@inproceedings{8c31889914d1466ca0c86f45bf937935,
title = "Digital twinning of bridges from point cloud data by deep learning and parametric models",
abstract = "The Digital Twin (DT) of a bridge is a geometric-semantic model that supports and facilitates the operation and maintenance process of the structure. For existing structures, the semantically enriched 3D model of the DT is typically created by processing point cloud data (PCD). Semantic segmentation and parametric modeling are two essential but laborious steps in the digital twinning of bridges. This paper contributes to automating these steps by applying deep learning and metaheuristic algorithms. Semantic features of points are extracted, and a deep learning model is trained. Subsequently, the segmented parts are parametrically modeled by applying a metaheuristic algorithm for model fitting. The presented results show that the DT of bridges can be created with a mean intersection over union (mIoU) of 88.45% and mean accuracy (mAcc) of 95.62% in semantic segmentation, as well as a mean absolute error (MAE) of 4 cm/m in parametric modeling.",
author = "Mafipour, {M. S.} and S. Vilgertshofer and A. Borrmann",
note = "Publisher Copyright: {\textcopyright} 2023 the Author(s).; 14th European Conference on Product and Process Modelling, ECPPM 2022 ; Conference date: 14-09-2022 Through 16-09-2022",
year = "2023",
doi = "10.1201/9781003354222-69",
language = "English",
isbn = "9781032406732",
series = "eWork and eBusiness in Architecture, Engineering and Construction - Proceedings of the 14th European Conference on Product and Process Modelling, ECPPM 2022",
publisher = "CRC Press/Balkema",
pages = "543--550",
editor = "Eilif Hjelseth and Sujan, {Sujesh F.} and Scherer, {Raimar J.}",
booktitle = "eWork and eBusiness in Architecture, Engineering and Construction - Proceedings of the 14th European Conference on Product and Process Modelling, ECPPM 2022",
}