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Sewer deterioration modeling with condition data lacking historical records.

C Egger1, A Scheidegger, P Reichert

  • 1Eawag, Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, P.O. Box 611, CH-8600 Dübendorf, Switzerland; ETH Zürich, Department of Civil, Environmental and Geomatic Engineering, Institute of Environmental Engineering, Wolfgang-Pauli-Strasse 15, CH-8093 Zürich, Switzerland.

Water Research
|October 12, 2013
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Summary
This summary is machine-generated.

This study introduces a novel sewer deterioration model combined with rehabilitation modeling. It enables accurate sewer condition predictions even without historical data, improving rehabilitation planning.

Keywords:
Bayesian inferenceData managementDeterioration modelLikelihoodRehabilitation modelSurvival selection bias

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Area of Science:

  • Civil Engineering
  • Environmental Engineering
  • Asset Management

Background:

  • Effective rehabilitation planning for sewer systems relies on accurate condition prediction models.
  • Existing sewer deterioration models require calibration with observed data, which is challenging when historical records are absent.
  • Lack of historical data leads to overestimation of sewer pipe lifespans because rehabilitated pipes are excluded.

Purpose of the Study:

  • To develop a combined sewer deterioration and rehabilitation model.
  • To enable calibration of sewer models using datasets lacking historical information.
  • To improve the accuracy of sewer system condition predictions for better rehabilitation planning.

Main Methods:

  • Combined a sewer deterioration model with a simple rehabilitation model.
  • Employed Bayesian inference for parameter estimation, suitable for limited data and parameter identifiability.
  • Utilized importance sampling for efficient regional sensitivity analysis.

Main Results:

  • The combined model effectively compensates for bias caused by the absence of historical data.
  • Sensitivity analysis indicated model robustness, highlighting the importance of careful prior elicitation for specific parameters.
  • The approach successfully calibrated sewer pipe deterioration models with limited historical condition records.

Conclusions:

  • This novel approach allows for the calibration of sewer pipe deterioration models even without historical condition records.
  • Bayesian inference is particularly advantageous for small datasets when prior knowledge is available.
  • The method enhances the reliability of sewer system management and rehabilitation strategies.