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Article: Mapping rework causes and effects using artificial neural networks
Title | Mapping rework causes and effects using artificial neural networks | ||||||||
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Authors | |||||||||
Keywords | Construction projects Cost overrun Productivity Project performance Rework Time overrun | ||||||||
Issue Date | 2008 | ||||||||
Publisher | Routledge. 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? | ||||||||
Abstract | Rework 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 Identifier | http://hdl.handle.net/10722/58595 | ||||||||
ISSN | 2023 Impact Factor: 3.7 2023 SCImago Journal Rankings: 0.766 | ||||||||
ISI Accession Number ID |
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 Field | Value | Language |
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dc.contributor.author | Palaneeswaran, E | en_HK |
dc.contributor.author | Love, PED | en_HK |
dc.contributor.author | Kumaraswamy, MM | en_HK |
dc.contributor.author | Ng, TST | en_HK |
dc.date.accessioned | 2010-05-31T03:33:06Z | - |
dc.date.available | 2010-05-31T03:33:06Z | - |
dc.date.issued | 2008 | en_HK |
dc.identifier.citation | Building Research And Information, 2008, v. 36 n. 5, p. 450-465 | en_HK |
dc.identifier.issn | 0961-3218 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/58595 | - |
dc.description.abstract | Rework 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.language | eng | en_HK |
dc.publisher | Routledge. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/09613218.html | en_HK |
dc.relation.ispartof | Building Research and Information | en_HK |
dc.subject | Construction projects | en_HK |
dc.subject | Cost overrun | en_HK |
dc.subject | Productivity | en_HK |
dc.subject | Project performance | en_HK |
dc.subject | Rework | en_HK |
dc.subject | Time overrun | en_HK |
dc.title | Mapping rework causes and effects using artificial neural networks | en_HK |
dc.type | Article | en_HK |
dc.identifier.openurl | http://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+networks | en_HK |
dc.identifier.email | Kumaraswamy, MM:mohan@hkucc.hku.hk | en_HK |
dc.identifier.email | Ng, TST:tstng@hkucc.hku.hk | en_HK |
dc.identifier.authority | Kumaraswamy, MM=rp00126 | en_HK |
dc.identifier.authority | Ng, TST=rp00158 | en_HK |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1080/09613210802128269 | en_HK |
dc.identifier.scopus | eid_2-s2.0-49549113153 | en_HK |
dc.identifier.hkuros | 151995 | en_HK |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-49549113153&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 36 | en_HK |
dc.identifier.issue | 5 | en_HK |
dc.identifier.spage | 450 | en_HK |
dc.identifier.epage | 465 | en_HK |
dc.identifier.isi | WOS:000258452400005 | - |
dc.publisher.place | United Kingdom | en_HK |
dc.identifier.scopusauthorid | Palaneeswaran, E=6603323319 | en_HK |
dc.identifier.scopusauthorid | Love, PED=7101960035 | en_HK |
dc.identifier.scopusauthorid | Kumaraswamy, MM=35566270600 | en_HK |
dc.identifier.scopusauthorid | Ng, TST=7403358853 | en_HK |
dc.identifier.citeulike | 3140782 | - |
dc.identifier.issnl | 0961-3218 | - |