Pipelines 2020
Development of a Fuzzy Inference Performance Rating System for Water Pipelines Using a Comprehensive List of Input Variables
Publication: Pipelines 2020
ABSTRACT
Fuzzy logic model or fuzzy inference system is effective in modeling heuristic expert knowledge which is delivered as inexact statements with uncertainty, ambiguity, and even contradictions. Compared with other statistical or machine learning models, fuzzy logic model has great advantages in the interpretability and transparency as it directly mimics the human thought process. Because the previous fuzzy inference models existing in literature were mainly limited in metallic water pipes, this paper extends the fuzzy logic performance rating system to incorporate not only metallic but also cementitious and plastic pipes. A comprehensive list of variables from various sources are compiled and incorporated. The implementation procedures are discussed in detail, and the model outputs are checked and verified as intuitive.
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REFERENCES
Al-Barqawi, H., and Zayed, T. (2006). “Condition Rating Model for Underground Infrastructure Sustainable Water Mains.” Journal of Performance of Constructed Facilities, 20(2), 126–135.
Al-Barqawi, H., and Zayed, T. (2008). “Infrastructure Management: Integrated AHP/ANN Model to Evaluate Municipal Water Mains’ Performance.” Journal of Infrastructure Systems, 14(4), 305–318.
Angkasuwansiri, T. (2013). “Development of wastewater pipe performance index and performance prediction model.” Virginia Tech.
Angkasuwansiri, T., and Sinha, S. (2014). “Development of a robust wastewater pipe performance index.” Journal of Performance of Constructed Facilities, 29(1), 04014042.
Shi, F., Liu, Y., Liu, Z., and Li, E. (2018). “Prediction of pipe performance with stacking ensemble learning based approaches.” Journal of Intelligent & Fuzzy Systems, 34(6), 3845–3855.
Shi, F., Liu, Z., and Li, E. “Prediction of Pipe Performance with Ensemble Machine Learning Based Approaches.” Proc., 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), IEEE, 408–414.
St Clair, A. M. (2013). “Development of a Novel Performance Index and a Performance Prediction Model for Metallic Drinking Water Pipelines.” Virginia Tech.
St. Clair, A. M., and Sinha, S. K. (2014). “Development of a Fuzzy Inference Performance Index for Ferrous Drinking Water Pipelines.” Journal of Pipeline Systems Engineering and Practice, 5(3), 04014003.
Uslu, B. (2017). “Development of Protocols and Methods for Predicting the Remaining Economic Life of Wastewater Pipe Infrastructure Assets.” Virginia Tech.
Velayutham Kandasamy, V. P., and Sinha, S. K. (2018). “Stormwater Pipe Performance Index Using Fuzzy Inference Method.” Journal of Water Resources Planning and Management, 144(10), 04018062.
Xu, H., and Sinha, S. K. (2019). “A Framework for Statistical Analysis of Water Pipeline Field Performance Data>” Pipelines 2019: Multidisciplinary Topics, Utility Engineering, and Surveying, American Society of Civil Engineers Reston, VA, 180–189.
Xu, H., and Sinha, S. K. (2020). “Applying Survival Analysis to Pipeline Data: Gaps and Challenges.” Pipelines 2020, American Society of Civil Engineers Reston, VA.
Information & Authors
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Published In
Pipelines 2020
Pages: 178 - 188
Editors: J. Felipe Pulido, OBG, Part of Ramboll and Mark Poppe, Brown and Caldwell
ISBN (Online): 978-0-7844-8321-3
Copyright
© 2020 American Society of Civil Engineers.
History
Published online: Aug 6, 2020
Published in print: Aug 6, 2020
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