Generating model-based construction details for existing bridges using Informed Machine Learning.

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The use of digital twins offers new possibilities for the operation and maintenance management of civil engineering structures. The prerequisite for this is an as-built model which is rarely available for existing structures. The as-built models should at least correspond to the detailing of the structural design. The focus of the research project is the generation of three-dimensional structural details for bridges and their integration into an existing digital model of the main components. This includes in particular details of reinforcement and tendons. The necessary information about reinforcement and installed tendons cannot be obtained from surface information such as point clouds or images. Conventional execution plans and text documents such as the structural analysis are the only source of information. Currently, no methods exist for automated evaluation of 2D plans and related reports of bridge structures with the purpose of three-dimensional generation of relevant execution details for integration into an as-built model. The generation is based on machine learning approaches and knowledge-based systems (Informed Machine Learning). Knowledge sources, data sets and methods are developed for specific detection and recognition steps. The following scientific sub-goals will be addressed: Methods for the recognition and classification of technical details based on conventional two-dimensional construction or reinforcement plans. Methods for the extraction of semantic information, such as dimensions and labels for individual structural details. Knowledge-based concepts for the matching of extracted structural details and semantic information from the different documents. Methods for extracting and integrating additional information based on conventional text documents. Concepts for generating structural details using parametric techniques and their positioning in the corresponding components.