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Article: A fuzzy neural network approach for contractor prequalification

TitleA fuzzy neural network approach for contractor prequalification
Authors
KeywordsContractor prequalification
Fuzzy reasoning
Neural network
Issue Date2001
PublisherRoutledge. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/01446193.asp
Citation
Construction Management And Economics, 2001, v. 19 n. 2, p. 175-188 How to Cite?
AbstractNon-linearity, uncertainty and subjectivity are the three predominant characteristics of contractors prequalification which lead to the process being more of an art than a scientific evaluation. A fuzzy neural network (FNN) model, amalgamating both the fuzzy set and neural network theories, has been developed aiming to improve the objectiveness of contractor prequalification. Through FNN theory, the fuzzy rules as used by the prequalifiers can be identified and the corresponding membership functions can be transformed. Eighty-five cases with detailed decision criteria and rules for prequalifying Hong Kong civil engineering contractors were collected. These cases were used for training (calibrating) and testing the FNN model. The performance of the FNN model was compared with the original results produced by the prequalifiers and those generated by the general feedforward neural network (GFNN, i.e. a crisp neural network) approach. Contractors' ranking orders, the model efficiency (R 2) and the mean absolute percentage error (MAPE) were examined during the testing phase. These results indicate the applicability of the neural network approach for contractor prequalification and the benefits of the FNN model over the GFNN model. The fuzzy neural network is a practical approach for modelling contractor prequalification.
Persistent Identifierhttp://hdl.handle.net/10722/71046
ISSN
2015 SCImago Journal Rankings: 0.967
References

 

DC FieldValueLanguage
dc.contributor.authorLam, KCen_HK
dc.contributor.authorHu, Ten_HK
dc.contributor.authorNg, STen_HK
dc.contributor.authorSkitmore, Men_HK
dc.contributor.authorCheoung, SOen_HK
dc.date.accessioned2010-09-06T06:28:26Z-
dc.date.available2010-09-06T06:28:26Z-
dc.date.issued2001en_HK
dc.identifier.citationConstruction Management And Economics, 2001, v. 19 n. 2, p. 175-188en_HK
dc.identifier.issn0144-6193en_HK
dc.identifier.urihttp://hdl.handle.net/10722/71046-
dc.description.abstractNon-linearity, uncertainty and subjectivity are the three predominant characteristics of contractors prequalification which lead to the process being more of an art than a scientific evaluation. A fuzzy neural network (FNN) model, amalgamating both the fuzzy set and neural network theories, has been developed aiming to improve the objectiveness of contractor prequalification. Through FNN theory, the fuzzy rules as used by the prequalifiers can be identified and the corresponding membership functions can be transformed. Eighty-five cases with detailed decision criteria and rules for prequalifying Hong Kong civil engineering contractors were collected. These cases were used for training (calibrating) and testing the FNN model. The performance of the FNN model was compared with the original results produced by the prequalifiers and those generated by the general feedforward neural network (GFNN, i.e. a crisp neural network) approach. Contractors' ranking orders, the model efficiency (R 2) and the mean absolute percentage error (MAPE) were examined during the testing phase. These results indicate the applicability of the neural network approach for contractor prequalification and the benefits of the FNN model over the GFNN model. The fuzzy neural network is a practical approach for modelling contractor prequalification.en_HK
dc.languageengen_HK
dc.publisherRoutledge. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/01446193.aspen_HK
dc.relation.ispartofConstruction Management and Economicsen_HK
dc.subjectContractor prequalificationen_HK
dc.subjectFuzzy reasoningen_HK
dc.subjectNeural networken_HK
dc.titleA fuzzy neural network approach for contractor prequalificationen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0144-6193&volume=19 &issue=2&spage=175 &epage= 188&date=2001&atitle=A+fuzzy+neural+network+approach+for+contractor+prequalificationen_HK
dc.identifier.emailNg, ST:tstng@hkucc.hku.hken_HK
dc.identifier.authorityNg, ST=rp00158en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/01446190150505108en_HK
dc.identifier.scopuseid_2-s2.0-0035282199en_HK
dc.identifier.hkuros61196en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0035282199&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume19en_HK
dc.identifier.issue2en_HK
dc.identifier.spage175en_HK
dc.identifier.epage188en_HK
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridLam, KC=55106365500en_HK
dc.identifier.scopusauthoridHu, T=15759762900en_HK
dc.identifier.scopusauthoridNg, ST=7403358853en_HK
dc.identifier.scopusauthoridSkitmore, M=7003387239en_HK
dc.identifier.scopusauthoridCheoung, SO=6504450027en_HK

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