1. 2. © Classifying Construction Contractors Using Unsupervised- Learning Neural Networks Elazouni,...
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1. 2. © Classifying Construction Contractors Using Unsupervised- Learning Neural Networks Elazouni, AM ASCE-AMER SOC CIVIL ENGINEERS, JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT-ASCE; pp: 1242-1253; Vol: 132 King Fahd University of Petroleum & Minerals http://www.kfupm.edu.sa Summary actor prequalification involves the screening of contractors by a project own mine their competence to complete the project on time, within budget, and to ted quality standards. The process of prequalification involves a large numbe actors, each being represented by many attributes. A neural network model was ed to aid in the prequalification process by classifying contractors into gro on similarity in performance using the financial ratios of liquidity, activi tability, and leverage. Contractors are represented in this model by patterns dimensional space. Patterns of similar performance tend to form clus cepting regions of low pattern density in between. A neuron with weights is u classifier to set a decision boundary between clusters. The method basically tes the neuron weights to move the decision boundary to a place of low patter ty. Then, the statistical hypothesis testing of the mean difference endent samples was used to validate the classification of the parent class to child classes considering the four ratios separately. The method was rchically to classify a group of 245 contractors into classes of small number ly, the inferred procedure of classification proves that the neural network m ified the four-dimension pattern representing contractors efficiently. References: ATIYA AF, 1990, NEURAL NETWORKS, V3, P707 DUDA R, 1973, PATTER CLASSIFICATIO Copyright: King Fahd University of Petroleum & Minerals; http://www.kfupm.edu.sa
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Transcript of 1. 2. © Classifying Construction Contractors Using Unsupervised- Learning Neural Networks Elazouni,...
1.2.
©
Classifying Construction Contractors Using Unsupervised-
Learning Neural
Networks
Elazouni, AM
ASCE-AMER SOC CIVIL ENGINEERS, JOURNAL OF CONSTRUCTION
ENGINEERING AND MANAGEMENT-ASCE; pp: 1242-1253; Vol: 132
King Fahd University of Petroleum & Minerals
http://www.kfupm.edu.sa
Summary
Contractor prequalification involves the screening of contractors by a project owner to
determine their competence to complete the project on time, within budget, and to
expected quality standards. The process of prequalification involves a large number of
contractors, each being represented by many attributes. A neural network model was
applied to aid in the prequalification process by classifying contractors into groups
based on similarity in performance using the financial ratios of liquidity, activity,
profitability, and leverage. Contractors are represented in this model by patterns in
four-dimensional space. Patterns of similar performance tend to form clusters
intercepting regions of low pattern density in between. A neuron with weights is used
as a classifier to set a decision boundary between clusters. The method basically
iterates the neuron weights to move the decision boundary to a place of low pattern
density. Then, the statistical hypothesis testing of the mean difference of two
independent samples was used to validate the classification of the parent class to the
two child classes considering the four ratios separately. The method was used
hierarchically to classify a group of 245 contractors into classes of small numbers.
Finally, the inferred procedure of classification proves that the neural network model
classified the four-dimension pattern representing contractors efficiently.
References:ATIYA AF, 1990, NEURAL NETWORKS, V3, P707DUDA R, 1973, PATTER CLASSIFICATIO
Copyright: King Fahd University of Petroleum & Minerals;http://www.kfupm.edu.sa
3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.28.29.30.31.
©
EELAZOUNI A, 2006, P 1 INT CONSTR SPEC, P1EVERITT B, 2001, CLUSTER ANALGROSSBERG S, 1976, BIOL CYBERN, V23, P121HATUSH Z, 1996, THESIS U SALFORD SALHATUSH Z, 1997, CONSTRUCTION MANAGEM, V15, P327HERBSMAN Z, 1992, J CONSTR ENG M ASCE, V118, P142HOLT GD, 1994, EUROPEAN J PURCHASIN, V1, P139HOLT GD, 1996, BUILD ENVIRON, V31, P557HOLT GD, 1997, BUILD RES INF, V25, P374HOLT GD, 1998, INT J PROJECT MANAGE, V16, P153JUANG CH, 1987, CIVIL ENG SYSTEMS, V4, P124KOHONEN T, 1984, SELF ORG ASS MEMORYLAM KC, 2000, ENG CONSTR ARCHIT MA, V7, P251LAM KC, 2001, CONSTR MANAGE EC, V19, P175MOORE MJ, 1985, PLANT ENG, V39, P74MOSELHI O, 1990, P INT S BUILD EC CON, P335NGUYEN VU, 1985, J CONSTR ENG M ASCE, V111, P231PALANEESWARAN E, 2005, J COMPUT CIVIL ENG, V19, P69, DOI10.1061/(ASCE)0887-3801(2005)19:(69)PONGPENG J, 2003, CONSTR MANAGE EC, V21, P267RUSSELL JS, 1988, J MANAGEMENT ENG, V4, P148RUSSELL JS, 1990, J COMPUT CIVIL ENG, V4, P77RUSSELL JS, 1990, J CONSTR ENG M ASCE, V116, P157RUSSELL JS, 1992, CONSTRUCTION MANAGEM, V10, P117SONMEZ M, 2001, ENG CONSTRUCTION ARC, V8, P198TAM CM, 1996, ENG CONSTR ARCHIT MA, V3, P187WONG CH, 2004, J CONSTR ENG M ASCE, V130, P691, DOI10.1061/(ASCE)0733-9364(2004)130:5(691)YASAMIS F, 2002, CONSTRUCTION MANAGEM, V20, P211
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Copyright: King Fahd University of Petroleum & Minerals;http://www.kfupm.edu.sa
