Effect of various powder content on the properties of sustainable self-compacting concrete

被引:18
|
作者
Khan, Md. Munir Hayet [1 ]
Sobuz, Md. Habibur Rahman [2 ]
Meraz, Md Montaseer [2 ]
Tam, Vivian W. Y. [3 ]
Hasan, Noor Md. Sadiqul [4 ]
Shaurdho, Nur Mohammad Nazmus [4 ]
机构
[1] INTI Int Univ INTI IU, Fac Engn & Quant Surveying, Nilai 71800, Negeri Sembilan, Malaysia
[2] Khulna Univ Engn & Technol, Dept Bldg Engn & Construct Management, Khulna 9203, Bangladesh
[3] Western Sydney Univ, Sch Engn Design & Built Environm, Locked Bag 1797, Penrith, NSW 1797, Australia
[4] Int Univ Business Agr & Technol, Coll Engn & Technol, Dept Civil Engn, Dhaka 1230, Bangladesh
关键词
Supplementary cementitious materials; Sustainable self-compacting concrete; Fresh properties; Mechanical properties; Artificial neural network; GLASS POWDER; LIMESTONE POWDER; CEMENT REPLACEMENT; MECHANICAL-PROPERTIES; PERFORMANCE; MICROSTRUCTURE; DURABILITY; HYDRATION;
D O I
10.1016/j.cscm.2023.e02274
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
This research goal is to evaluate the characteristics of glass powder (GP), quartz powder (QP), and limestone powder (LP) as Supplementary Cementitious Materials (SCMs) to replace cement content in terms of fresh and hardened properties of Self-Compacting Concrete (SCC) for sustainable building construction. Moreover, the obtained results were modeled using a soft computing approach. This investigation created ten mixtures incorporating varying percentages of GP, QP, and LP by replacing cement at about 0 %, 10 %, 20 %, and 30 %, respectively. The slump flow and J-ring tests were done to observe how SCMs affected the properties in fresh condition. In addition, the mechanical properties and pore structure configuration of the specimens were investigated. It was observed that GP and LP positively affected the fresh properties, increasing the mixes flowability by up to 8 %. Moreover, 20 % GP was able to enhance the compressive strength by 7 % by improving the pore structure of the cement matrix, which was confirmed by the mercury intrusion porosimetry analysis. Finally, the built machine learning models indicated good accord with test outcomes for Artificial Neural Network (R2 = 0.95) and could be applied to calculate the compressive strength of concrete containing GP, QP and LP for construction housing sector.
引用
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页数:16
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