Intelligent Technologies for Understanding and Controlling the Impact of Geological Disasters on Construction
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Hazards and Sustainability".
Deadline for manuscript submissions: closed (2 July 2024) | Viewed by 8986
Special Issue Editors
Interests: deep rock mechanics and engineering safety
Interests: rock mechanics and testing; rockburst prevention; mining safety
Special Issue Information
Dear Colleagues,
Geological disasters associated with engineering construction seriously restrict the sustainable exploitation of large-scale resources. The geological disasters impacting construction are varied, such as rockburst in deep tunnels and mines, landslide of reservoir slopes, and collapses in underground powerhouses. In recent years, the technology for testing, monitoring and preventing these disasters has made great progress. These achievements play a vital role in ensuring the safety and sustainability of major projects. However, under the background of global industrial intelligent reform, the intelligent level of disaster observation and prevention technology needs to be improved.
This Special Issue aims to collect innovative achievements in intelligent technologies toward understanding and controlling the impact of geological disasters on the built environment. Original research and review papers are welcome. The topics of interest include, but are not limited to, the following:
- Laboratory test technology of disasters;
- Intelligent perception technology for disaster prevention;
- Big data analysis of disaster precursor information;
- Disaster warning cloud platforms based on deep learning;
- Self-decision technology and equipment for disaster control.
We look forward to receiving your contributions.
Dr. Yaxun Xiao
Prof. Dr. Yanchun Yin
Dr. Haitao Li
Guest Editors
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Keywords
- intelligent technology
- construction geological disaster
- rockburst
- laboratory test
- intelligent perception
- disaster inoculation
- big data analysis
- deep learning
- disaster warning
- self-decision disaster control
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