Abstract
A 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.
Original language | English |
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Pages (from-to) | 675-690 |
Number of pages | 16 |
Journal | Canadian Journal of Civil Engineering |
Volume | 44 |
Issue number | 9 |
DOIs | |
Publication status | Published - 1 Jan 2017 |
Externally published | Yes |
Keywords
- Bayesian belief network
- Deterioration model
- Dynamic Bayesian network
- Monte Carlo simulation
- Multinomial logistic regression
- Sewer pipelines defects
ASJC Scopus subject areas
- Civil and Structural Engineering
- General Environmental Science