Compressive behavior of steel fiber reinforced recycled aggregate concrete after exposure to elevated temperatures

For sustainability considerations, the use of recycled aggregate in concrete has attracted many interests in the research community. One of the main concerns for using such concrete in buildings is its spalling in fire. This may be alleviated by adding steel fibers to form steel fiber reinforced recycled aggregate concrete (SFRAC). This paper presents an experimental investigation into the compressive properties of SFRAC cylinders after exposure to elevated temperatures, including the compressive strength, Young's modulus (stiffness), stress-strain curve and energy absorption capacity (toughness). The effects of two parameters, namely steel fiber volume content (0%, 0.5%, 1%, 1.5%) and temperature (room temperature, 200 °C, 400 °C and 600 °C) on the compressive mechanical properties of concrete were investigated. The test results show that both compressive strength and stiffness of the concrete are significantly reduced after exposure to high temperatures. The addition of steel fibers is helpful in preventing spalling, and significantly improves the ductility and the cracking behavior of recycled aggregate concrete (RAC) after exposure to high temperatures, which is favorable for the application of RAC in building construction.

Influence of aggregate and curing regime on the mechanical properties of ultra-high performance fibre reinforced concrete (UHPFRC)

A new generation of concrete, Ultra-high performance fibre reinforced concrete (UHPFRC) has been developed for its outstanding mechanical performance and shows a very promising future in construction applications. In this paper, several possibilities are examined for reducing the price of producing UHPFRC and for bringing UHPFRC away from solely precast applications and onto the construction site as an in situ material. Recycled glass cullet and two types of local natural sand were examined as replacement materials for the more expensive silica sand normally used to produce UHPFRC. In addition, curing of UHPFRC cubes and prisms at 20 degrees C and 90 degrees C has been investigated to determine differences in both mechanical and ductility.

New Views on Effect of Recycled Aggregates on Concrete Compressive Strength

This paper presents an in-depth study on the effect that composition and properties of recycled coarse aggregates from previous concrete structures, together with water/cement ratio (w/c) and a replacement ratio of coarse aggregate, have on compressive strength, its evolution through time, and its variability. A rigorous approach through statistical inference based on multiple linear regression has identified the key factors. A predictive equation is given for compressive strength when recycled coarse aggregates are used. The w/c and replacement ratio are the capital factors affecting concrete compressive strength. Their effect is significantly modified by the properties and composition of the recycled aggregates used. An equation that accurately predicts concrete compressive strength in terms of these parameters is presented. Particular attention has been paid to the complex effect that old concrete and adhered mortar have on concrete compressive strength and its mid-term evolution. It has been confirmed that the presence of contaminants tends to increase variability of compressive strength values.

A machine vision approach to the grading of crushed aggregate

The grading of crushed aggregate is carried out usually by sieving. We describe a new image-based approach to the automatic grading of such materials. The operational problem addressed is where the camera is located directly over a conveyor belt. Our approach characterizes the information content of each image, taking into account relative variation in the pixel data, and resolution scale. In feature space, we find very good class separation using a multidimensional linear classifier. The innovation in this work includes (i) introducing an effective image-based approach into this application area, and (ii) our supervised classification using wavelet entropy-based features.