Utilization of Aluminosilicate Industrial Wastes as Precursors in CO2-Cured Alkali-Activated Precast Concrete Pavement Blocks

Construction Materials(2024)

引用 0|浏览2
暂无评分
摘要
This research focuses on the utilization of recently investigated aluminosilicate industrial wastes as precursors to produce non-structural precast alkali-activated concrete pavement blocks. For this purpose, conventional blocks (200 mm × 100 mm × 80 mm) were produced using electric arc furnace slag and municipal solid waste incineration bottom ashes as the sole binders. Portland cement and fly ash blocks were produced as references. The blocks underwent a curing regimen comprising thermal, dry, and carbonation curing stages. Control uncarbonated specimens were subjected to dry curing instead of CO2-based curing to evaluate the influence of carbonation on the blocks’ strength development. The specimens were subsequently examined following EN 1338, which is the European standard for assessing and ensuring the conformity of conventional concrete pavement blocks. The carbonated blocks revealed improved mechanical and physical properties in relation to the uncarbonated ones. All blocks met standard dimensions, showed minimal skid potential (most indicating extremely low potential for slip for reporting unpolished slip resistance values exceeding 75), and had enhanced abrasion resistance due to carbonation, showing 30% and 11% less volume loss due to abrasion for fly ash and bottom ash, respectively. Carbonated blocks performed better than non-carbonated ones, displaying lower water absorption (0.58% and 0.23% less water absorption for bottom ash and slag, respectively) and higher thermal conductivity (20%, 13%, and 8% increase in values for fly ash, slag, and bottom ash, respectively). These results confirm the effectiveness of the accelerated carbonation curing technique in improving the block’s performance. Despite the promising outcomes, further optimization of the alkaline solution and carbonation curing conditions is recommended for future research.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要