Abstract
BackgroundThe concept of building information management (BIM) is based on its holistic nature. This idea pays off, if all relevant information is fused into one consistent data set. As a consequence, the completeness of data is vital and the research question on how to complete data automatically remains open.MethodsIn this article we present a data completion technique based on knowledge management. We encode expert and domain knowledge in a generative system that represents norms and standards in a machine-readable manner. The implementation of this approach be used to automatically determine a hypothesis on the location of electrical lines within indoor range scans.ResultsThe generative paradigm can encode domain expert knowledge in a machine-readable way. In this article we demonstrate its usage to represent norms and standards.ConclusionsThe benefit of our method is the further completion of digital building information models – a necessary step to take full advantage of building information modeling.
Highlights
The concept of building information management (BIM) is based on its holistic nature
Studies conducted on the productivity of the construction industries show that the industry could improve efficiency using a standardized workflow across its stakeholders, in the field of electrical construction companies
This study shows a ranking of BIM features used, e.g. clash detections, visualization of electrical design or space utilization
Summary
The proposed approach is divided into four main concepts: data acquisition, data preprocessing, the detection of visible endpoints of electrical installations (sockets and switches), and eventually the hypothesis of installation zones and a possible wiring inside the walls. The starting production rule, which is generated from the scanned data, produces nonterminal symbols according to the elements that influence installation zone placement (walls, doors, windows, detections). Users can manually add points that should be contained in the routing hypothesis, e.g. if there are known positions of wirings that could not be detected by the automatic endpoint detection This grammar is context free, i.e. there is only exactly one nonterminal on the left hand side of a rule, but it turned out that this approach was not sufficient in cases where there is strong evidence for an installation area that is not given solely by the rules of the technical standard, but when the detected endpoints contain large horizontal or vertical structures, e.g. an array of sockets. Our current solution implements a local search algorithm that is similar to “A fast algorithm for steiner trees” from Kou et al (Kou et al 1981); this algorithm subsequently grows the final graph by sorting endpoints by 3D euclidean distance and connecting each endpoint to the graph
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