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Five recycled coarse aggregate (RCA) treatment techniques including flow-through carbonation, pressurized carbonation, wet carbonation, nano silica (NS) pre-spraying and combined pressurized carbonation with NS pre-spraying, were utilized to improve the performance of recycled aggregate concrete (RAC). The characteristics of the stress–strain curves of RACs including peak stress, peak strain, elastic modulus, ultimate strain and toughness were evaluated after using the above RCA treatment techniques. A theoretical model for natural aggregate concrete was used to analyse the stress–strain curve of RAC. Additionally, the carbonation resistance of RAC after using different RCA treatment techniques were investigated. The results showed that the calculated stress–strain curve of RAC based on the theoretical model matched well with the experimental results. Among the three types of carbonation techniques, pressurized carbonation caused the highest improvement in peak stress and elastic modulus of RAC, followed by flow-through carbonation, the last was wet carbonation. The NS pre-spraying method contributed to even higher improvement in peak stress and elastic modulus of RAC than the pressurized carbonation method. The combined pressurized carbonation with NS pre-spraying exhibited the highest enhancement of RAC because both the RCA and the new interface transition zone (ITZ) were improved. The carbonation resistance of RAC was improved after using all the studied RCA treatment techniques.

Concrete is the most used manufactured material in the world and certainly one of those having the most impact on the environment [...]

Hong Kong is a densely populated city with high-rise developments, and as in other metropolitan cities, the amount of waste generated from construction projects in the city is increasing annually. The capacity of existing landfills is expected to be saturated by the 2020s. Construction waste management has been implemented for years but the performance is still not satisfactory. The aim of this research paper is to explore and formulate strategies and measures for effective construction waste management and reduction in highly urbanized cities such as Hong Kong. A desktop study on construction waste management practices was carried out for a preliminary understanding of the current situation in Hong Kong. Semistructured interviews and focus group meetings were further conducted to shed light on how to improve construction waste reduction and management in Hong Kong. The main contributions of this research study are the potential short-term, medium-term, and long-term strategies, which are related to the design stage, tender stage, construction stage, and government support. The five major strategies recommended are financial benefits to stakeholders, public policies in facilitating waste sorting, government supports for the green building industry, development of a mature recycling market, and education and research in construction waste minimization and management.

It has been proved that artificial neural networks (ANN) can be used to predict the compressive strength and elastic modulus of recycled aggregate concrete (RAC) made with recycled aggregates from different sources. This paper is a further study of the use of ANN to analyze the significance of each aggregate characteristic and determine the best combinations of factors that would affect the compressive strength and elastic modulus of RAC. The experiments were carried out with 46 mixes with several types of recycled aggregates. The experimental results were used to build ANN models for compressive strength and elastic modulus, respectively. Different combinations of factors were selected as input variables until the minimum error was reached. The results show that water absorption has the most important effect on aggregate characteristics, further affecting the compressive strength of RAC, and that combined factors including concrete mixes, curing age, specific gravity, water absorption and impurity content can reduce the prediction error of ANN to 5.43%. Moreover, for elastic modulus, water absorption and specific gravity are the most influential, and the network error with a combination of mixes, curing age, specific gravity and water absorption is only 3.89%.