Building Information Modeling (BIM) representations of bridges enriched by inspection data will add tremendous value to future Bridge Management Systems (BMSs). This paper presents an approach for point cloud-based detection of spalling damage, as well as integrating damage components into a BIM via semantic enrichment of an as-built Industry Foundation Classes (IFC) model. An approach for generating the as-built BIM, geometric reconstruction of detected damage point clusters and semantic-enrichment of the corresponding IFC model is presented. Multiview-classification is used and evaluated for the detection of spalling damage features. The semantic enrichment of as-built IFC models is based on injecting classified and reconstructed damage clusters back into the as-built IFC, thus generating an accurate as-is IFC model compliant to the BMS inspection requirements."
Keywords:
Damage detection / Building Information Modeling / 3D point clouds / Multiview classification / Bridge Management Systems
TY - JOUR
AU - Isailović, Dušan
AU - Stojanovic, Vladeta
AU - Trapp, Matthias
AU - Richter, Rico
AU - Hajdin, Rade
AU - Döllner, Jürgen
PY - 2020
UR - http://grafar.grf.bg.ac.rs/handle/123456789/1857
AB - Building Information Modeling (BIM) representations of bridges enriched by inspection data will add tremendous value to future Bridge Management Systems (BMSs). This paper presents an approach for point cloud-based detection of spalling damage, as well as integrating damage components into a BIM via semantic enrichment of an as-built Industry Foundation Classes (IFC) model. An approach for generating the as-built BIM, geometric reconstruction of detected damage point clusters and semantic-enrichment of the corresponding IFC model is presented. Multiview-classification is used and evaluated for the detection of spalling damage features. The semantic enrichment of as-built IFC models is based on injecting classified and reconstructed damage clusters back into the as-built IFC, thus generating an accurate as-is IFC model compliant to the BMS inspection requirements."
PB - Elsevier
T2 - Automation in Construction
T1 - Bridge damage: Detection, IFC-based semantic enrichment and visualization
VL - 112
DO - 10.1016/j.autcon.2020.103088
ER -
@article{
author = "Isailović, Dušan and Stojanovic, Vladeta and Trapp, Matthias and Richter, Rico and Hajdin, Rade and Döllner, Jürgen",
year = "2020",
url = "http://grafar.grf.bg.ac.rs/handle/123456789/1857",
abstract = "Building Information Modeling (BIM) representations of bridges enriched by inspection data will add tremendous value to future Bridge Management Systems (BMSs). This paper presents an approach for point cloud-based detection of spalling damage, as well as integrating damage components into a BIM via semantic enrichment of an as-built Industry Foundation Classes (IFC) model. An approach for generating the as-built BIM, geometric reconstruction of detected damage point clusters and semantic-enrichment of the corresponding IFC model is presented. Multiview-classification is used and evaluated for the detection of spalling damage features. The semantic enrichment of as-built IFC models is based on injecting classified and reconstructed damage clusters back into the as-built IFC, thus generating an accurate as-is IFC model compliant to the BMS inspection requirements."",
publisher = "Elsevier",
journal = "Automation in Construction",
title = "Bridge damage: Detection, IFC-based semantic enrichment and visualization",
volume = "112",
doi = "10.1016/j.autcon.2020.103088"
}
Isailović, D., Stojanovic, V., Trapp, M., Richter, R., Hajdin, R.,& Döllner, J. (2020). Bridge damage: Detection, IFC-based semantic enrichment and visualization.
Automation in Construction
Elsevier., 112.
https://doi.org/10.1016/j.autcon.2020.103088
Isailović D, Stojanovic V, Trapp M, Richter R, Hajdin R, Döllner J. Bridge damage: Detection, IFC-based semantic enrichment and visualization. Automation in Construction. 2020;112
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