A state of the art review on condition assessment models developed for sewer pipelines

Abstract

In order to achieve an efficient and a successful operation and maintenance plan for assets, management personnel should have detailed information on the condition of the assets to make informed strategic decisions and properly plan expenditure of capital investments. Condition assessment models for sewage pipelines can be considered as a helpful tool to achieve such objective and from which a decision regarding the required and appropriate intervention can be made. This paper presents a review for the different physical, Artificial Intelligence and statistical models that have been developed to assess the condition of sewage pipelines over a period from 1998 through 2019. The description of different techniques used in building the condition assessment models, and the data required to construct these models are presented. In addition, the major disadvantages and limitations of using these techniques in developing the models have also been discussed. The conducted literature review indicates that various condition assessment models were capable of precisely forecasting the future condition of sewer pipelines. Most of the developed assessment models have been validated with various identified techniques to ensure the adequacy of the predictions. The main problem in model development arises from data availability and liability as several factors were identified by researchers to impact the deterioration of sewer pipelines. In order to overcome this problem, municipalities must utilize the new emerging technologies to facilitate gathering the required dataset in a complete and precise manner. Also, certain techniques such as evidential reasoning or Bayesian Belief Network can be used due to their capabilities in dealing with missing data. Furthermore, the influence of the factors on the pipe condition were identified by some researchers. Although there were discrepancies in the findings, but the majority concluded that both age and material factors have high influence and pipe slope has low influence. 2020