Forecasting Construction and Demolition Waste Using Gene Expression Programming

Zezhou Wu, Hongqin Fan, Guiwen Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

15 Citations (Scopus)

Abstract

Accurate forecasting of construction and demolition waste (CDW) generation could provide valuable information for the planning, design, and management of CDW at municipal levels. However, the lack of reliable forecasting approaches and historical records makes it difficult to predict the amount of CDW for a long- or short-term plan. To effectively tackle the CDW forecasting problem, a novel computer-based prediction model, gene expression programming (GEP), is introduced and tested. With the CDW and other data on predictor variables from the last two decades, the amount of CDW is forecasted in this study. Results and findings obtained from this research show that GEP is an effective model for predicting waste generation, with lower average forecasting error than the multiple linear model and the artificial neural network. Research issues related to model selection, training, and validation are also discussed in the paper.
Original languageEnglish
Article number4014059
JournalJournal of Computing in Civil Engineering
Volume29
Issue number5
DOIs
Publication statusPublished - 1 Jan 2015

Keywords

  • Construction and demolition waste
  • Forecasting
  • Gene expression programming
  • Time series analysis

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Computer Science Applications

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