Physical, chemical and mineralogical properties of fine recycled aggregates made from concrete waste

This paper assesses the physical, chemical and mineralogical characteristics of fine recycled aggregates obtained from crushed concrete waste, comparing them with two types of natural fine aggregates from different origins. A commercial concrete was jaw crushed, and the effect of different aperture sizes on the particle size distribution of the resulting aggregates was evaluated. The density and water absorption of the recycled aggregates was determined and a model for predicting water absorption over time is proposed. Both natural and recycled aggregates were characterized regarding bulk density and fines content. Recycled aggregates were additionally characterized by XRD, SEM/EDS and DTA/TG of individual size fractions. The results show that natural and recycled fine aggregates have very different characteristics. This should be considered in potential applications, both in terms of the limits for replacing amounts and of the rules and design criteria of the manufactured products. (C) 2015 Elsevier Ltd. All rights reserved.

Rheological behaviour of concrete made with fine recycled concrete aggregates - Influence of the superplasticizer

This paper evaluates the influence of two superplasticizers (SP) on the rheological behaviour of concrete made with fine recycled concrete aggregates (FRCA). Three families of concrete were tested: family CO made without SP, family Cl made with a regular superplasticizer and family C2 made with a high-performance superplasticizer. Five replacement ratios of natural sand by FRCA were tested: 0%, 10%, 30%, 50% and 100%. The coarse aggregates were natural gravels. Three criteria were established to design the concrete mixes' composition: keep the same particle size distribution curves, adjust the water/cement ratio to obtain a similar slump and no pre-saturation of the FRCA. All mixes had the same cement and SP content. The results show that the incorporation of FRCA significantly increased the shrinkage and creep deformation. The FRCA's effect was influenced by the curing age. The reference concrete made with natural sand stabilizes the creep deformation faster than the mixes made with FRCA. The incorporation of superplasticizer increased the shrinkage at early ages and decreased the shrinkage at 91 days of age. The regular superplasticizer did not improve the creep deformation while the high-performance superplasticizer highly improved this property. The incorporation of FRCA jeopardized the SP's effectiveness. This study demonstrated that to use FRCA and superplasticizer for concrete production it is necessary to take into account the different rheological behaviour of these mixes. (C) 2015 Elsevier Ltd. All rights reserved.

Magnetic flux density distribution in the air gap of a ferromagnetic core with superconducting blocks: three-dimensional analysis and experimental NMR results

The design of magnetic cores can be carried out by taking into account the optimization of different parameters in accordance with the application requirements. Considering the specifications of the fast field cycling nuclear magnetic resonance (FFC-NMR) technique, the magnetic flux density distribution, at the sample insertion volume, is one of the core parameters that needs to be evaluated. Recently, it has been shown that the FFC-NMR magnets can be built on the basis of solenoid coils with ferromagnetic cores. Since this type of apparatus requires magnets with high magnetic flux density uniformity, a new type of magnet using a ferromagnetic core, copper coils, and superconducting blocks was designed with improved magnetic flux density distribution. In this paper, the designing aspects of the magnet are described and discussed with emphasis on the improvement of the magnetic flux density homogeneity (Delta B/B-0) in the air gap. The magnetic flux density distribution is analyzed based on 3-D simulations and NMR experimental results.

Feature Discretization with Relevance and Mutual Information Criteria

Feature discretization (FD) techniques often yield adequate and compact representations of the data, suitable for machine learning and pattern recognition problems. These representations usually decrease the training time, yielding higher classification accuracy while allowing for humans to better understand and visualize the data, as compared to the use of the original features. This paper proposes two new FD techniques. The first one is based on the well-known Linde-Buzo-Gray quantization algorithm, coupled with a relevance criterion, being able perform unsupervised, supervised, or semi-supervised discretization. The second technique works in supervised mode, being based on the maximization of the mutual information between each discrete feature and the class label. Our experimental results on standard benchmark datasets show that these techniques scale up to high-dimensional data, attaining in many cases better accuracy than existing unsupervised and supervised FD approaches, while using fewer discretization intervals.