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AuthorElmasry, Mohamed
AuthorHawari, Alaa
AuthorZayed, Tarek
Available date2023-05-23T09:39:16Z
Publication Date2017
Publication NameCanadian Journal of Civil Engineering
ResourceScopus
URIhttp://dx.doi.org/10.1139/cjce-2016-0592
URIhttp://hdl.handle.net/10576/43381
AbstractA defect based deterioration model to determine the condition ratings in a probabilistic manner for sewer pipelines is presented in this paper. Bayesian belief network (BBN) is used to develop a static model using probabilities of occurrences, and conditional probabilities from observations of existing sewage network. Time dimension is introduced to the developed BBN model by using logistic regression as temporal links required to construct a dynamic Bayesian belief network (DBN). The accuracy of the model’s prediction is examined using actual data where the mean absolute error and root mean square error for the BBN model resulted in values of 0.67, 1.06, 0.56 and 1.05, 1.60, 0.95 for structural, operational, and overall conditions, respectively. As for the DBN model, values achieved for the year at which a pipeline would reach a certain condition state were close to the actual values from the validation dataset.
SponsorThis publication was made possible by NPRP grant # (NPRP6-357-2-150) from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors. Also the authors would like to thank the public works authority of Qatar (ASHGAL) for their support in the data collection.
Languageen
PublisherCanadian Science Publishing
SubjectBayesian belief network
Deterioration model
Dynamic Bayesian network
Monte Carlo simulation
Multinomial logistic regression
Sewer pipelines defects
TitleDefect based deterioration model for sewer pipelines using bayesian belief networks
TypeArticle
Pagination675-690
Issue Number9
Volume Number44
dc.accessType Abstract Only


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