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Article: Benchmarking construction waste management performance using big data

TitleBenchmarking construction waste management performance using big data
Authors
KeywordsConstruction waste management (CWM)
Key performance indicator (KPI)
Waste generation rate (WGR)
Benchmarking
Big data
Data mining
Hong Kong
Issue Date2015
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/resconrec
Citation
Resources, Conservation and Recycling, 2015, v. 105 n. pt. A, p. 49-58 How to Cite?
AbstractThe waste generation rate (WGR) is usually used as a key performance indicator (KPI) to benchmark construction waste management (CWM) performance, with a view to improving the performance con- tinuously. However, existing researches, for different reasons, only investigated a relatively small amount of construction projects, whose WGRs cannot be confidently accepted as KPIs. This study develops a set of more reliable KPIs/WGRs using an available big dataset on CWM in Hong Kong. By mining the 2,212,026 waste disposal records generated from 5764 projects in two consecutive years of 2011 and 2012, the WGRs/KPIs are revisited and refined. Demolition is found the most wasteful works. New building, and maintenance and renovation (M&R) works individually produce the least waste amount but by accumu- lating all M&R works, their contribution to the total amount of construction waste could be phenomenal. Based on the more reliable WGRs from the big data, CWM performance benchmarks for different cate- gories of projects are set up. A contractor can benchmark its CWM performance against its counterparts or its past performance as ‘Good’, ‘Average’, and ‘Not-so-good’, and thus identify better CWM practices that induce superior performance. Based on the benchmarks, the government may consider setting up a WGR-step toll system to encourage those ‘Not-so-good’ contractors to perform well in the future, and initiate incentives to the companies conducting ‘Good’ projects to spur better CWM performance. Overall, the WGRs derived from the big data and more robust analyses provide a very powerful and handy tool for CWM.
Persistent Identifierhttp://hdl.handle.net/10722/223894
ISSN
2015 Impact Factor: 3.28
2015 SCImago Journal Rankings: 1.360
ISI Accession Number ID
Grants

 

DC FieldValueLanguage
dc.contributor.authorLu, W-
dc.contributor.authorChen, X-
dc.contributor.authorPeng, Y-
dc.contributor.authorShen, L-
dc.date.accessioned2016-03-18T02:30:40Z-
dc.date.available2016-03-18T02:30:40Z-
dc.date.issued2015-
dc.identifier.citationResources, Conservation and Recycling, 2015, v. 105 n. pt. A, p. 49-58-
dc.identifier.issn0921-3449-
dc.identifier.urihttp://hdl.handle.net/10722/223894-
dc.description.abstractThe waste generation rate (WGR) is usually used as a key performance indicator (KPI) to benchmark construction waste management (CWM) performance, with a view to improving the performance con- tinuously. However, existing researches, for different reasons, only investigated a relatively small amount of construction projects, whose WGRs cannot be confidently accepted as KPIs. This study develops a set of more reliable KPIs/WGRs using an available big dataset on CWM in Hong Kong. By mining the 2,212,026 waste disposal records generated from 5764 projects in two consecutive years of 2011 and 2012, the WGRs/KPIs are revisited and refined. Demolition is found the most wasteful works. New building, and maintenance and renovation (M&R) works individually produce the least waste amount but by accumu- lating all M&R works, their contribution to the total amount of construction waste could be phenomenal. Based on the more reliable WGRs from the big data, CWM performance benchmarks for different cate- gories of projects are set up. A contractor can benchmark its CWM performance against its counterparts or its past performance as ‘Good’, ‘Average’, and ‘Not-so-good’, and thus identify better CWM practices that induce superior performance. Based on the benchmarks, the government may consider setting up a WGR-step toll system to encourage those ‘Not-so-good’ contractors to perform well in the future, and initiate incentives to the companies conducting ‘Good’ projects to spur better CWM performance. Overall, the WGRs derived from the big data and more robust analyses provide a very powerful and handy tool for CWM.-
dc.languageeng-
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/resconrec-
dc.relation.ispartofResources, Conservation and Recycling-
dc.rightsPosting accepted manuscript (postprint): © <year>. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/-
dc.subjectConstruction waste management (CWM)-
dc.subjectKey performance indicator (KPI)-
dc.subjectWaste generation rate (WGR)-
dc.subjectBenchmarking-
dc.subjectBig data-
dc.subjectData mining-
dc.subjectHong Kong-
dc.titleBenchmarking construction waste management performance using big data-
dc.typeArticle-
dc.identifier.emailLu, W: wilsonlu@hku.hk-
dc.identifier.authorityLu, W=rp01362-
dc.identifier.doi10.1016/j.resconrec.2015.10.013-
dc.identifier.hkuros257344-
dc.identifier.volume105-
dc.identifier.issuept. A-
dc.identifier.spage49-
dc.identifier.epage58-
dc.identifier.isiWOS:000367769800006-
dc.relation.projectApplication of Hong Kong construction waste management experience in mainland China: an empirical exploration-

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