Automated Translation of Rebar Information from GPR Data into As-Built BIM: A Deep Learning-Based Approach

Xiang, Zhongming; Ou, Ge; Rashidi, Abbas

doi:10.1061/9780784483893.047

Cited by 3 publications

(2 citation statements)

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“…To develop an as-built BIM, an innovative procedure by using inverse photogrammetry and a deep learning approach has been proposed and illustrated in Figure 3 [11].…”

Section: A As-built Bim Development For Surface Elementsmentioning

confidence: 99%

“…Ground penetrating radar (GPR) provides the potential to accomplish these objectives as it can obtain rebar's spatial information by scanning concrete elements and has the possibility to build the link between rebar and scanned elements. A number of solutions have been suggested to address the first two objectives [10][11][12] and research in this area is still ongoing. Thus, to fill the gap, this paper proposes an innovative procedure to combine the as-built BIM development and rebar identification.…”

Section: Introductionmentioning

confidence: 99%

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An Integrated Framework for BIM Development of Concrete Buildings Containing Both Surface Elements and Rebar

Xiang

Rashidi

2023

IEEE Access

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As-built Building Information Models (BIM) are increasingly used to facilitate the management of all aspects of built infrastructure's life cycle. Existing studies mainly focus on automating as-built BIM development for surface elements but often ignore embedded elements such as rebar due to the inaccessibility with typical sensing devices, such as image-based or time-of-flight-based methods. To tackle the issue, this research innovatively utilizes Ground Penetrating Radar (GPR) and the photogrammetry method together to generate BIMs for in-service buildings considering both surface elements (e.g., column, slab, wall) and rebar. As-built BIM for surface elements is developed at first and rebar is identified by using GPR. A calibration label is designed and attached to elements which are scanned by GPR device, and a series of images are captured from those elements and then used with other images to generate point clouds. Faster RCNN is utilized to recognize labels among all images. Then, an inverse photogrammetry approach is deployed to identify the scanned elements in BIM. By matching the recorded timestamps of GPR data and labeled images, links between the rebar in GPR data and elements in BIMs are successfully developed. Finally, 𝐼𝐹𝐶 is developed to generate as-built BIM models. Six cases studies demonstrate the system provides a highly automated procedure to develop as-built BIM, and rebars could be efficiently localized and projected into corresponding elements in BIM.INDEX TERMS As-built BIM, deep learning, GPR, IFC, inverse photogrammetry, point cloud, rebar placement.

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“…To develop an as-built BIM, an innovative procedure by using inverse photogrammetry and a deep learning approach has been proposed and illustrated in Figure 3 [11].…”

Section: A As-built Bim Development For Surface Elementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%