Parametrisierte Modellierung für den Einsatz von KI am Beispiel Betonbrückenbau.

Parametrization in bridge construction for the application of artificial intelligence For the application of digital methods in the execution phase, the acquisition of exisiting structures or the construction progress with the aid of LiDAR is a suitable method to subsequently detect components as automatically as possible and e. g., to evaluate them for construction progress monitoring. Machine learning methods are suitable for component detection and are used for various classification problems. One problem is the semantic segmentation of point clouds, in which the individual points are assigned to specific component classes. To solve the segmentation with sufficient accuracy and to make predictions on real data, a large amount of training data is needed. This process is also called "Scan to BIM". In this paper, synthetic data for training a convolutionalneural network (CNN) is provided in a highly parameterized and fully automatic way. An approach for generating variations of bridge models based with the visual programming interface Dynamo in Autodesk Revit is proposed. At the beginning, guidelines for the definition of parameters are evaluated to subsequently use them in adaptive templates. Attributes are assigned to each component and grouped into a feature structure when exported to the IFC data format. This approach resulted in numerous variations of bridge models that can be used for semantic segmentation of point clouds and training of a CNN. [ABSTRACT FROM AUTHOR]