4.7 Article

Effect of concrete unit weight on the mechanical properties of bottom ash aggregate concrete

期刊

CONSTRUCTION AND BUILDING MATERIALS
卷 273, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.conbuildmat.2020.121998

关键词

Bottom ash; Lightweight concrete; Concrete unit weight; Workability; Mechanical properties

资金

  1. Korea South-East Power CO. of South Korea

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The depletion of natural aggregates is a pressing issue in many regions, with bottom ash identified as a potential replacement material for concrete. The study focused on evaluating the mechanical properties of concrete with different ratios of bottom ash aggregate, providing valuable data for future structural design models.
The depletion of natural aggregates has already progressed significantly in some regions and countries, including South Korea, and it is critical to identify replacement materials to address this problem. In recent decades, researchers have focused on low-cost industrial residues or solid waste as potential solutions, and this study involves application of bottom ash (BA), a byproduct of coal-fired electrical power plants, as fine and coarse aggregates. The application of BA to the manufacture of concrete is noteworthy in terms of economic as well as ecological aspects for preserving natural resources. In the experiment, a total of 20 concrete containing bottom ash (CCBA) mixtures classified according to W/C and the bottom ash aggregate (BAA) mixing ratios were evaluated for their workability and various mechanical properties. The results of the experiment showed that the factor having the greatest effects on the mechanical properties of concrete was the usage of coarse BAA or lack thereof, and that the rates of decline in mechanical properties due to the replacement of fine BAA were within levels that could be controlled by other design factors, such as W/C. This study provides a comparison between the experimental results and the prediction results of several existing design codes, which will serve as basic data for proposing a reliable, predictive CCBA structural design model. (C) 2020 Elsevier Ltd. All rights reserved.

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