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Article: Mapping rework causes and effects using artificial neural networks

TitleMapping rework causes and effects using artificial neural networks
Authors
KeywordsConstruction projects
Cost overrun
Productivity
Project performance
Rework
Time overrun
Issue Date2008
PublisherRoutledge. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/09613218.html
Citation
Building Research And Information, 2008, v. 36 n. 5, p. 450-465 How to Cite?
AbstractRework can have adverse effects on the performance and productivity of construction projects. Techniques such as artificial neural networks (ANN) are widely used for prediction and classification problems and thus can be used to map the causes and effects of rework. The traditional back propagation neural network and general regression neural network data from 112 Hong Kong construction projects are used to examine the influence of rework causes on the various project performance indicators such as cost overrun, time overrun, and contractual claims. The results from this research could be used to develop forecasting systems and appropriate intelligent decision support frameworks for enhancing performance in construction projects. Furthermore, analysis of the neural network results indicates that the general regression neural network architecture is better suited for modelling rework causes and their impacts on project performance.
Persistent Identifierhttp://hdl.handle.net/10722/58595
ISSN
2015 Impact Factor: 2.196
2015 SCImago Journal Rankings: 1.433
ISI Accession Number ID
Funding AgencyGrant Number
University of Hong KongHKU URC
10205236
City University of Hong KongCityU
7200097
Hong Kong Research Grants Council7126/ 06E
Funding Information:

This research study was supported by a seed funding from the University of Hong Kong (Grant No. HKU URC No. 10205236) and another grant from the City University of Hong Kong (Grant No. CityU Project No. 7200097). In addition, the support of a grant from the Hong Kong Research Grants Council (Grant No. 7126/ 06E) is acknowledged. The authors are grateful for the valuable knowledge-based contributions from many Hong Kong construction industry practitioners who shared their valuable experiences with the research team. The authors also wish to thank Dr Nitin Muttil for his useful discussions on data mining and evolutionary modelling.

References

 

DC FieldValueLanguage
dc.contributor.authorPalaneeswaran, Een_HK
dc.contributor.authorLove, PEDen_HK
dc.contributor.authorKumaraswamy, MMen_HK
dc.contributor.authorNg, TSTen_HK
dc.date.accessioned2010-05-31T03:33:06Z-
dc.date.available2010-05-31T03:33:06Z-
dc.date.issued2008en_HK
dc.identifier.citationBuilding Research And Information, 2008, v. 36 n. 5, p. 450-465en_HK
dc.identifier.issn0961-3218en_HK
dc.identifier.urihttp://hdl.handle.net/10722/58595-
dc.description.abstractRework can have adverse effects on the performance and productivity of construction projects. Techniques such as artificial neural networks (ANN) are widely used for prediction and classification problems and thus can be used to map the causes and effects of rework. The traditional back propagation neural network and general regression neural network data from 112 Hong Kong construction projects are used to examine the influence of rework causes on the various project performance indicators such as cost overrun, time overrun, and contractual claims. The results from this research could be used to develop forecasting systems and appropriate intelligent decision support frameworks for enhancing performance in construction projects. Furthermore, analysis of the neural network results indicates that the general regression neural network architecture is better suited for modelling rework causes and their impacts on project performance.en_HK
dc.languageengen_HK
dc.publisherRoutledge. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/09613218.htmlen_HK
dc.relation.ispartofBuilding Research and Informationen_HK
dc.subjectConstruction projectsen_HK
dc.subjectCost overrunen_HK
dc.subjectProductivityen_HK
dc.subjectProject performanceen_HK
dc.subjectReworken_HK
dc.subjectTime overrunen_HK
dc.titleMapping rework causes and effects using artificial neural networksen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=ISSN: 0961-3218&volume=36, Issue 5&spage=450&epage=465&date=2008&atitle=Mapping+rework+causes+and+effects+using+artificial+neural+networksen_HK
dc.identifier.emailKumaraswamy, MM:mohan@hkucc.hku.hken_HK
dc.identifier.emailNg, TST:tstng@hkucc.hku.hken_HK
dc.identifier.authorityKumaraswamy, MM=rp00126en_HK
dc.identifier.authorityNg, TST=rp00158en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/09613210802128269en_HK
dc.identifier.scopuseid_2-s2.0-49549113153en_HK
dc.identifier.hkuros151995en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-49549113153&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume36en_HK
dc.identifier.issue5en_HK
dc.identifier.spage450en_HK
dc.identifier.epage465en_HK
dc.identifier.isiWOS:000258452400005-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridPalaneeswaran, E=6603323319en_HK
dc.identifier.scopusauthoridLove, PED=7101960035en_HK
dc.identifier.scopusauthoridKumaraswamy, MM=35566270600en_HK
dc.identifier.scopusauthoridNg, TST=7403358853en_HK
dc.identifier.citeulike3140782-

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