Enhancing mechanical performance, durability, and environmental impact assessment of green concrete incorporating industrial and agricultural wastes

Scientific Reports

卷:16

Article

Samia Parvez¹  Syed Shakil Afsar¹  Ibadur Rahman¹  Osama Khan²  Zeinebou Yahya³  Aiyeshah Alhodaib³  Brahmdeo Yadav⁴  Ashok Kumar Yadav⁵ 

[1] Department of Physics, College of Science, Qassim University, Buraidah, Al-Qassim, Saudi Arabia

[2] Department of Mechanical Engineering, College of Engineering, King Faisal University, 31982, Al-Ahsa, Saudi Arabia

[3] Department of Civil Engineering, Jamia Millia Islamia, New Delhi, India

[4] Department of Civil Engineering, BIT Sindri, Dhanbad, Jharkhand, India

[5] Symbiosis Institute of Technology Pune, Symbiosis International (Deemed University), Pune, India

关键词: Green concrete;    Multi-criteria decision making;    Supplementary cementitious materials;    K-means clustering;    Silica fume;   

DOI  :  10.1038/s41598-026-57664-2

 received in 2026-03-10, accepted in 2026-06-08,  发布年份 2026

来源: Springer

【 摘 要 】

This study proposes a data-driven experimental decision framework to identify the most suitable sustainable supplementary materials for green concrete, aiming to reduce cement usage, industrial waste burden, and environmental impacts in the construction sector. It experimentally evaluates compressive strength, split tensile strength, flexural strength, and ultrasonic pulse velocity (UPV) of green concrete incorporating waste materials including silica fume, GGBS, metakaolin, granite dust, rice husk ash, ceramic waste, marble powder, coconut shell powder, plastic waste, and bottom ash. A hybrid methodology integrating Pearson correlation, Analytical Hierarchy Process (AHP), and k-means clustering was developed to capture complex interrelationships. Correlation-based dependency analysis was incorporated into AHP to generate objective performance weightages, where compressive strength was ranked highest (37%), followed by flexural strength (25%), UPV (22%), and split tensile strength (16%). K-means clustering then categorized materials into best and worst performance groups. The findings revealed silica fume as the most optimal and balanced material, achieving 48.5 MPa compressive strength, 4.0 MPa split tensile strength, 7.5 MPa flexural strength, and 4400 m/s UPV, indicating superior structural performance and durability potential. ANOVA confirmed strong statistical distinction between clusters (p < 0.0001), validating the robustness of the classification. The main contribution of this work lies in introducing a scalable machine-learning-assisted multi-criteria framework that objectively ranks sustainable cement replacement materials, enabling reliable selection for high-performance green concrete design.

【 授权许可】

CC BY-NC-ND   
© The Author(s) 2026

【 图 表 】

Figure 7. Representative image of 30% confluent satellite cells

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