A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Probabilistic condition assessment of reinforced concrete sanitary sewer pipelines using LiDAR inspection data
https://orcid.org/0000-0003-3602-2423
https://orcid.org/0000-0002-0075-9725
https://orcid.org/0000-0002-8952-7575
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.
Graphical abstract Display Omitted
Highlights Automated algorithm proposed for noise filtering of LiDAR point cloud of data. Automatic goodness-of-fit test by comparison of values of the QQ-plots. Automated algorithm for evaluating concrete erosion rate of sanitary sewer pipes. Verified 3-D pipe profile by CCTV feeds. Verification of predicted life span with Monte Carlo Simulation on Pomeroy model.
Probabilistic condition assessment of reinforced concrete sanitary sewer pipelines using LiDAR inspection data
https://orcid.org/0000-0003-3602-2423
https://orcid.org/0000-0002-0075-9725
https://orcid.org/0000-0002-8952-7575
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.
Graphical abstract Display Omitted
Highlights Automated algorithm proposed for noise filtering of LiDAR point cloud of data. Automatic goodness-of-fit test by comparison of values of the QQ-plots. Automated algorithm for evaluating concrete erosion rate of sanitary sewer pipes. Verified 3-D pipe profile by CCTV feeds. Verification of predicted life span with Monte Carlo Simulation on Pomeroy model.
Probabilistic condition assessment of reinforced concrete sanitary sewer pipelines using LiDAR inspection data
https://orcid.org/0000-0003-3602-2423
https://orcid.org/0000-0002-0075-9725
https://orcid.org/0000-0002-8952-7575
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.
Graphical abstract Display Omitted
Highlights Automated algorithm proposed for noise filtering of LiDAR point cloud of data. Automatic goodness-of-fit test by comparison of values of the QQ-plots. Automated algorithm for evaluating concrete erosion rate of sanitary sewer pipes. Verified 3-D pipe profile by CCTV feeds. Verification of predicted life span with Monte Carlo Simulation on Pomeroy model.
Probabilistic condition assessment of reinforced concrete sanitary sewer pipelines using LiDAR inspection data
Ebrahimi, Moein (author) / Hojat Jalali, Himan (author) / Sabatino, Samantha (author)
2023-03-28
Article (Journal)
Electronic Resource
English
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