A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Automated Cleaning of Point Clouds for Highway Retaining Wall Condition Assessment
Continuous condition monitoring and inspection of under-construction highway retaining walls is essential to ensure that construction performance criteria are met. The use of LIDAR systems by the construction industry has been significantly increased in recent years, especially for recording the as-built and as-is conditions of facilities. The high-precision, dense 3-D point clouds generated by 3-D laser scanners can facilitate the process of Asset Condition Assessment (ACA). ACA involves preprocessing the point cloud data, for which point clouds have to be cleaned of any unwanted or occluding objects and noises. As part of this research, the retaining wall point cloud data from an ongoing highway construction project was processed and analyzed. The project uses 3-D laser scanners for regular monitoring of mechanically stabilized earth walls that retain the soil supporting the highway alignment. The temporary steel and wooden brackets that hold formworks on top of the walls along with other construction materials are defined as unwanted objects. The authors have used a non-deterministic algorithm to remove the brackets and noises from the point clouds. Various settings of the algorithm have been analyzed using different sets of data. This paper presents the accuracy and performance of the tested algorithm and its evaluation when comparing the results with manually cleaned point clouds.
Automated Cleaning of Point Clouds for Highway Retaining Wall Condition Assessment
Continuous condition monitoring and inspection of under-construction highway retaining walls is essential to ensure that construction performance criteria are met. The use of LIDAR systems by the construction industry has been significantly increased in recent years, especially for recording the as-built and as-is conditions of facilities. The high-precision, dense 3-D point clouds generated by 3-D laser scanners can facilitate the process of Asset Condition Assessment (ACA). ACA involves preprocessing the point cloud data, for which point clouds have to be cleaned of any unwanted or occluding objects and noises. As part of this research, the retaining wall point cloud data from an ongoing highway construction project was processed and analyzed. The project uses 3-D laser scanners for regular monitoring of mechanically stabilized earth walls that retain the soil supporting the highway alignment. The temporary steel and wooden brackets that hold formworks on top of the walls along with other construction materials are defined as unwanted objects. The authors have used a non-deterministic algorithm to remove the brackets and noises from the point clouds. Various settings of the algorithm have been analyzed using different sets of data. This paper presents the accuracy and performance of the tested algorithm and its evaluation when comparing the results with manually cleaned point clouds.
Automated Cleaning of Point Clouds for Highway Retaining Wall Condition Assessment
Oskouie, Pedram (author) / Becerik-Gerber, Burcin (author) / Soibelman, Lucio (author)
2014 International Conference on Computing in Civil and Building Engineering ; 2014 ; Orlando, Florida, United States
2014-06-17
Conference paper
Electronic Resource
English
Automated Cleaning of Point Clouds for Highway Retaining Wall Condition Assessment
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