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

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 cons...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Case Studies in Construction Materials 2023-12, Vol.19, p.e02274, Article e02274
Hauptverfasser: Khan, Md. Munir Hayet, Sobuz, Md. Habibur Rahman, Meraz, Md Montaseer, Tam, Vivian W.Y., Hasan, Noor Md. Sadiqul, Shaurdho, Nur Mohammad Nazmus
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
ISSN:2214-5095
2214-5095
DOI:10.1016/j.cscm.2023.e02274