The S-curve for forecasting waste generation in construction projects

Lu, Weisheng, Peng, Yi, Chen, Xi, Skitmore, Martin, & Zhang, Xiaoling (2016) The S-curve for forecasting waste generation in construction projects. Waste Management, 56, pp. 23-34.

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Forecasting construction waste generation is the yardstick of any effort by policy-makers, researchers, practitioners and the like to manage construction and demolition (C&D) waste. This paper develops and tests an S-curve model to indicate accumulative waste generation as a project progresses. Using 37,148 disposal records generated from 138 building projects in Hong Kong in four consecutive years from Jan 2011 to June 2015, a wide range of potential S-curve models are examined, and as a result, the formula that best fits the historical data set is found. The S-curve model is then further linked to project characteristics using artificial neural networks (ANNs) so that it can be used to forecast waste generation in future construction projects. It was found that, amongst the S-curve models, cumulative logistic distribution is the best formula to fit the historical data. Meanwhile, contract sum, location, public-private nature, and duration can be used to forecast construction waste generation. The study provides contractors with not only an S-curve model to forecast overall waste generation before a project commences, but also with a detailed baseline to benchmark and manage waste during the course of construction. The major contribution of this paper is to the body of knowledge in the field of construction waste generation forecasting. By examining it with an S-curve model, the study elevates construction waste management to a level equivalent to project cost management where the model has already been readily accepted as a standard tool.

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ID Code: 98697
Item Type: Journal Article
Refereed: Yes
Keywords: Construction waste management, Waste generation quantification, Forecast, S-curve, Curve fitting
DOI: 10.1016/j.wasman.2016.07.039
ISSN: 0956-053X
Divisions: Current > Schools > School of Civil Engineering & Built Environment
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2016 Elsevier Ltd.
Deposited On: 06 Sep 2016 22:47
Last Modified: 13 Sep 2016 19:02

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