Context Aware Community Based Systems for Sustainable Waste Management in Morasia, Bihar

Vishnu, K.; Adarsh, Abhinav; Namitha, N. A.; Devadas, K. Sayooj; Nair, Jayashree; Mohan, Renjith

doi:10.1007/978-981-15-1420-3_174

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…An innovative approach to creating composite blocks/bricks for construction using waste plastics and filler materials is gaining traction. Plastic waste can be converted into more value‐added products rather than cause environmental problems by taking such innovative steps 4–7 …”

Section: Introductionmentioning

confidence: 99%

Analysis and prediction of plastic waste composite construction material properties using machine learning techniques

Jain

Bhadauria

Kushwah

2023

Env Prog and Sustain Energy

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The use of plastic‐based composites as building materials may be one method of recycling plastic waste. Plastic pollution has emerged as a global issue since our planet is becoming increasingly engulfed in plastic waste. In order to preserve a long‐term sustainable ecosystem, alternative methods of recycling this plastic trash would be extremely beneficial. In this study, the six attributes, which are ingredients used to make plastic composite construction material, are used to predict five target variables or properties of plastic composite construction material, such as compressive strength, flexural strength, split tensile strength, density, and water absorption. Five machine learning techniques random forest, Decision Tree, Ridge, Lasso, and Linear regression have been used to get the best prediction model for the properties of plastic composite construction material. The correlation method has been used to analyze the relationships among the properties and ingredients of plastic composite construction material. Analytical results for K‐fold cross‐validation show that random forest and decision tree form the best predictive models, having greater prediction accuracy than the rest of the machine learning algorithms. This pioneering work provides a simple and convenient method for predicting various properties of plastic‐based composite construction material and finding the impact of each composition mix on various properties.

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Section: Introductionmentioning

confidence: 99%