Sustainable use of plastic waste in plastic sand paver blocks: An experimental and modelling-based study

被引:3
作者
Iftikhar, Bawar [1 ,2 ]
Alih, Sophia C. [3 ]
Vafaei, Mohammadreza [1 ]
Alkhattabi, Loai [4 ]
Althoey, Fadi [5 ]
Ali, Mujahid [6 ]
Javed, Muhammad Faisal [7 ]
机构
[1] Univ Teknol Malaysia, Sch Civil Engn, Johor Baharu 81310, Johor, Malaysia
[2] COMSATS Univ Islamabad, Dept Civil Engn, Abbottabad Campus, Abbottabad 22060, Pakistan
[3] Univ Teknol Malaysia, Sch Civil Engn, Inst Noise & Vibrat, Johor Baharu 81310, Johor, Malaysia
[4] Univ Jeddah, Coll Engn, Dept Civil & Environm Engn, Jeddah, Saudi Arabia
[5] Najran Univ, Coll Engn, Dept Civil Engn, Najran, Saudi Arabia
[6] Silesian Tech Univ, Fac Transport & Aviat Engn, Dept Transport Syst Traff Engn & Logist, Krasinskiego 8 St, PL-40019 Katowice, Poland
[7] Ghulam Ishaq Khan Inst Engn Sci & Technol, Dept Civil Engn, Swabi 23640, Khyber Pakhtoon, Pakistan
关键词
Plastic sand paver block; Plastic waste; Basalt fibres; Compressive strength; Machine learning; UNCONFINED COMPRESSIVE STRENGTH; HIGH-PERFORMANCE CONCRETE; BASALT FIBER; MECHANICAL-PROPERTIES; CATALYTIC PYROLYSIS; REPLACEMENT; REGRESSION;
D O I
10.1016/j.istruc.2024.106285
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Plastic waste and cement manufacturing are detrimental to the environment, so this study employed plastic sand as paver blocks to promote a more sustainable future. This research prepared two kinds of paver blocks: plasticbased from plastic and sand and fibre-based from plastic, sand, and basalt fibres. An experimental and modellingbased study was employed. In the experiment, the samples were evaluated for compressive strength. The result showed that the 30% to 70% plastic-sand yields a superior effect of 18.06 MPa while 0.5% of basalt fibre showed 22.82 MPa, which enhances compressive strength by about 26.34%. Moreover, microstructural analysis conducted through scanning electron microscopy and energy dispersive X-ray spectroscopy, aimed to deepen insights into the interactions among plastic, sand, and basalt fibers in the composite material. In the modellingbased investigation, supervised machine learning techniques were employed: individual (gene expression programming, decision tree) and ensemble (random forest). The coefficient of determination (R2) results for GEP, DT, and RF were 0.95, 0.98, and 0.98, respectively. The statistical and k-fold cross-validation of the models was also done. It showed that RF performed better than the GEP and DT. Thus, eliminating cement from PSPB and replacing it with plastic waste will help the environment, save money, and reduce carbon dioxide emissions, all contributing to sustainable development. In addition, the construction industry can benefit from algorithms for modelling because they provide more efficient and cost-effective methods for evaluating the quality of materials.
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页数:20
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