To create an innovative three-dimensional (3D) numerical code for wave propagation in linear and nonlinear earth media, for analysing earthquake and man-made vibratory ground motions in the presence of significant site effects, including foundation interaction domains.
– To demonstrate the practical use and reliability of the numerical code through validation with data from local seismic arrays.
– To identify new practical classifications of site-dependent ground motion hazard by th einnovative application of artificial intelligence techniques.
– To formulate improved criteria for taking site effects into account, and to incorporate them into preliminary, simplified guidelines for practical applications.<\/p>\n
The objectives will be attained by a combination of analytical tools and software, experimental observations in
the field, and laboratory tests, salient among which are:
– an original hybrid finite element- spectral appoximation algorithm that constitutes the basis for the 3D
numerical code;
– large-scale (approx. 5mx5mx2m) cyclic soil-foundation interaction tests with saturated sand at near-failure
conditions to be carried out at the ELSA reaction wall facility of JRC (Joint Research Center) Ispra. Theoretical
simulations on simplified models using existing codes will be performed; – use of strong and weak motion recordings from local seismometer arrays, including one from Kefallonia Island
(Greece), one from Japan (Ohba Ohashi bridge) and the Nya Ullevi Arena in Gothenburg (Sweden);
– use of pattern recognition methods developed in artificial intelligence to identify the most relevant site
conditions that control the level of risk, and to provide the basis of guidelines for practical applications.
The main deliverables of this project will be the software for the 3D numerical code for wave propagation and
for the automatic classification of the level of risk at each location of the study area, an experimental database
with the results of the soil-foundation interaction tests, and synthetic guidelines for evaluation of site effects.<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"_acf_changed":false},"gruppo":[],"class_list":["post-19783","projects","type-projects","status-publish","hentry"],"acf":{"title":"3D Site effects and soil-foundation interaction in earthquake and vibration risk evaluation","acronym":"TRISEE","project_uuid":"c7e27ad2-6418-11e4-917d-127e8ddc4b30","start_date":"01\/03\/1996","end_date":"30\/11\/1998","funder":"EU FP4","partners":"\n
| Nome<\/th>\n | Nazione<\/th>\n | Ruolo<\/th>\n <\/tr>\n\n |
|---|---|---|
| Consorzio Milano Ricerche<\/a><\/td>\n | Italy<\/td>\n | Coordinator<\/td>\n <\/tr>\n\n |
| Universit\u00e0 degli Studi di Cagliari<\/a><\/td>\n | Italy<\/td>\n | Contractor<\/td>\n <\/tr>\n\n |
| European Commission Joint Research Centre<\/a><\/td>\n | Belgium<\/td>\n | Contractor<\/td>\n <\/tr>\n\n |
| National Technical University of Athens<\/a><\/td>\n | Greece<\/td>\n | Contractor<\/td>\n <\/tr>\n<\/table>","team_members":[],"proj_website":"","project_status":"Completed","project_coordinator":[{"ID":10264,"post_author":"4","post_date":"2025-06-30 17:23:51","post_date_gmt":"2025-06-30 15:23:51","post_content":"Fabio Maggio is a senior researcher and Permanent Project Leader (PPL) of the Digital Technologies for Aerospace sector. His focus lies within digital agriculture and precision farming, which he explores through three foundational and interconnected ICT technologies: AI, big data, and robotics.Data pertaining to agriculture reaches CRS4 through various channels. Agro-meteorological data is automatically delivered daily, thanks to our collaboration with Arpa Sardegna; additionally, images, time series, and other forms of information are sourced from agencies for agriculture of Regione Sardegna (Laore and Agris), as well as from end users who collaborate with us. Machine learning methods play a pivotal role in data analysis and processing, such as optimal interpolation of agro-meteorological data to obtain crop-scale information. Moreover, cutting-edge deep learning models enable us to automatically classify images for early detection of field emergencies, such as occurrences of phytopathologies, weed\/pest infestations, and deficiencies in water and\/or nutritional elements. With the aid of state-of-the-art edge devices, able to mitigate the necessity for cloud computing, and optimized AI techniques, real-time classification is now achievable. This opens the way to a new generation of smart rovers (unmanned ground vehicles, UGV) capable of continuous and quasi-autonomous operation. This interdisciplinary expertise forms the foundation of a structured initiative led by Fabio and his colleagues at CRS4.Fabio also boasts decades of professional experience in numerical simulation (i.e., computer-based solutions for large-scale partial differential equations), high-performance computing (HPC) utilizing parallelism and general-purpose GPUs, and leading edge numerical methods (such as spectral elements and domain decomposition).Between 2018 and 2023, Fabio delivered courses on \"Numerical Simulation and HPC\" and \"Machine Learning\" for the Master's Degree program in Mathematics at the University of Cagliari.","post_title":"Fabio Maggio","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"fabio-maggio","to_ping":"","pinged":"","post_modified":"2026-04-09 15:11:53","post_modified_gmt":"2026-04-09 13:11:53","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.crs4.it\/people\/fabio-maggio\/","menu_order":0,"post_type":"people","post_mime_type":"","comment_count":"0","filter":"raw"}]},"yoast_head":"\n |