Moselhi, O. and Shehab-Eldeen, T. (2000) Classification of Defects in Sewer Pipes Using Neural Networks. Journal of Infrastructure Systems, 6, 97-104.
http://dx.doi.org/10.1061/(ASCE)1076-0342(2000)6:3(97)

has been cited by the following article:

• TITLE: Prediction of Sewer Pipe Main Condition Using the Linear Regression Approach

  AUTHORS: Ali Gedam, Suraj Mangulkar, Bal Gandhi

  KEYWORDS: Sewer Pipe Main, Rehabilitation, Regression, Condition Prediction, Deterioration

  JOURNAL NAME: Journal of Geoscience and Environment Protection, Vol.4 No.5, May 19, 2016

  ABSTRACT: This research presents the condition prediction of sewer pipes using a linear regression approach. The analysis is based on data obtained via Closed Circuit Television (CCTV) inspection over a sewer system. Information such as pipe material and pipe age is collected. The regression approach is developed to evaluate factors which are important and predict the condition using available information. The analysis reveals that the method can be successfully used to predict pipe condition. The specific model obtained can be used to assess the pipes for the given sewer system. For other sewer systems, the method can be directly applied to predict the condition. The results from this research are able to assist municipalities to forecast the condition of sewer pipe mains in an effort to schedule inspection, allocate budget and make decisions.