TY - GEN
T1 - Creating digital twins of existing bridges through AI-based methods
AU - Mafipour, M. Saeed
AU - Vilgertshofer, Simon
AU - Borrmann, André
N1 - Publisher Copyright:
© 2022 IABSE Symposium Prague, 2022: Challenges for Existing and Oncoming Structures - Report. All rights reserved.
PY - 2022
Y1 - 2022
N2 - Bridges require regular inspection and maintenance during their service life, which is costly and time-consuming. Digital twins (DT), which incorporate a geometric-semantic model of an existing bridge, can support the operation and maintenance process. The process of creating such DT models can be based on Point cloud data (PCD), created via photogrammetry or laser scanning. However, the semantic segmentation of PCD and parametric modeling is a challenging process, which is nonetheless necessary to support DT modeling. This paper aims to propose a segmentation method that is the basis for a parametric modeling approach to enable the semi-automatic geometric modeling of bridges from PCD. To this end, metaheuristic algorithms, fuzzy C-mean clustering, and signal processing algorithms are used. The results of this paper show that the scan to BIM process of bridges can be automated to a large extent and provide a model that meets the industry's demand.
AB - Bridges require regular inspection and maintenance during their service life, which is costly and time-consuming. Digital twins (DT), which incorporate a geometric-semantic model of an existing bridge, can support the operation and maintenance process. The process of creating such DT models can be based on Point cloud data (PCD), created via photogrammetry or laser scanning. However, the semantic segmentation of PCD and parametric modeling is a challenging process, which is nonetheless necessary to support DT modeling. This paper aims to propose a segmentation method that is the basis for a parametric modeling approach to enable the semi-automatic geometric modeling of bridges from PCD. To this end, metaheuristic algorithms, fuzzy C-mean clustering, and signal processing algorithms are used. The results of this paper show that the scan to BIM process of bridges can be automated to a large extent and provide a model that meets the industry's demand.
KW - artificial intelligence
KW - bridge
KW - building information modeling
KW - digital twin
KW - fuzzy C-mean clustering
KW - metaheuristic algorithms
KW - parametric modeling
KW - semantic segmentation
UR - http://www.scopus.com/inward/record.url?scp=85133502465&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85133502465
T3 - IABSE Symposium Prague, 2022: Challenges for Existing and Oncoming Structures - Report
SP - 727
EP - 734
BT - IABSE Symposium Prague, 2022
PB - International Association for Bridge and Structural Engineering (IABSE)
T2 - IABSE Symposium Prague 2022: Challenges for Existing and Oncoming Structures
Y2 - 25 May 2022 through 27 May 2022
ER -