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

delete2024-12-01
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PRE
AI
P
Pooja Lamba
D
Dilraj Preet Kaur *
S
Seema Raj
R
Rupesh Kumar Tipu
J
Jyoti Sorout
A
Azmat Ali Khan
DOI:10.1016/j.molstruc.2024.139158delete
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摘要

摘要

En 中文
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.
Keyword:
Solid waste management
Fly ash
Waste plastic
Compressive strength
XRD
Machine learning
Deep neural network
SHAP analysis

期刊

Journal of Molecular Structure 封面图
Journal of Molecular Structure
IF:
4.7
论文数:
3.6W
被引数:
6.6W

机构

K
King Saud University
学者数:
3.4W
论文数: 3.8W
被引数: 815
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