Dynamic Deterioration Models for Sewer Pipe Network
by Syadaruddin Syachrani, (Ph.D. Candidate, School of Civil and Environmental Engineering, Oklahoma State Univ., Stillwater, OK 74078. E-mail: syadaru@okstate.edu), Hyung Seok (David) Jeong, Ph.D., (corresponding author), A.M.ASCE, (Associate Professor, School of Civil and Environmental Engineering, Oklahoma State Univ., Stillwater, OK 74078. E-mail: david.jeong@okstate.edu), and Colin S. Chung, Ph.D., (Principal, Management Consultant, GHD, Inc., 16451 Scientific Way, Irvine, CA 92618. E-mail: colin.chung@ghd.com)
Journal of Pipeline Systems Engineering and Practice, Vol. 2, No. 4, November 2011, pp. 123-131, (doi: http://dx.doi.org/10.1061/(ASCE)PS.1949-1204.0000085)
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| Document type: |
Journal Paper |
| Abstract: |
One of the key components in successfully implementing asset-management programs is to have accurate and reliable deterioration models for the assets because the deterioration models are the core computational basis for predicting and prioritizing future maintenance, rehabilitation, or replacement activities of the assets. Many large and advanced utilities have put extensive efforts into collecting condition assessment data of their assets since the late 1990s. This change has posed new challenges in developing deterioration models. This paper presents a framework for developing dynamic deterioration models that can avoid the uniform treatment of the entire sewer pipe network by using the clustering and filtering process on the basis of location-related attributes and operational conditions. The dynamic deterioration models are dynamic because they are dual models for a single sewer network; one model is for individual prediction and the other for group prediction. The performance and benefits of the dynamic deterioration models are discussed with the conventional deterioration models developed in this study for comparison purposes. More realistic and reliable decisions can be made by using the dynamic deterioration models, which can translate into accountable short-term and long-term funding strategies for sustainable infrastructure asset management. |
| Author Keywords: | 
| Asset management |
| Deterioration model |
| Sewer pipe |
| Cluster analysis |
| Regression model |
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