Abstract
![CDATA[Construction waste prediction and quantification contribute to effective waste management. Waste prediction enables information and data utilization at the early stage of the project to minimise waste during subsequent stages. However, waste prediction and estimation have gained less attention among researchers. The purpose of this paper is to review the available papers on waste prediction and evaluate current prediction models. Based on the review, waste prediction models can be divided into macro and micro models. The macro models use project-level data to predict waste at a city or country level, while the micro models use data to predict waste generated during construction and demolition stages. The focus of this paper is on the micro models. Most of the existing studies are theoretical-based and use descriptive analysis of waste data. The common models of waste prediction, which are evaluated in this paper are linear and regression models, S-curve and Artificial Neural Network model, Big Data framework, and designing-out waste. The outcome of this paper provides an insight into main prediction models, limitations of these models, and factors influencing accurate waste predication.]]
Original language | English |
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Title of host publication | Proceedings of the 43rd Australasian Universities Building Education Association (AUBEA) Conference: Built to Thrive: Creating Buildings and Cities that Support Individual Well-being and Community Prosperity, 6-8 November 2019, Noosa, QLD, Australia |
Publisher | Central Queensland University |
Pages | 414-425 |
Number of pages | 12 |
ISBN (Print) | 9781921047510 |
Publication status | Published - 2019 |
Event | Australasian Universities Building Education Association. Conference - Duration: 6 Nov 2019 → … |
Conference
Conference | Australasian Universities Building Education Association. Conference |
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Period | 6/11/19 → … |
Keywords
- construction industry
- waste disposal
- waste minimization