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Building Information Modeling (BIM) is increasingly used in the constructionindustry, but existing studies often ignore embedded rebars. Ground PenetratingRadar (GPR) provides a potential solution to develop as-built BIM with surfaceelements and rebars. However, automatically translating rebars from GPR intoBIM is challenging since GPR cannot provide any information about the scannedelement. Thus, we propose an approach to link GPR data and BIM according toFaster R-CNN. A label is attached to each element scanned by GPR for capturingthe labeled images, which are used with other images to build a 3D model.Meanwhile, Faster R-CNN is introduced to identify the labels, and theprojection relationship between images and the model is used to localize thescanned elements in the 3D model. Two concrete buildings is selected toevaluate the proposed approach, and the results reveal that our method couldaccurately translate the rebars from GPR data into corresponding elements inBIM with correct distributions.

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