early warning
Interests: intelligent mining; risk assessment
Interests: monitoring technology; big data
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Rock engineering plays an important role in the development of human society. Due to the complexity of engineering geological conditions, some disasters will inevitably occur during the construction process, such as landslide, rockburst, water inrush, large deformation and rock collapse. One of the important reasons lies in the unreliability of data collection and analysis, which makes the workers unable to grasp the spatial and temporal evolution information of disasters in time. Therefore, it is of great significance to study high-precision disaster monitoring technologies and intelligent early-warning approaches. More recently, artificial intelligence (AI) technologies such as machine learning, deep learning, machine vision and intelligent optimization have developed rapidly. They can be adopted to achieve reliable disaster monitoring and early warning in rock engineering. This Special Issue welcomes papers on the state-of-the-art applications of AI in the monitoring and early warning of rock engineering. The key areas include, but are not limited to:
- Advanced intelligent monitoring technology in rock engineering;
- AI in rock fracture signal monitoring;
- Machine vision in rock deformation monitoring;
- AI-based dynamic disaster risk assessment;
- Intelligent diagnosis of disaster precursory information;
- Time series prediction of monitoring data;
- Big data in managing disaster information;
- Early warning methods based on multi-source data.
Dr. Weizhang Liang
Prof. Dr. Kang Peng
Dr. Ju Ma
Dr. Hao Wu
Guest Editors
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Keywords
- artificial intelligence (AI)
- monitoring technology
- early warning
- rock engineering
- risk prediction
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