Advances in Sensors, Monitoring, and Intelligence Techniques for Geotechnical Engineering and Geology

Dear Colleagues,

The latest developments in sensors are providing undeniable influence on real-time monitoring techniques employed for geotechnical engineering and geology projects. Technologies such as particle image velocimetry, excavation, transparent soils and computed tomography scanning are now being applied to monitor the behavior of geo-based materials as well as engineering earth structures under different conditions in the field or small-scale laboratories. Computer-based vision methods, LiDAR, and field observation wireless-based sensor networks are just a few examples.

In addition, new satellite and UAV photogrammetric sensors and cameras provide new solutions for generating data of large spatial coverage and very high resolution at the field scale with relatively low cost for geotechnical engineering and geological exploration projects. Moreover, advanced artificial intelligence, metaheuristic optimization, and data-science can be a reliable methods for geotechnical engineering and geology approaches employed for exploration, risk assessment, construction, design, and maintenance at a higher level. This Special Issue invites scholars to share recently developed advances in sensors and intelligent techniques in sensors, monitoring, and intelligence techniques for geotechnical engineering and geology. 

We kindly invite scientists to contribute novel and original research to this Special Issue, attributing at least one of below topics:

• Advances in sensors and intelligent methods for large-scale geotechnical engineering and geological exploration projects
• Recent advances in real-time monitoring, early failure systems, and risk assessment
• Recent advances in artificial intelligence and meta-heuristic optimization algorithms employed after small/large scale laboratory experiments
• Real-life case studies of applied sensors, geospatial data science, and intelligence techniques in engineering projects with findings of clear interest to the scientific community
• Finally, the authors are encouraged to share data and codes (if possible) for considering the reproducibility of their works as well as future improvements of research.

Prof. Dr. Dieu Tien Bui
Dr. Hossein Moayedi
Dr. Viet Ha Nhu
Dr. Paraskevas Tsangaratos
Guest Editors

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