  Risk-based assessment in pipe condition mainly focuses on prioritizing the
most critical assets by evaluating the risk of pipe failure. This paper's goal
is to classify a comprehensive pipe rating model which is obtained based on a
series of pipe physical, external, and hydraulic characteristics that are
identified for the proposed methodology. The traditional manual method of
assessing sewage structural conditions takes a long time. By building an
automated process using K-Nearest Neighbors (K-NN), this study presents an
effective technique to automate the identification of the pipe defect rating
using the pipe repair data. First, we performed the Shapiro Wilks Test for 1240
data from the Dept. of Engineering & Environmental Services, Shreveport,
Louisiana Phase 3 with 12 variables to determine if factors could be
incorporated in the final rating. We then developed a K-Nearest Neighbors model
to classify the final rating from the statistically significant factors
identified in Shapiro Wilks Test. This classification process allows
recognizing the worst condition of wastewater pipes that need to be replaced
immediately. This comprehensive model is built according to the
industry-accepted and used guidelines to estimate the overall condition.
Finally, for validation purposes, the proposed model is applied to a small
portion of a US wastewater collection system in Shreveport, Louisiana.
Keywords: Pipe rating, Shapiro Wilks Test, K-Nearest Neighbors (KNN), Failure,
Risk analysis

Machine Learning