Probabilistic condition assessment of reinforced concrete sanitary sewer pipelines using LiDAR inspection data

Abstract

Frequent inspections of reinforced concrete sanitary sewer pipelines (RCSSPs) are crucial for performing a proper life cycle management strategy; however, due to the large inventory of SSPs, only limited inspection data is typically available. In this work, a data-driven method for condition assessment of RCSSPs is developed in a probabilistic framework, wherein the pipe wall erosion is evaluated using LiDAR inspection data. Results indicate that the deteriorated inner concrete wall geometry of RCSSPs is best characterized by the half-normal probability density function (PDF). The final product of the proposed condition assessment algorithm is a probabilistic estimate of remaining service life using the inspection LiDAR point cloud data (PCD). The effect of different reliability methods are compared with the proposed methodology. The results are validated using available closed-circuit television (CCTV) images, previous research that employs the same inspection data, and Monte Carlo Simulation (MCS) method. The proposed algorithm provides an automated framework that can be utilized with any PCD associated with non-destructive inspections.