W. Pula, Wrocław University of Science and Technology, Poland: wojciech.pula@pwr.edu.pl
G. Vessia, University “G. d’Annunzio” of Chieti-Pescara, Italy: g.vessia@unich.it
D. Di Curzio, University “G. d’Annunzio” of Chieti-Pescara, Italy: diego.dicurzio@unich.it
M. Chwała, Wrocław University of Science and Technology, Poland: marcin.chwala@pwr.edu.pl

Subsoil characterization and modelling for engineering structure designing and urban planning is the main concern for geological, hydrogeological and geotechnical studies performed in current practise. As long as the size of the investigated area increases heterogeneities and spatial variability of soils and rocks must be taken into account. Since sixties, geostatistical methods, random field theory and several other numerical algorithms have been developed to manage physio-chemo-mechanical properties as regionalized variables meanwhile data driven methods (e.g. Bayesian, neural network, fuzzy methos, machine learning algorithms) have been conceived to be applied to space and temporal varying environmental parameters. Focusing on geoengineering applications, this session is aimed at discussing new methods or novel applications of several different data driven approaches to evidence the efficiency of different methods to achieve reliable spatial and temporal models of subsoil properties to be used in different kind of geotechnical designs and hazard maps. In addition, will be appreciated all those contributions conceived to support and develop a reliable territorial managing especially devoted to natural risk assessment and mitigation under extreme events due to climate changes. Thus, all the contributions focused on the above mention topics are warmly welcomed.