Influence of gravel and adjuvant on the compressive strength and water absorption of concrete.

Concrete is the most commonly used material in civil engineering, given its economic cost and ease of manufacture. Its strength depends on the characteristics of its constituents. A good mix makes it possible to build solid, durable and economical structures. The present work aims to characterize the gravel of the Eastern region (quarry of eastern Morocco) by granulometric analysis and water absorption. Then, the studied gravel is used to produce three types of concrete (B20, B25 and B30), which were assessed in terms of water absorption and compressive strength. The last step is to study the effect of an adjuvant, more specifically a water reducer, on mechanical characteristics of local concrete. B25 concrete was chosen for the last step since it is the most used type in the region. Results show that adding a water reducer adjuvant, in this case 'Chrysoplast', can improve the compressive strength of concrete if the percentage added is accurately determined

Organizing Gaussian mixture models into a tree for scaling up speaker retrieval

International audienceNumerous pattern recognition tasks set in the probabilistic framework face the following issue : it is expensive to evaluate the likelihood function for test data, when there are given very many candidate probabilistic models for explaining this data.We consider the application of this general and important problem to speaker recognition for indexing and retrieval purposes in radio archives.More precisely, we propose to reduce complexity at query time, by prior organization of speaker models into a hierarchy. This is very classically done for multi-dimensional vectors, but we propose herein a technique for building a hierarchy of probabilistic models, in the case these models take the form of a Gaussian mixture. From a closed-form approximation of Kullback-Leibler divergence between parent and children, an optimality criterion and an optimization technique are derived, from which we propose an efficient approach for building a tree of models, using clustering techniques (dendrogram-based or k-means-like). The proposed scheme is evaluated on real data