  Wastewater infrastructure systems deteriorate over time due to a combination
of physical and chemical factors. Failure of this significant infrastructure
could affect important social, environmental, and economic impacts.
Furthermore, recognizing the optimized timeline for inspection of sewer
pipelines are challenging tasks for the utility managers and other authorities.
Regular examination of sewer networks is not cost-effective due to limited time
and high cost of assessment technologies and a large inventory of pipes. To
avoid such obstacles, various researchers endeavored to improve infrastructure
condition assessment methodologies to maintain sewer pipe systems at the
desired condition. Sewer condition prediction models are developed to provide a
framework to forecast the future condition of pipes to schedule inspection
frequencies. The main goal of this study is to develop a predictive model for
wastewater pipes using random forest classification. Predictive models can
effectively predict sewer pipe condition and can increase the certainty level
of the predictive results and decrease uncertainty in the current condition of
wastewater pipes. The developed random forest classification model has achieved
a stratified test set false negative rate, the false positive rate, and an
excellent area under the ROC curve of 0.81 in a case study application for the
City of LA, California. An area under the ROC curve > 0.80 indicates the
developed model is an "excellent" choice for predicting the condition of
individual pipes in a sewer network. The deterioration models can be used in
the industry to improve the inspection timeline and maintenance planning.
