Caractérisation d’un modèle d’indentation permanente pour impact basse vitesse à l’aide d’un essai de flexion 3 points

International audienceno abstrac

The effect of stacking sequence on the low-velocity impact response of composite laminates

International audienceStacking sequence is an important effect in optimizing composite structures on the low-velocity impact. This paper presents 7 out of 12 possible configurations of eight-double-ply, mirror-symmetric, quasi-isotropic, and laminated plates of T700GC/M21, oriented at 0°, 90°, 45° and -45°. Thanks to discrete modelling with interface finite elements based on fracture mechanics, a finite element model simulates impact damages. The numerical simulations can well predict the impact damages. Comparison of stacking sequence in term of areas and shapes of delamination, and fibre failures are discussed

Discrete ply modelling of low velocity impact and compression after impact in unidirectional laminated composites

International audienceThis paper deals with impact damage, permanent indentation and compression after impact (CAI) modelling. A model enabling the formation of damage developing during low velocity / low energy impact test and CAI test in laminated composite panels has been elaborated. The different impact and CAI damage types, i.e. matrix cracking, fibres failure and interfaces delamination, are simulated. This model is compared to experimental tests and is used to highlight the failure scenario of laminate during residual compression test. Finally the impact energy effect on the residual strength is evaluated and compared to experimental results

The effect of stacking sequence on the low-velocity impact response of composite laminates

International audienceStacking sequence is an important effect in optimizing composite structures on the low-velocity impact. This paper presents 7 out of 12 possible configurations of eight-double-ply, mirror-symmetric, quasi-isotropic, and laminated plates of T700GC/M21, oriented at 0°, 90°, 45° and -45°. Thanks to discrete modelling with interface finite elements based on fracture mechanics, a finite element model simulates impact damages. The numerical simulations can well predict the impact damages. Comparison of stacking sequence in term of areas and shapes of delamination, and fibre failures are discussed

Modelling permanent indentation in composite subjected to impact : identification through a specific three-point bending test

International audienceno abstrac

Caractérisation d’un modèle d’indentation permanente pour impact basse vitesse à l’aide d’un essai de flexion 3 points

International audienceno abstrac

Method to apply and visualize physical models associated to a land cover performed by CNN: A case study of vegetation and water cooling effect in Bangkok Thailand

International audienceConvolutional Neural Networks (CNNs) are useful tools to perform land cover analysis, in particular when working on large areas. The information extracted from these land cover analyses is useful in many practical applications and can be used as input data for physical models capable of simulating any type of phenomenon of interest. Models results can be used to support policy making and can be visualized on the associated remote sensing image to aid interpretation. A method covering these aspects and illustrated on a practical use case is proposed in this paper. The use case focuses on the cooling effect of vegetation and water in Bangkok. For this use case, a dedicated dataset of approximately 680,000 64 × 64 pixels tiles with a resolution of 0.15 m/pixel is created. Three CNN models are built and optimized to classify each of the following classes: city, vegetation and water. The validation of the models shows that, with the exception of water where the accuracy is only 84%, the other two classes have an accuracy of over 90%. Post-processing is performed on each prediction before aggregating these results to obtain the land cover. Vegetation and water cooling models, given in the literature, are successfully applied using the land cover and their effects are plotted on the associated satellite images. Results show that small areas of trees close to water have little influence on the cooling effect and that it is not efficient to plant isolated trees near a larger vegetation site. Through this study, the proposed tool has demonstrated its usefulness

Land cover classification through Convolutional Neur-al Network model assembly: A case study of a local rural area in Thailand

International audienceRecent Convolutional Neural Network (CNN) has shown great potential in image classification, segmentation and object detection. Land cover takes advantage of CNN development for a large type of applications as water management and urban growing. However, to perform a land cover with numerous features - classes in classical CNN terminology, CNN models require a significant number of layers and neurons, resulting in high computational costs. To address this problem, a methodology is proposed in this paper to build a land cover using the aggregation of several CNN models. The overall process is based on 7 steps. The first two steps are the dataset creation and arrangement in smaller dataset fit for the specific features to detect. Then, a CNN architecture is built and validated on each sub-dataset corresponding to each class. Post-processing is conducted on each prediction before assembling the results. In the last step, a data cleaning is performed, giving the final land cover. The land cover of a rural area in Thailand is performed as a demonstration of the method, using satellite images with a resolution of 0.15 m/pixel. A 5-class (buildings, crops, forests, roads, and wastelands) dataset is created, consisting of a total of 1 million tiles of 64 x 64 pixels. The prediction results using the developed CNN model show an accuracy greater than 90% for each class, except for the road class where the accuracy only reaches 72%. Post-processing is performed on each of the 5 predictions. Only the 4 best results are retained and assembled to obtain the land cover, which generally corresponds to buildings, crops, forests, and wastelands. This method enables to identify by substitution with improved accuracy the last class whose prediction is the least accurate, and which generally corresponds to roads due to their small width relative to the tile size. The proposed methodology to perform a land cover by aggregating the prediction of different CNN models is found to predict correctly the land cover of two areas, especially roads can be classified, demonstrating the usefulness of the approach