STATISTICAL ANALYSIS OF INSPECTION DATA FOR THE ASSET MANAGEMENT OF SEWER NETWORKS

Investments in sewer pipe rehabilitation must be based on optical inspection and evaluation of sewer conditions with respect to the severity of the damage and to environmental risks. The work presented here discusses the problems of forecasting the condition of sewers in a network from a sample of inspected sewers. Transition functions from one into the next poorer condition class, which were empirically derived from this sample, are used to forecast the condition of sewers. By the same procedure, transition functions were subsequently calibrated for sub-samples of different types of sewers. With these transition functions, the most probable date of entering a critical condition class can be forecast. Thus, rehabilitation needs arising from the modelled deterioration process, can be calculated year by year into the future. The requirements of condition grading systems for the application of deterioration models are discussed.