Estimating Annual Maintenance Expenditures for Infrastructure: Artificial Neural Network Approach
Publication: Journal of Infrastructure Systems
Volume 22, Issue 2
Abstract
For the purposes of long-term planning and budgeting, infrastructure user cost allocation, and financial need forecasts, infrastructure agencies seek knowledge of the annual expenditure levels for maintaining their assets. Often, this information is expressed in dollars per unit dimension of the infrastructure and is estimated using observed data from historical records. This paper presents an artificial neural network (ANN) approach for purposes of estimating annual expenditures on infrastructure maintenance and demonstrates the application of the approach using a case study involving rural interstate highway pavements. The results of this exploratory study demonstrate that not only is it feasible to use ANN to derive reliable predictions of annual maintenance expenditures (AMEX) at aggregate level, but also it is possible to identify the influential factors of such expenditures and to quantify the sensitivity of AMEX to such factors.
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Acknowledgments
One of the authors is funded by the Colombian government, under the Department of Science and Technology, and Universidad del Valle, under the fellows program Colciencias, Generación del Bicentenario.
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Information & Authors
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Published In
Journal of Infrastructure Systems
Volume 22 • Issue 2 • June 2016
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Mar 7, 2015
Accepted: Sep 17, 2015
Published online: Dec 22, 2015
Discussion open until: May 22, 2016
Published in print: Jun 1, 2016
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