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A formal evaluation of KNN and decision tree algorithms for waste generation prediction in residential projects: a comparative approach

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Abstract

This study aimed to develop prediction models for accurately estimating construction waste generated at various stages of a project, focusing on basic civil engineering materials and to perform comparative analysis for the algorithms used for the prediction. Data from 134 construction sites was collected to create a comprehensive dataset, essential for calculating waste during dynamic construction activities. Two predictive modelling techniques, the decision tree algorithm and K-nearest neighbors (KNN) algorithm, were used to analyse the data. The study also included a comparative analysis of neural networks incorporating factors like gross floor area and material estimation. Performance evaluation of the models utilized root mean square error (RMSE) and mean absolute percent error (MAPE) methods. Results showed that waste generation occurs throughout construction projects and can significantly impact costs. A discernible waste generation pattern was identified at typical project stages. This information helps project managers anticipate waste and implement measures to minimize it, reducing costs and promoting sustainability. The developed prediction models demonstrated satisfactory accuracy, with an average RMSE value of 0.49, making them reliable for waste estimation. The decision tree and KNN models showed average accuracies of 88.32% and 88.51% respectively, highlighting their effectiveness in waste prediction. These findings provide insights for waste management and utilization, enabling stakeholders to develop strategies for sustainable construction practices. Anticipating waste facilitates the implementation of effective measures, leading to environmentally friendly projects. This research contributes valuable knowledge to waste management in the construction industry, guiding professionals and fostering a greener future.

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(Source: Gulghane et al., 2023a; b)

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Data availability

The data that support the findings of this study are available from the author, upon reasonable request.

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Acknowledgements

The research work supported by the Lovely Professional University, Punjab, India and G H Raisoni College of Engineering, Maharashtra, India. The authors thankfully acknowledge the funding agencies for the support.

Funding

The research work supported by the Lovely Professional University, Punjab, India and G H Raisoni College of Engineering, Maharashtra, India.

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Authors and Affiliations

  1. School of Civil Engineering, Lovely Professional University, Phagwara, Punjab, 144411, India

    Akshay Gulghane & R. L. Sharma

  2. Department of Civil Engineering, G. H. Raisoni College of Engineering, Nagpur, 440016, India

    Akshay Gulghane

  3. Mastersoft ERP Private Ltd., Nagpur, India

    Prashant Borkar

Authors
  1. Akshay Gulghane
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  2. R. L. Sharma
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  3. Prashant Borkar
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Contributions

AG: conceptualization, methodology, validation, formal analysis, investigation, resources, data curation, writing—original draft, writing—review and editing, visualization. RLS: conceptualization, writing—review and editing, supervision. PB: writing—review and editing, supervision.

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Correspondence to Akshay Gulghane.

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Gulghane, A., Sharma, R.L. & Borkar, P. A formal evaluation of KNN and decision tree algorithms for waste generation prediction in residential projects: a comparative approach. Asian J Civ Eng 25, 265–280 (2024). https://doi.org/10.1007/s42107-023-00772-5

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  • DOI: https://doi.org/10.1007/s42107-023-00772-5

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