System of Multiple ANNs for Online Planning of Numerous Building Improvements
Publication: Journal of Construction Engineering and Management
Volume 134, Issue 5
Abstract
The aging infrastructure in North America and worldwide mandates large investments in repair and improvement (R&I) activities. For organizations that own many assets, managing a large number of R&I activities is not a simple task and requires accurate estimating and scheduling so that proper budgeting and resource allocation decisions can be made. To support these decisions, this paper introduces a Web-based system that estimates the cost and duration of a user-requested R&I activity and provides alternative schedules based on resource availability. For estimating, the Web-based system hosts 32 artificial neural networks (ANNs), trained on actual historical data, for 32 common R&I activities in building projects. Each ANN incorporates a sensitivity analysis to consider the uncertainty in the input parameters on the estimate, and is linked to a central scheduling algorithm for resource allocation based on a first-come first-serve basis. The automated system helps practitioners in planning numerous R&I requests with least time, cost, and paper work. Details on system development are provided in this paper along with perceived benefits and the opinion of users on its performance.
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Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 134 • Issue 5 • May 2008
Pages: 342 - 351
Copyright
© 2008 ASCE.
History
Received: May 23, 2006
Accepted: Sep 6, 2007
Published online: May 1, 2008
Published in print: May 2008
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