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Article: The S-curve for forecasting waste generation in construction projects

TitleThe S-curve for forecasting waste generation in construction projects
Authors
KeywordsConstruction waste management
Waste generation quantification
Forecast
S-curve
Curve fitting
Issue Date2016
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/wasman
Citation
Waste Management, 2016, v. 56, p. 23-34 How to Cite?
AbstractForecasting 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 January 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, among 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. (C) 2016 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/231253
ISSN
2015 Impact Factor: 3.829
2015 SCImago Journal Rankings: 1.764
ISI Accession Number ID
Grants

 

DC FieldValueLanguage
dc.contributor.authorLu, W-
dc.contributor.authorPeng, Y-
dc.contributor.authorChen, X-
dc.contributor.authorSkitmore, M-
dc.contributor.authorZhang, X-
dc.date.accessioned2016-09-20T05:21:48Z-
dc.date.available2016-09-20T05:21:48Z-
dc.date.issued2016-
dc.identifier.citationWaste Management, 2016, v. 56, p. 23-34-
dc.identifier.issn0956-053X-
dc.identifier.urihttp://hdl.handle.net/10722/231253-
dc.description.abstractForecasting 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 January 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, among 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. (C) 2016 Elsevier Ltd. All rights reserved.-
dc.languageeng-
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/wasman-
dc.relation.ispartofWaste Management-
dc.subjectConstruction waste management-
dc.subjectWaste generation quantification-
dc.subjectForecast-
dc.subjectS-curve-
dc.subjectCurve fitting-
dc.titleThe S-curve for forecasting waste generation in construction projects-
dc.typeArticle-
dc.identifier.emailLu, W: wilsonlu@hku.hk-
dc.identifier.authorityLu, W=rp01362-
dc.identifier.doi10.1016/j.wasman.2016.07.039-
dc.identifier.pmid27485070-
dc.identifier.hkuros263615-
dc.identifier.volume56-
dc.identifier.spage23-
dc.identifier.epage34-
dc.identifier.isiWOS:000383827700004-
dc.publisher.placeUnited Kingdom-
dc.relation.projectApplication of Hong Kong construction waste management experience in mainland China: an empirical exploration-

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