Utilizing Neural Networks for Condition Assessment of Sanitary Sewer Infrastructure

This paper will describe the development of an improved methodology for accurately analyzing and interpreting data regarding the condition of sanitary sewer systems. The proposed methodology enables fast and accurate assessment, which is significant in building a sewer condition database for asset management. The inspection system obtains optical data from the Sewer Scanner and Evaluation Technology (SSET). Multiple neural networks are developed to classify the pipe defect features and a fuzzy logic system suggested to filter and fusion the multiple neural network outputs.