Predictive Model Development to Perform Condition Assessment on Pipeline Networks

Behzad Rouhanizadeh, Sharareh Kermanshachi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Sewer networks' performance includes several uncertainties, which increases the corresponding risks of the system. To improve performance of sewer pipeline networks, a useful method is to inspect the operation of the pipes regularly. Since random inspection of pipes is greatly expensive, the pipeline network decision-makers tend to have a predictive model to anticipate the condition of these systems. In this research, a model was developed to predict the performance of pipes, using multiple regression method. This model utilizes historical data as input including the failures, and hydraulic data. The results of this model revealed that the predictive model was more sensitive to the age of the pipeline system as well as the type of pipes. This model assists engineers and decision-makers to identify the pipe segments which are more inclined to failure and helps them to develop a comprehensive risk management system for sewer pipeline systems.

Original languageEnglish (US)
Title of host publicationComputing in Civil Engineering 2019
Subtitle of host publicationSmart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019
EditorsYong K. Cho, Fernanda Leite, Amir Behzadan, Chao Wang
PublisherAmerican Society of Civil Engineers (ASCE)
Pages24-32
Number of pages9
ISBN (Electronic)9780784482445
DOIs
StatePublished - 2019
EventASCE International Conference on Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience, i3CE 2019 - Atlanta, United States
Duration: Jun 17 2019Jun 19 2019

Publication series

NameComputing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019

Conference

ConferenceASCE International Conference on Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience, i3CE 2019
Country/TerritoryUnited States
CityAtlanta
Period6/17/196/19/19

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Civil and Structural Engineering

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