Repurposing plastic waste: Experimental study and predictive analysis using machine learning in bricks

被引:5
作者
Lamba, Pooja [1 ]
Kaur, Dilraj Preet [1 ]
Raj, Seema [1 ]
Tipu, Rupesh Kumar [2 ]
Sorout, Jyoti [1 ]
Malik, Abdul [3 ]
Khan, Azmat Ali [4 ]
机构
[1] KR Mangalam Univ, Sch Basic & Appl Sci, Gurugram 122103, Haryana, India
[2] KR Mangalam Univ, Sch Engn & Technol, Gurugram 122103, Haryana, India
[3] King Saud Univ, Coll Pharm, Dept Pharmaceut, Riyadh 11451, Saudi Arabia
[4] King Saud Univ, Coll Pharm, Dept Pharmaceut Chem, Pharmaceut Biotechnol Lab, Riyadh 11451, Saudi Arabia
关键词
Solid waste management; Fly ash; Waste plastic; Compressive strength; XRD; Machine learning; Deep neural network; SHAP analysis; ARTIFICIAL NEURAL NETWORKS; FLY-ASH; CLAY;
D O I
10.1016/j.molstruc.2024.139158
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
The construction industry significantly contributes to environmental degradation and resource depletion. This paper investigates the use of waste plastic from post-consumer and post-industrial sources as a sustainable alternative in brick manufacturing. By substituting plastic waste for traditional materials like clay or cement, the ecological footprint of brick production is reduced, and plastic pollution is mitigated by diverting waste from landfills and oceans. This study employs dual approach of experimentation as well as machine learning for assessing the strength behaviour of bricks enhanced with plastic, emphasising compressive strength, durability, thermal insulation, and moisture resistance. Utilization of Chlorinated Polyvinyl Chloride (CPVC) for Waste plastic bricks (WPB) was done by replacing cement and sand in different proportions i.e. 10 %, 20 %, 30 %, 40 %, and 50 % of fly ash bricks The best performance was observed at 30 % replacement of sand with waste CPVC. A Deep Neural Network (DNN) model also predicted compressive strength and water absorption, identifying key material components influencing brick properties through SHAP value analysis. This combined experimental and predictive modelling approach demonstrates the potential of waste plastic in creating lightweight, sustainable building materials for structural applications, contributing to sustainable construction practices.
引用
收藏
页数:21
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