A neural network-based approach to predict the condition for sewer pipes

Xianfei Yin, Yuan Chen, Ahmed Bouferguene, Mohamed Al-Hussein, Randy Russell, Luke Kurach

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)


Sewer pipe systems are of vital importance in the development of cities, especially in the context of highly populated areas where hygiene is a key factor in avoiding large-scale epidemics. However, the deterioration problem led by age is one of the major issues that need regular monitoring and maintenance to keep the drainage system operational at all time. In order to schedule maintenance effectively, an accurate prediction model of the pipes' condition is required as a reference for prioritization. Given the importance of this assessment, this research proposed a neural network-based approach to automatically predict the condition of sewer pipes. Taking advantage of the availability of both the large volume of data and increasing computational power, a data-driven method is employed in this research. Historical data containing the recorded information of sewer pipes serves as the input of the model, and the output is the condition of the sewer pipes. The final neural network model could serve as a reference for the purpose of efficient scheduling of sewer pipe maintenance work by assigning different priorities to sewer pipes.

Original languageEnglish
Title of host publicationConstruction Research Congress 2020
Subtitle of host publicationInfrastructure Systems and Sustainability - Selected Papers from the Construction Research Congress 2020
EditorsMounir El Asmar, Pingbo Tang, David Grau
PublisherAmerican Society of Civil Engineers
Number of pages9
ISBN (Electronic)9780784482858
Publication statusPublished - 2020
Externally publishedYes
EventConstruction Research Congress 2020: Infrastructure Systems and Sustainability - Tempe, United States
Duration: 8 Mar 202010 Mar 2020


ConferenceConstruction Research Congress 2020: Infrastructure Systems and Sustainability
Country/TerritoryUnited States


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