Performance Prediction Methodology of Slow Aging Assets
Asset management of urban infrastructures faces a multitude of challenges that need to be overcome to obtain a reliable measurement of performances. Predicting the performance of slowly aging systems is one of those challenges, which helps the asset manager to investigate specific failure modes and to undertake the appropriate maintenance and rehabilitation interventions to avoid catastrophic failures as well as to optimize the maintenance costs. This article presents a methodology for modeling the deterioration of slowly degrading assets based on an operating history. It consists of extracting degradation profiles by grouping together assets that exhibit similar degradation sequences using an unsupervised classification technique derived from artificial intelligence. The obtained clusters are used to build the performance prediction models. This methodology is applied to a sample of a stormwater drainage culvert dataset.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 356
 Ens, A. (2012). Development of a flexible framework for deterioration modelling in infrastructure asset management. (Doctoral dissertation). Torronto University.
 Tran, H. D. (2007). Investigation of deterioration models for stormwater pipe systems. (Doctoral dissertation). Victoria University.
 Cho, S., May, G., Tourkogiorgis, I., Perez, R., Lazaro, O., de la Maza, B., & Kiritsis, D. (2018). A hybrid machine learning approach for predictive maintenance in smart factories of the future. Paper presented at the IFIP International Conference on Advances in Production Management Systems.
 Ana, E. V., & Bauwens, W. (2010). Modeling the structural deterioration of urban drainage pipes: the state-of-the-art in statistical methods. Urban Water Journal, 7(1), 47-59.
 Baik, H.-S., Jeong, H. S., & Abraham, D. M. (2006). Estimating transition probabilities in Markov chain-based deterioration models for management of wastewater systems. Journal of Water Resources Planning and Management, 132(1), 15-24.
 Wirahadikusumah, R., Abraham, D., & Iseley, T. (2001). Challenging issues in modeling deterioration of combined sewers. Journal of Infrastructure Systems, 7(2), 77-84.
 Hasan, M. (2015). Deterioration prediction of concrete bridge components using artificial intelligence and stochastic methods. (Doctoral dissertation). RMIT University.
 Tran, H. D., Perera, B. J. C., & Ng, A. W. M. (2010). Markov and Neural Network Models for Prediction of Structural Deterioration of Storm-Water Pipe Assets. Journal of Infrastructure Systems, 16(2), 167-171.
 Falamarzi, A., Moridpour, S., Nazem, M., & Cheraghi, S. (2018). Development of a random forests regression model to predict track degradation index: Melbourne case study. Paper presented at the Australian Transport Research Forum, Darwin, Australia.
 Sakoe, H. (1971). Dynamic-programming approach to continuous speech recognition. Paper presented at the 1971 Proc. the International Congress of Acoustics, Budapest.
 Lee Rodgers, J., & Nicewander, W. A. (1988). Thirteen ways to look at the correlation coefficient. The American Statistician, 42(1), 59-66.
 Faloutsos, C., Ranganathan, M., & Manolopoulos, Y. (1994). Fast subsequence matching in time-series databases. Acm Sigmod Record, 23(2), 419-429.
 Kumar, M., Patel, N. R., & Woo, J. (2002). Clustering seasonality patterns in the presence of errors. Paper presented at the Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining.
 Möller-Levet, C. S., Klawonn, F., Cho, K.-H., & Wolkenhauer, O. (2003). Fuzzy clustering of short time-series and unevenly distributed sampling points. Paper presented at the International symposium on intelligent data analysis.
 Aßfalg, J., Kriegel, H.-P., Kröger, P., Kunath, P., Pryakhin, A., & Renz, M. (2006). Similarity search on time series based on threshold queries. Paper presented at the International Conference on Extending Database Technology.
 Aghabozorgi, S., Shirkhorshidi, A. S., & Wah, T. Y. (2015). Time-series clustering–a decade review. Information Systems, 53, 16-38.
 Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis.
 Albalate, A., & Minker, W. (2013). Semi-supervised and unsupervised machine learning: novel strategies: John Wiley & Sons.
 Bergroth, L., Hakonen, H., & Raita, T. (2000). A survey of longest common subsequence algorithms. Paper presented at the Proceedings Seventh International Symposium on String Processing and Information Retrieval. SPIRE 2000.
 Lewinson, E. (2019). Explaining feature importance by example of a random forest. In: Aug.
 Transports Québec: Service de l'exploitation et Direction du soutien aux opérations. (2012, p. 144). Manuel d'inspection des ponceaux.