Mountain territories are remarkably exposed to natural phenomena such as torrential floods, arising due to climate and geophysical environmental changes. Protection structures deteriorate with time due to the harsh phenomena they are subjected to since their construction. If not regularly maintained, the level of protection offered by these structures will be reduced. The methodology presented in this paper integrates physics-based and dependability models for monitoring the state evolution of protection structures and improving maintenance decision-making processes. The modeling approach proposed is based on 1) physics-based modeling for identifying the probabilistic laws of the transition times between the defined states of the structure depending on its behavior over time and 2) a decision aiding method based on Petri nets, which helps in choosing the best maintenance strategy while considering budgetary constraints. This approach is applied on a check dam located within a series of check dams in the Manival torrent in Saint-Ismier, France.
Monitoring bedload transport is of interest for studying river morphology evolution and hydraulic structures stability (e.g., dams andbridges). Bedload self‐generated noise measured by hydrophones has been experimentally correlated to bedload flux in several studies. However, the lack of theoretical background linking the recorded acoustic noise to bedload and river characteristics has prevented a good understanding of the experimental results. Here, we develop a model of the acoustic noise generated by bedload transport in rivers. The model provides an estimation of the acoustic power generated by impacts of bedload particles with riverbed particles. In this model, we account for bedload kinematics (e.g., impact velocities, saltation length) and the environment in which acoustic wave is propagated (e.g., acoustic wave attenuation). Sensitivity analysis shows that the noise generated by bedload transport depends on the grain size distribution and river characteristics such as slope, water level, and propagation effects. We tested the model on a field data set comprising acoustic and direct bedload measurements from different rivers. The acoustic powers predicted by the model are consistent with field measurements for some rivers while questionable for other ones. The analysis has shown a great sensitivity of the model to bedload kinematics (in particular the way the grain velocity is computed) and riverbed grain size distribution. The model provides a first basis that can serve as a framework in future work concerned with acoustic measurements of bedload transport. However, the model is still imperfect and it is limited by today's knowledge of the physics of bedload transport.
Abstract. Recent studies have shown that hydrophone sensors can monitor bedload flux in rivers by measuring the self-generated noise (SGN) emitted by bedload particles when they impact the riverbed. However, experimental and theoretical studies have shown that the measured SGN depends not only on bedload flux intensity but also on the propagation environment, which differs between rivers. Moreover, the SGN can propagate far from the acoustic source and be well measured at distant river positions where no bedload transport exists. It has been shown that this dependence of the SGN measurements on the propagation environment can significantly affect the performance of monitoring bedload flux by hydrophone techniques. In this article, we propose an inversion model to solve the problem of SGN propagation and integration effect. In this model, we assume that the riverbed acts as SGN source areas with intensity proportional to the local bedload flux. The inversion model locates the SGN sources and calculates their corresponding acoustic power by solving a system of linear algebraic equations accounting for the actual measured cross-sectional acoustic power (acoustic mapping) and attenuation properties. We tested the model using two field campaigns conducted in 2018 and 2021 on the Giffre River in the French Alps, which measured the bedload SGN profile (acoustic mapping with a drift boat) and bedload flux profile (direct sampling with an Elwha sampler). Results confirm that the bedload flux profile better correlates with the inversed acoustic power than measured acoustic power. Moreover, it was possible to fit the two field campaign with a unique curve after inversion, which was not possible with the measured acoustic data. The inversion model shows the importance of considering the propagation effect when using the hydrophone technique and offers new perspectives for the calibration of bedload flux with SGN in rivers.
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