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Effect of cement dosing without additions (CPA) on high-performance and ordinary concrete based glass powder as fine partial cement replacement - a comparative study

Aissat-Arab Bahia, Mehaddene Rachid

Advances in Materials Science

2024

Vol. 24, nr 1(79)

17--31

The main objective of this paper research is a comparative study on the effect of the glass powder (GP) substitute from collected and recycled glass waste, as a fine partial cement replacement on the mechanical performance and durability of high performance concrete (HPC) and ordinary concrete (OC). For this two cement dosing were used of 400 kg/m3 to formulate OC and 450 kg/m3 to formulate HPC, and GP as considered binder like cement and not as fine addition, hence binder represent the sum of cement with GP (L=C+GP) with which will be made our two concretes formulation. Two ratios were used for the Water/Binder (W/B), the first W/B=0.35 for the HPC and the second W/B=0.5 for the OC, this ration is very important to fix the concentration of superplasticizer. A percentage of 10% and 20% substitution of cement CPA without additions noted CEM I 52.5 by the glass powder with fineness of 3600 cm 2/g are used. The evaluation of the compressive strength was followed from 7 to 365 days in order to study the behavior of the GP at different ages affected by the cement dosing and the ratio W/B compared to the reference concrete without GP for the two concretes HPC and OC. At 28 days the strengths of concretes with GP is affected by the replacement of a quantity of cement since the two reference concretes were superior but beyond this age an inverse behavior is noticed such that results obtained at age of 365 days seem to be advantageous in terms of savings in the quantity of cement used by interpreting the compressive strength, and the decrease in quantity of water in the mixtures offers a remarkable difference between the two concretes studied by using 20 % of GP as replacement of cement.

Designing Model Based Classifiers by Emphasizing Soft Targets

El Jelali S., Lyhyaoui A., Figueiras-Vidal A.R.

Fundamenta Informaticae

2009

Vol. 96, nr 4

419-433

When training machine classifiers, to replace hard classification targets by emphasized soft versions of them helps to reduce the negative effects of using standard cost functions as approximations to misclassification rates. This emphasis has the same kind of effect as sample editing methods, that have proved to be effective for improving classifiers performance. In this paper, we explore the effectiveness of using emphasized soft targets with generative models, such as Gaussian MixtureModels (GMM), and Gaussian Processes (GP). The interest of using GMMis that they offer advantages such as an easy interpretation and straightforward possibilities to deal with missing values. With respect to GP, if we use soft targets, we do not need to resort to any complex approximation to get a Gaussian Process classifier and, simultaneously, we can obtain the advantages provided by the use of an emphasis. Simulation results support the usefulness of the proposed approach to get better performance and show a low sensitivity to design parameters selection.