Closure
Article Abstract:
The points discussed by H. Yucel Ersoz have merit and should be considered when implementing an artificial neural network (ANN). The development of ANNs usually generates a huge amount of data because it should represent the system being modeled as accurately as possible. The latter has already been used at a big construction company to estimate the productivity of pipe welding, fitting and handling operations. It showed better performance than currently available methods.
Publication Name: Journal of Construction Engineering and Management
Subject: Science and technology
ISSN: 0733-9364
Year: 1999
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Neural network model for estimation construction productivity
Article Abstract:
J. Portas and S. AbouRizk should be lauded for studying the effects of work conditions on the productivity of construction crews. The neural network they propose for measuring construction productivity is valid and can be applied in practice. It has its disadvantages but is a significant contribution to the field of cost estimating. However, the system can only produce results if the construction company makes the required changes in its management information systems.
Publication Name: Journal of Construction Engineering and Management
Subject: Science and technology
ISSN: 0733-9364
Year: 1999
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Model for construction budget performance - neural network approach
Article Abstract:
D.K. Chua and his associates used the RMS classification error as a measure of how well their neural network predicts actual results. However, they should have specified the relationship between classification error values and the levels of actual performance. They also failed to explain why the input variables were reduced from 27 to eight. They should also have included the variables with the greatest percentage change in the reduced eight-variable model.
Publication Name: Journal of Construction Engineering and Management
Subject: Science and technology
ISSN: 0733-9364
Year: 1999
User Contributions:
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