Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction
This manuscript provides a robust framework for the extraction of common structural components, such as columns, from terrestrial laser scanning point clouds acquired at regular rectangular concrete construction projects. The proposed framework utilizes geometric primitive as well as relationship-ba...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-05-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/11/9/1102 |
| _version_ | 1819109700891836416 |
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| author | Reza Maalek Derek D. Lichti Janaka Y. Ruwanpura |
| author_facet | Reza Maalek Derek D. Lichti Janaka Y. Ruwanpura |
| author_sort | Reza Maalek |
| collection | DOAJ |
| description | This manuscript provides a robust framework for the extraction of common structural components, such as columns, from terrestrial laser scanning point clouds acquired at regular rectangular concrete construction projects. The proposed framework utilizes geometric primitive as well as relationship-based reasoning between objects to semantically label point clouds. The framework then compares the extracted objects to the planned building information model (BIM) to automatically identify the as-built schedule and dimensional discrepancies. A novel method was also developed to remove redundant points of a newly acquired scan to detect changes between consecutive scans independent of the planned BIM. Five sets of point cloud data were acquired from the same construction site at different time intervals to assess the effectiveness of the proposed framework. In all datasets, the framework successfully extracted 132 out of 133 columns and achieved an accuracy of 98.79% for removing redundant surfaces. The framework successfully determined the progress of concrete work at each epoch in both activity and project levels through earned value analysis. It was also shown that the dimensions of 127 out of the 132 columns and all the slabs complied with those in the planned BIM. |
| first_indexed | 2024-12-22T03:30:00Z |
| format | Article |
| id | doaj.art-e8aa93d6ed8a401b8546ca674573ae68 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-22T03:30:00Z |
| publishDate | 2019-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e8aa93d6ed8a401b8546ca674573ae682022-12-21T18:40:32ZengMDPI AGRemote Sensing2072-42922019-05-01119110210.3390/rs11091102rs11091102Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete ConstructionReza Maalek0Derek D. Lichti1Janaka Y. Ruwanpura2Department of Civil Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaDepartment of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaDepartment of Civil Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaThis manuscript provides a robust framework for the extraction of common structural components, such as columns, from terrestrial laser scanning point clouds acquired at regular rectangular concrete construction projects. The proposed framework utilizes geometric primitive as well as relationship-based reasoning between objects to semantically label point clouds. The framework then compares the extracted objects to the planned building information model (BIM) to automatically identify the as-built schedule and dimensional discrepancies. A novel method was also developed to remove redundant points of a newly acquired scan to detect changes between consecutive scans independent of the planned BIM. Five sets of point cloud data were acquired from the same construction site at different time intervals to assess the effectiveness of the proposed framework. In all datasets, the framework successfully extracted 132 out of 133 columns and achieved an accuracy of 98.79% for removing redundant surfaces. The framework successfully determined the progress of concrete work at each epoch in both activity and project levels through earned value analysis. It was also shown that the dimensions of 127 out of the 132 columns and all the slabs complied with those in the planned BIM.https://www.mdpi.com/2072-4292/11/9/1102semantic object classificationpoint cloud segmentationterrestrial laser scanner (TLS)progress monitoringdimensional compliance controlreinforced concrete construction3D surface intersectionchange detectionbuilding information modeling (BIM) |
| spellingShingle | Reza Maalek Derek D. Lichti Janaka Y. Ruwanpura Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction Remote Sensing semantic object classification point cloud segmentation terrestrial laser scanner (TLS) progress monitoring dimensional compliance control reinforced concrete construction 3D surface intersection change detection building information modeling (BIM) |
| title | Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction |
| title_full | Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction |
| title_fullStr | Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction |
| title_full_unstemmed | Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction |
| title_short | Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction |
| title_sort | automatic recognition of common structural elements from point clouds for automated progress monitoring and dimensional quality control in reinforced concrete construction |
| topic | semantic object classification point cloud segmentation terrestrial laser scanner (TLS) progress monitoring dimensional compliance control reinforced concrete construction 3D surface intersection change detection building information modeling (BIM) |
| url | https://www.mdpi.com/2072-4292/11/9/1102 |
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