Monitoring and real time risk analysis of earth dams

Earth dams, either natural or developed as part of mining operations (tailing dams) are prone to failure. In particular, recent studies show that tailing dams have a worldwide failure rate close to one collapse per year [1].

In this work we present the developments done in the monitoring and risk assessment for dams; including sensor technology, real-time numerical modelling and safety factor calculation. The recent surge in the availability of sensors allows enhancing the data that can be gathered to monitor the mechanical and hydraulic state of the dams. Numerical models can be used to enrich the local information collected by the sensors (e.g. piezometers, inclinometers) and provide the current physical state of the dam.

For monitoring purposes, numerical models are only useful if they provide results fast enough to react to an unsafe state. The results presented include the works of [2] and [3], where model order reduction techniques are applied in the context of data assimilation to learn about the state of dams. A transient nonlinear hydro-mechanical model describing the groundwater flow in unsaturated soil conditions is solved using Reduced Basis method. Hyper-reduction techniques (DEIM, LDEM) are tested and show time gains up to 1/100 with respect to standard finite element methods.

REFERENCES

[1] Clarkson, Luke, and David Williams. "Critical review of tailings dam monitoring best practice.International Journal of Mining, Reclamation and Environment, 34.2: 119-148, doi:10.1080/17480930.2019.1625172, 2020.

[2] Nasika C., P. Díez, P. Gerard, T.J. Massart and S. Zlotnik. Towards real time assessment of earthfill dams via Model Order Reduction. Finite Elements in Analysis & Design, Vol. 199, 103666, doi:10.1016/j.finel.2021.103666, 2022.

[3] Nasika C., P. Díez, P. Gerard, T.J. Massart and S. Zlotnik. Discrete Empirical Interpolation for hyper-reduction of hydro-mechanical problems in groundwater flow through soil. Journal for Numerical and Analytical Methods in Geomechanics, doi:10.1002/nag.3487, 2022.