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Development of a Prediction Model for Demolition Waste Generation Using a Random Forest Algorithm Based on Small

Gi-Wook Cha1, Hyeun Jun Moon1, Young-Min Kim2

  • 1Department of Architectural Engineering, Dankook University, Yongin 16890, Korea.

International Journal of Environmental Research and Public Health
|September 29, 2020
PubMed
Summary

This study shows that the random forest algorithm effectively predicts construction and demolition (C&D) waste generation, even with small, categorical datasets. This AI approach enhances waste management accuracy in C&D facilities.

Keywords:
construction waste managementdemolition waste managementleave-one-out cross-validationprediction modelrandom forestsmall data

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Area of Science:

  • Environmental Science
  • Computer Science
  • Civil Engineering

Background:

  • Artificial intelligence (AI) is increasingly used for predicting construction and demolition (C&D) waste.
  • Existing machine learning models often rely on continuous data, potentially limiting accuracy with categorical C&D waste data.
  • Accurate prediction of C&D waste is crucial for effective waste management in facilities.

Purpose of the Study:

  • To investigate the efficacy of machine learning algorithms in predicting C&D waste generation using datasets with categorical variables.
  • To improve the accuracy of waste management in C&D facilities through enhanced predictive modeling.
  • To evaluate the performance of the random forest (RF) algorithm on small, categorical C&D waste datasets.

Main Methods:

  • Utilized a dataset comprising both categorical (region, building structure, use, materials) and continuous (gloss floor area) variables.
  • Applied the random forest (RF) machine learning algorithm for predicting C&D waste generation.
  • Analyzed predictive performance using Pearson's correlation coefficient (R) and coefficient of determination (R²).

Main Results:

  • The random forest algorithm demonstrated adequate performance for predicting C&D waste generation from small, categorical datasets.
  • Predictive performance for demolition waste (DW) types showed stable results with R values ranging from 0.691 to 0.871 and R² values from 0.554 to 0.800.
  • Optimal prediction accuracy was achieved using specific numbers of input variables: three for mortar, five for other DW types, and six for concrete.

Conclusions:

  • The proposed RF model is capable of accurately predicting demolition waste generation even with limited data.
  • This study highlights the potential of applying AI, specifically the RF algorithm, to multi-purpose demolition waste management.
  • The findings suggest that AI can be a valuable tool for improving waste management strategies in the construction and demolition sectors.