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.

为了获得有效而成功的资产运营和维护计划，管理人员应掌握有关资产状况的详细信息，以制定明智的战略决策并适当计划资本投资的支出。污水管道的状态评估模型可以被认为是实现这一目标的有用工具，并可以据此做出所需和适当干预的决策。本文介绍了已开发的用于评估1998年至2019年期间污水管道状况的不同物理，人工智能和统计模型。本文对用于建立状况评估模型的不同技术的描述以及数据介绍了构建这些模型所需的信息。此外，还讨论了在开发模型时使用这些技术的主要缺点和局限性。进行的文献综述表明，各种状态评估模型能够精确预测下水道的未来状况。大多数已开发的评估模型已通过各种确定的技术进行了验证，以确保预测的充分性。模型开发中的主要问题来自数据的可用性和可靠性，因为研究人员确定了影响下水道恶化的几个因素。为了解决这个问题，市政当局必须利用新兴技术来促进以完整而精确的方式收集所需的数据集。也，可以使用诸如证据推理或贝叶斯信念网络之类的某些技术，因为它们具有处理丢失数据的能力。此外，一些研究人员确定了这些因素对管道状况的影响。尽管发现存在差异，但大多数人认为，年龄和材料因素均具有较高的影响力，而管道坡度的影响较小。