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Several deterioration models have been used to predict the structural condition of sewer pipes, and some have been applied in different cities in the world. However, each one of these models has not been proved simultaneously for case studies with different characteristics (topographic conditions, soil uses, demographic growth, utilities’ service operation and city’s dynamic) and the use of their predictions have not been analyzed to support different management objectives. Therefore, the objective of this work was to assess the prediction results of two models (based on Logistic Regression and Random Forest (RF) methods), which previously have been identified as successful in other experiences, for two different case studies (a city in Colombia and a city in Germany). The prediction assessment was carried out by three analysis techniques (Positive Likelihood Rate (PLR) index, performance curve and deviation analysis). According to the results, we found that: (i) the model based on RF was the one that could be useful as a support tool in the sewer asset management of both case studies; (ii) for the German city, the prediction results could be useful for designing strategic investment plans in order to know the number of pipes that the utility should rehabilitate each year; and (iii) for the Colombian city, the predictions are appropriate to make decisions concerning inspection or rehabilitation plans, since the probability of identifying the sewer’s assets in critical condition (C4) correctly (according to the analysis of the sample of the 10% of sewers with the highest probability to be in this condition) is around 63% and could be 83% if the stakeholders also consider in these plans the misclassification of those pipes in a bad structural con-

Support tools to predict the critical structural condition of uninspected pipes for case studies of Germany and Colombia