Predicting Structural Deterioration Condition of Individual Storm-Water Pipes Using Probabilistic Neural Networks and Multiple Logistic Regression Models
Publication: Journal of Water Resources Planning and Management
Volume 135, Issue 6
Abstract
After several decades in service, the deterioration of storm-water pipe assets is inevitable. The deterioration of storm-water pipes is characterized by structural deterioration and hydraulic deterioration. Condition assessment using closed circuit television (CCTV) inspection is often carried out to assess the structural condition of pipes. However, the knowledge on the condition of storm-water pipe assets is still limited for strategic planning of maintenance and rehabilitation, because generally only a small sample is CCTV-inspected and in almost all cases, these pipes are inspected once only due to high costs. The challenge for researchers is to use the sample of CCTV-inspected pipes for developing mathematical models that can predict the structural condition of remaining pipes as well as the future condition of pipes. In this present study, the deterioration pattern of storm-water pipes is constructed on the basis that each pipe has its own deterioration rate due to its pipe factors. Based on this, two mathematical models using multiple logistic regression (MLR) and probabilistic neural networks (PNN) are developed for predicting the structural condition of individual pipes. The MLR model was calibrated using the maximum likelihood method and the PNN model was trained using a genetic algorithm (GA). The predictive performances of both models were compared using CCTV data collected for a local government authority in Melbourne, Australia. The results showed that the PNN model was more suited for modeling the structural deterioration of individual storm-water pipes than the MLR model. Furthermore, the use of GA improved the training results of the PNN model compared to the trial and error method.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The writers thank the UNSPECIFIEDCity of Greater Dandenong for their support in this study. The writers also thank the reviewers for their constructive comments, which have improved the quality of the paper.
References
Ariaratnam, T. S., Assaly, E. A., and Yuqing, Y. (2001). “Assessment of infrastructure inspection needs using logistic models.” J. Infrastruct. Syst., 7(4), 160–165.
Attalla, M., and Hegazy, T. (2003). “Predicting Cost deviation in reconstruction projects: Artificial neural networks versus regression.” J. Constr. Eng. Manage., 129(4), 405–411.
Baik, H. S., Jeong, H. S., and Abraham, D. M. (2006). “Estimating transition probabilities in Markov chain-based deterioration models for management of wastewater systems.” J. Water Resour. Plann. Manage., 132(1), 15–24.
Davies, J. P., Clarke, B. A., and Whiter, J. T. (2001). “The structural condition of rigid sewer pipes: A statistical investigation.” Urban Water, 3, 277–286.
Engineers Australia. (2001). Australia infrastructure report card, Engineers Australia, Australia, ⟨www.infrastructurereportcard.org.au⟩.
Engineers Australia. (2005). Australia infrastructure report card, Engineers Australia, Australia, ⟨www.infrastructurereportcard.org.au⟩.
Flintsch, G. W., and Chen, C. (2004). “Soft computing applications in infrastructure management.” J. Infrastruct. Syst., 10(4), 157–166.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning, Addison-Wesley, Reading, Mass.
Hajmeer, M., and Basheer, I. (2003). “Comparison of logistic regression and neural network-based classifiers for bacterial growth.” Food Microbiol., 20(1), 43–55.
Johnson, R. A., and Wichern, D. W. (2002). Applied multivariate analysis, Prentice-Hall, Upper Saddle River, N.J.
Johnson, V. E., and Albert, J. H. (1999). Ordinal data modeling, Springer, Berlin.
Kim, D. K., Lee, J. J., Lee, J. H., and Chang, S. K. (2005). “Application of probabilistic neural networks for prediction of concrete strength.” J. Mater. Civ. Eng., 17(3), 353–362.
Koo, D. -H., and Ariaratnam, S. T. (2006). “Innovative method for assessment of underground sewer pipe condition.” Autom. Construct., 15, 479–488.
Leung, P., and Tran, L. T. (2000). “Predicting shrimp disease occurrence: Artificial neural networks vs. logistic regression.” Aquaculture, 187(1–2), 35–49.
Li, Z., Chau, C. K., and Zhou, X. (2005). “Accelerated assessment and fuzzy evaluation of concrete durability.” J. Mater. Civ. Eng., 17(3), 257–263.
McManus, K. J., Lopes, D., and Osman, Y. N. (2004). “The effect of thornthwaite moisture index changes on ground movement predictions in Australian soils.” Proc., 9th Australia New Zealand Conf. on Geomechanics, New Zealand Geotechnical Society, Auckland, New Zealand, 675–680.
Micevski, T., Kuczera, G., and Coombes, P. (2002). “Markov model for storm water pipe deterioration.” J. Infrastruct. Syst., 8(2), 49–56.
Morcous, G., Rivard, H., and Hanna, M. A. (2002). “Case-based reasoning system for modeling infrastructure deterioration.” J. Comput. Civ. Eng., 16(2), 104–114.
Ng, A. W. M., and Perera, B. J. C. (2003). “Selection of genetic algorithm operators for river water quality model calibration.” Eng. Applic. Artif. Intell., 16(5–6), 529–541.
Tran, D. H., Ng, A. W. M., Perera, B. J. C., Burn, S., and Davis, P. (2006). “Application of probabilistic neural networks in modeling structural deterioration of storm-water pipes.” Urban Water, 3(3), 175–184.
Wirahadikusumah, R., Abraham, M. D., and Iseley, T. (2001). “Challenging issues in modeling deterioration of combined sewers.” J. Infrastruct. Syst., 7(2), 77–84.
Information & Authors
Information
Published In
Copyright
© 2009 ASCE.
History
Received: May 6, 2008
Accepted: Apr 21, 2009
Published online: Oct 15, 2009
Published in print: Nov 2009
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.