MS14 (TC304) Multi-source and Multi-methodological Data Fusion to Improve the Reliability of Geo-engineering and Environmental Characterization, Urban planning, and Geotechnical Designing
PhD. Di Curzio Diego: firstname.lastname@example.org
Di Curzio Diego, Ph.D., Department of Engineering and Geology (InGeo), University "G. d'Annunzio" of Chieti-Pescara, Italy, email@example.com
Castrignanò Annamaria, Prof., Department of Engineering and Geology (InGeo), University "G. d'Annunzio" of Chieti-Pescara, Italy, firstname.lastname@example.org
Pula Wojciech, Prof., Polytechnic of Wroclaw, Poland, wojciech.Pula@pwr.edu.pl
Vessia Giovanna, Prof., Department of Engineering and Geology (InGeo), University "G. d'Annunzio" of Chieti-Pescara, Italy, email@example.com
Abstract of the special session:
This is a session organized by TC304.
Several new technologies in the remote and proximal sensing, precise geophysical sensors and enhanced geotechnical testing devices are enriching the geo-datasets with spatial and temporal observation records. These multi-source and multi-scale pieces of information must be integrated in order to improve the reliability of the models describing mechanical and environmental processes in soil, rock, surface water and groundwater. In addition, the outcomes of this approach could lead to an enhanced infrastructure and structure designing as well as more effective monitoring and natural hazards assessment, especially in urban areas. To this end, scientists are required to use advanced methodological approaches, in the field of statistics, geostatistics, machine learning, artificial intelligence, and numerical modeling, to perform spatial-temporal multi-source data fusion.
All contributors interested in proposing data fusion approaches applied to geotechnical, geo-engineering and environmental issues are warmly welcome.
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