@phdthesis{,
    author = {Rahimi, Amar},
    title = {Modellunabhängiger Ansatz zur Erstellung von BIM-Modellen von seismisch beschädigten Stahlbetonrahmenkonstruktionen},
    editor = {},
    booktitle = {},
    series = {},
    journal = {},
    address = {},
    publisher = {},
    edition = {},
    year = {2024},
    isbn = {},
    volume = {},
    number =  {},
    pages = {},
    url = {},
    doi = {},
    keywords = {Building Information Modeling; Punktwolke; Automatisierung; Scan-to-BIM; Segmentierung; 3D-Rekonstruktion; Photogrammetrie; Laserscanning; Katastrophenbewältigung},
    abstract = {After the complete or partial collapse of a building due to a severe earthquake, immediate search and rescue efforts are required to locate  survivors. The structural information of the seismically damaged building plays a crucial role in the safe execution of rescue operations. In this context, Building Information Modeling (BIM) technology provides the possibility to manage collected data of the building in a digital 3D model in an object-oriented manner. The use of a BIM model in the event of a disaster offers a promising approach to optimize the planning of rescue operations and thus increase the safety of rescue forces. However, this requires that the BIM model can be generated quickly and user-friendly. In this thesis, an innovative, model-independent approach for the generation of BIM models of seismically damaged reinforced concrete frame structures is introduced. The focus is on the algorithmic processing of 3D point clouds which are used as the sole data basis for model creation. The 3D point cloud, obtained by photogrammetry or laser scanning, represents the damaged state of the building in the form of point data. The developed methodology begins with pre-processing and subsequently employs several segmentation algorithms for shape-based structuring of the point data. These include methods for segmenting planar and linear point sets as well as extracting wall point data from frame segments. Based on the segmented point data, the object and material classification for semantic information enrichment, the 3D reconstruction for digital model generation and the conversion of the generated model data into the BIM process are perfomed. The performance of these processing steps was evaluated using real case studies, including a partially collapsed reinforced concrete frame building in Lyon and seismically damaged reinforced concrete frame structures in Jindires and Gölcük. The results of the present work show that the developed methodology enables the semi-automatic generation of BIM models of seismically damaged reinforced concrete frame structures, achieving a fundamental level of detail based on 3D point clouds. For the case studies, BIM models could be generated within a maximum of 20 minutes using the model-independent approach. This represents a significant advance in the field of Scan-to-BIM for the considered use case in which only model-dependent methods have been taken into account to date. In particular, impressive performance was observed in the segmentation of planar point sets, object classification and conversion to the BIM format. However, the segmentation of wall point data from frame segments posed a challenge for irregular geometries of damaged masonry walls. In addition, the developed material classification method revealed potential for improvement in the material prediction of heavily shaded walls. Other challenges identified in this work resulted largely from the limited data collection in the case studies where only the externally visible surfaces of the buildings were scanned using photogrammetry.},
    note = {},
    
    school = {Universität der Bundeswehr München},
    
}