 |
|||
| Url | https://cimne.com/sgp/rtd/Project.aspx?id=1025 | ||
|
|||
| Acronym | MOTADA | ||
| Project title | Model-based and Data-driven Methods for Tailings Dams Monitoring | ||
| Reference | PID2023-148952OB-I00 | ||
| Principal investigator |
Ivan MARKOVSKY - imarkovsky@cimne.upc.edu
Alba MUIXI BALLONGA - alba.muixi@upc.edu |
||
| Start date | 01/09/2024 | End date | 31/08/2027 |
| Coordinator | CIMNE | ||
| Consortium members |
|
||
| Program | P.E. para Impulsar la Investigación Científico-Técnica y su Transferencia | Call | Proyectos Generación de Conocimiento 2023 |
| Subprogram | Subprograma Estatal de Generación de Conocimiento | Category | Nacional |
| Funding body(ies) | MCIU | Grant | $48,750.00 |
| Abstract | Tailings dams are recognized as high-risk structures due to the high rate of failure that they exhibit, in combination with the severe consequences that these failures entail. Currently, structural health monitoring of dams is done by human inspection and human-assisted analysis of data from sensors placed on the structure. The MOTADA project aims at developing computational methods for fully automatic real-time data processing in order to assist human decision-making. The goal of the project is to create an early warning tool for anomalies within the structure, capable of detecting the fault (fault detection) and providing information about the type of fault (fault isolation). The core idea is to integrate real-time sensors data with pre-existing mathematical models of the structure. The models are used for offline data generation under different scenarios (normal operation and operation under various faults). Then, novel data-driven methods are used for real-time processing of measurements from the structure and the offline data in order to assess its structural health condition. The approach will be validated for earth-fill tailings dams, but can be generalized for monitoring alternative applications in the future. The used methodology is highly novel and unconventional---it is based on the behavioral system theory and newly-emerged-from-it direct datadriven methods, in combination with reduced order modeling strategies from computational mechanics. If successful, the project will be a major step forward in monitoring of complex spatio-temporal systems. The project is multidisciplinary as it involves concepts and methods from systems & control, structural mechanics, and numerical linear algebra. | ||
| Proyecto PID2023-148952OB-I00 financiado por MCIU/AEI/10.13039/501100011033/ FEDER, UE | |||