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The Effect of Construction and Demolition Waste Plastic Fractions on Wood-Polymer Composite Properties
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The S-curve for forecasting waste generation in construction projects.

Weisheng Lu1, Yi Peng2, Xi Chen1

  • 1Department of Real Estate and Construction, Faculty of Architecture, The University of Hong Kong, Hong Kong.

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|August 4, 2016
PubMed
Summary

Forecasting construction and demolition (C&D) waste is crucial for effective management. This study introduces an S-curve model, enhanced by artificial neural networks (ANNs), to predict C&D waste generation accurately.

Keywords:
Construction waste managementCurve fittingForecastS-curveWaste generation quantification

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Area of Science:

  • Environmental Engineering
  • Construction Management
  • Data Science

Background:

  • Effective management of construction and demolition (C&D) waste is a global challenge.
  • Accurate forecasting of C&D waste generation is essential for policy-making and resource management.
  • Existing methods for waste forecasting lack precision and integration with project lifecycles.

Purpose of the Study:

  • To develop and validate an S-curve model for forecasting cumulative C&D waste generation.
  • To integrate project characteristics with the S-curve model using artificial neural networks (ANNs) for enhanced prediction.
  • To provide a robust tool for contractors to manage C&D waste proactively.

Main Methods:

  • Analysis of 37,148 disposal records from 138 Hong Kong building projects (2011-2015).
  • Examination and selection of various S-curve models, identifying cumulative logistic distribution as the best fit.
  • Application of artificial neural networks (ANNs) to link project characteristics (contract sum, location, public-private nature, duration) with the S-curve model.

Main Results:

  • The cumulative logistic distribution S-curve model demonstrated the best fit for historical C&D waste data.
  • Project characteristics including contract sum, location, public-private nature, and duration were identified as significant predictors of waste generation.
  • The developed model provides a reliable method for forecasting waste generation before project commencement.

Conclusions:

  • The S-curve model, particularly cumulative logistic distribution, offers a superior approach to forecasting C&D waste.
  • Integrating project characteristics via ANNs enhances the predictive power of the S-curve model.
  • This study establishes a new benchmark for C&D waste forecasting, comparable to established project cost management tools.