Experimental investigation on fresh, hardened and durability characteristics of partially replaced E-waste plastic concrete: A sustainable concept with machine learning approaches
The rapid global expansion of e-waste poses significant environmental and health risks, making it crucial to find sustainable uses and mitigate its harmful effects. The significance of this research is to look into the impact of e-waste as a possible substitute for natural coarse aggregates (NCA) on...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025003044 |
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| Summary: | The rapid global expansion of e-waste poses significant environmental and health risks, making it crucial to find sustainable uses and mitigate its harmful effects. The significance of this research is to look into the impact of e-waste as a possible substitute for natural coarse aggregates (NCA) on the fresh, hardened and durability characteristics of concrete, alongside machine learning (ML) predictive analysis. Four kinds of concrete mixes were made with produced coarse aggregates as a substitute material for NCA, and substitution levels were calculated as 0 %, 10 %, 15 % and 20 % (by mass of NCA). Compressive and splitting tensile tests evaluated the mechanical properties of e-waste concrete, whereas water permeability and electrical resistivity tests assessed durability to determine the optimal e-waste proportion for construction. The compressive and tensile strengths of e-waste concrete were reduced by 13.41%–25.50 % and 11%–19.26 %, respectively, for replacement levels ranging from 10 % to 20 % at 28 days. The specimens, evaluated at 300 °C, exhibited reductions in compressive strength by 15.26%–30.87 % and tensile strength by 10.52%–19.74 % for e-waste replacement levels of 10%–20 %, respectively. With high coefficient correlation (R2) values, the linear regression (LR) model predicted mechanical property outcomes more accurately than the random forest (RF) model. The electrical resistivity test showed better results increased range of 239.06 %–478.82 %. The findings of the water permeability test improved when the quantity of e-waste plastic was increased by 15 %. In terms of all the percentage results, the 15 % replacement produced the best results and produced a sustainable construction material. |
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| ISSN: | 2405-8440 |