Damage-augmented digital twins towards the automated inspection of buildings
Current procedures for the rapid inspection of buildings and infrastructure are subjective, time-consuming, and cumbersome to document, necessitating new technologies to automate the process and eliminate these shortcomings. Fortunately, recent developments in imaging devices and artificial intellig...
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Published in | Automation in construction Vol. 150; p. 104842 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
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Elsevier B.V
01.06.2023
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Abstract | Current procedures for the rapid inspection of buildings and infrastructure are subjective, time-consuming, and cumbersome to document, necessitating new technologies to automate the process and eliminate these shortcomings. Fortunately, recent developments in imaging devices and artificial intelligence, such as computer vision, provide the necessary tools for this, though they are not yet integrated into infrastructure applications. In this paper, we propose an end-to-end pipeline that generates damage-augmented digital twins for buildings at LOD3, including geometrical information as well as data pertaining to damage condition and its characterization. Our framework incorporates multiple-view images to (1) create a level of detail model, (2) segment damage information, and (3) characterize damage. The core of the method is the structure from motion, which is used to reconstruct the building scene, and machine-learning models that segment and characterize damage. In contrast to current practices, our method does not require manual intervention, generates lightweight models, and can be applied to a wide range of assets. The results generated with our pipeline represent a significant step towards an automated infrastructure damage assessment. We intend to expand our work in the future to include real-time applications and applications to other types of infrastructure. Codes and data sets are publicly available (https://github.com/eesd-epfl/DADT_buildings and https://doi.org/10.5281/zenodo.7767478).
•A novel framework to generate Damage Augmented Digital Twins (DADT) of buildings.•State-of-the-art solutions for 3D models, crack segmentation and characterization.•Bench-marking data set for evaluating the automated generation of DADT of buildings. |
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AbstractList | Current procedures for the rapid inspection of buildings and infrastructure are subjective, time-consuming, and cumbersome to document, necessitating new technologies to automate the process and eliminate these shortcomings. Fortunately, recent developments in imaging devices and artificial intelligence, such as computer vision, provide the necessary tools for this, though they are not yet integrated into infrastructure applications. In this paper, we propose an end-to-end pipeline that generates damage-augmented digital twins for buildings at LOD3, including geometrical information as well as data pertaining to damage condition and its characterization. Our framework incorporates multiple-view images to (1) create a level of detail model, (2) segment damage information, and (3) characterize damage. The core of the method is the structure from motion, which is used to reconstruct the building scene, and machine-learning models that segment and characterize damage. In contrast to current practices, our method does not require manual intervention, generates lightweight models, and can be applied to a wide range of assets. The results generated with our pipeline represent a significant step towards an automated infrastructure damage assessment. We intend to expand our work in the future to include real-time applications and applications to other types of infrastructure. Codes and data sets are publicly available (https://github.com/eesd-epfl/DADT_buildings and https://doi.org/10.5281/zenodo.7767478).
•A novel framework to generate Damage Augmented Digital Twins (DADT) of buildings.•State-of-the-art solutions for 3D models, crack segmentation and characterization.•Bench-marking data set for evaluating the automated generation of DADT of buildings. |
ArticleNumber | 104842 |
Author | Pantoja-Rosero, B.G. Achanta, R. Beyer, K. |
Author_xml | – sequence: 1 givenname: B.G. orcidid: 0000-0002-3723-3882 surname: Pantoja-Rosero fullname: Pantoja-Rosero, B.G. email: bryan.pantojarosero@epfl.ch organization: Earthquake Engineering and Structural Dynamics Laboratory (EESD), EPFL, 1015 Lausanne, Switzerland – sequence: 2 givenname: R. surname: Achanta fullname: Achanta, R. email: radhakrishna.achanta@epfl.ch organization: Swiss Data Science Center (SDSC), EPFL and ETH Zurich, 1015 Lausanne, Switzerland – sequence: 3 givenname: K. orcidid: 0000-0002-6883-5157 surname: Beyer fullname: Beyer, K. email: katrin.beyer@epfl.ch organization: Earthquake Engineering and Structural Dynamics Laboratory (EESD), EPFL, 1015 Lausanne, Switzerland |
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Keywords | Deep learning 3D building models Structure from motion Digital twins Masonry buildings Post-earthquake damage assessment |
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SubjectTerms | 3D building models Deep learning Digital twins Masonry buildings Post-earthquake damage assessment Structure from motion |
Title | Damage-augmented digital twins towards the automated inspection of buildings |
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