Mesures in-situ d'impacts de vagues sur une digue composite In-situ measurements of wave impacts on a composite breakwater

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LaboREM: A Remote Laboratory for Game-Like Training in Electronics

International audienceThe advances in communication networks and web technologies, in conjunction with the improved connectivity of test and measurement devices make it possible to implement e-learning applications that encompass the whole learning process. In the field of electrical engineering, automation or mechatronics, it means not only lectures, tutorials, demos and simulations, but also practical labwork for training with real-world devices that are controlled remotely. To make e-labs attractive, they should be easily implemented and accessed on the web by a client. This keypoint raises technical issues that are recalled in this paper. Nonetheless pedagogical issues are equally important. The benefit of a remote lab must be evaluated and compared to simulation labs or hands-on. Here, to foster student motivation, a game-like scenario embedded in a learning management system is proposed

Mesure in-situ d'impact de vague sur une digue composite

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In-Situ Measurement of Wave Impact Pressure on a Composite Breakwater in St-Jean-de-Luz

COMInternational audienc

Mesures in situ des impacts de vagues sur une digue composite : résultats préliminaires

ACTInternational audienc

USE OF GAMING AND COMPUTER VISION TO DRIVE STUDENT MOTIVATION IN REMOTE LEARNING LAB ACTIVITIES

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Vision par ordinateur pour suivi automatique de civelles en bassin

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In-situ measurements of energetic depth-limited wave loading

International audienceAn extensive database of in situ measurements of wave impact pressure on the wall of a composite breakwater and associated explanatory variables (i.e., waves, wind, and water level) was collected in a particularly high-energy wave environment. Due to the bottom profile, which includes a wide mound of concrete blocks with a seaward edge that rises to the Lowest Astronomical Tide level over a large distance, most waves break before reaching the monitored barrier, making the data set representative of depth-limited wave loading. Maximum pressure is consistently found at the sensor position closest to the mean free surface, and statistically, a decrease of maximum pressure with the altitude is observed. Nevertheless, the database also shows a wide variety of vertical profiles of maximum pressure. A detailed analysis of the pressure signal shows that there are two impact classes associated with large pressure values. The first is mostly observed during stormy conditions with relatively slow pressure variations over time and a fairly uniform spatial distribution. The second class exhibits very limited pressure peaks in time and space and is most often observed during moderate sea states and high water levels. The pressure signals for each class agree well with the prediction of the PROVERB [35] impact classification based on breakwater dimensions

Continuous measurement and automatic processing of in-situ wave impact pressure data

International audienceAlthough wave impact has been extensively studied in laboratories, field studies are comparatively rare. However, as real wave impacts are influenced by numerous environmental factors, complementing physical studies with in-situ data is necessary to better understand the processes at stake and provide reliable tools for coastal engineers. One of the main reasons for the lack of field data is the extreme conditions usually met on site. Nowadays, technology allows to set up stations able to resist those conditions and record data over long periods. In this context, the so-called Artha breakwater, in the French Basque coast, was equipped with an in-situ laboratory to record wave impact pressures. This station enables to collect long term wave impact pressure data therefore covering any weather conditions. In the present paper, the use of computer engineering based methods to process the large amount of wave impact data is described. It involves signal pre-processing, impact automatic segmentation, automatic computation of impact parameters, and artificial intelligence to classify the impacts. Impact automatic segmentation allows to have a big database of impacts available. This database has been used to make a first classification of the strongest impacts. The classification was performed thanks to the parameters automatically computed for each impact. As preliminary results for the classification, several wave impact pressure classes have been established. The approach is encouraging since the obtained results can be compared with the existing laboratory classification. However, the results can still be improved by computing other impact parameters and considering all impacts