Early detection of internal erosion in dams/levees: bridging between micro and macro scale behaviour
Project Leader: Negin Yousefpour
Staff: Dr Mahdi Miri Disfani
Collaborators: Dr Guillermo Narsilio, TBD
Primary Contact: Negin Yousefpour (firstname.lastname@example.org)
Keywords: artificial intelligence
Disciplines: Infrastructure Engineering
Internal erosion is one of the major failure causes of dams and levees. Detecting internal erosion at early stages has been one of the remaining challenges in dam industry. This project investigates A/ML and other data-driven methods to detect the internal erosion from monitoring data at early stages to serve as early warning systems. This includes monitoring data from acoustic emission, resistivity, thermal and settlement measurements, fiber optics, geophysical/seismic methods, etc. In addition by evaluating the field measurement datacombined with micro-scale characteristics of internal erosion obtained from lab tests (triaxial erosion), x-ray scans and imaging of samples, as well as DEM simulations, this project aims at better understanding of patterns of internal erosion, early symptoms in site and bridging between macro and micro scale behavior of soil during internal erosion.