Automated parsing of design data will increasingly be a prerequisite for efficient data- and analytics-driven management of building portfolios. The high complexity and low rigidity of building information modelling (BIM) model exchange standards such as Industry Foundation Classes result in considerable differences in data quality and impede direct data availability for analytics-based decision support. Mis- or unclassified building elements are a common issue and can lead to tedious manual reworks. At the same time, scan-to-BIM processes still require considerable manual effort to identify subclass element geometry. This work leverages the benefits of a three-dimensional lightweight, geometric algorithm to generate meaningful geometric features autonomously that assist shape classification in erroneous design models and pre-segmented point clouds. Geometric deep learning is introduced in two steps; a discussion about the benefits of graph convolutional networks (GCNs) is given before a set of experiments on BIM element data sets is conducted. Utilising explainable artificial intelligence methods, the GCN performance is made suitable for human-algorithm interaction. Leveraging element geometry solely, the classification reaches a promising average performance of above 83% for the model-healing task with a reduced computation time. The encoded geometric knowledge from the design models is shown to be helpful in showcasing examples of segment classification in point clouds.

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With the ever-growing digitalisation of the built environment, specifying information requirements (IR) is crucial to control the Building Information Modelling (BIM) data. However, the way of encoding these requirements is subject to a wide range of possibilities, making it difficult for the users to choose the most suitable method. The paper presents a comparative analysis of methods to define IR based on document study and expert group discussions. The study covers Data Dictionaries (ISO12006), Information Delivery Manual (IDM), IFC Property templates, Information Delivery Specification (IDS), Level of Information Need, Model View Definition (mvdXML), and Product Data Templates (PDT), as well as other, non-standardised methods such as Linked Data with SHACL. The comparison is based on criteria from the review of use-cases and covers aspects such as value constraints, properties of fields, geometry representation, metadata, expressiveness and dependency. The paper concludes that no single method covers all the discussed aspects, and selection should be made consciously based on a purpose. The results are relevant to information managers to understand the relations and differences between IR methods, suggest standardisation bodies a way forward to harmonise, integrate or differentiate the standards, and provide a framework for evaluating IR methods.

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In current BIM standards and today's BIM practice, drawings and models are used side-by-side. While respecting the de-facto coexistence of models and drawings, the induced information redundancy results in challenges for proper information management. It is thus desirable to establish and maintain deep links between the model elements and the corresponding drawing elements, as this allows for bi-directional navigation across the model/drawing boundaries and enables accessing non-geometrie model information from the drawing. In addition, consistency checking and preservation become more reliable. The paper specifies the requirements and semantics of the linking mechanism and discusses different technical implementations. Major emphasis is placed on the concept of the Information Container as defined by ISO 21597 which allows to represent and exchange linked models in a vendor-neutral format, but alternative mechanisms are also investigated. The paper finishes with presenting a feasibility case study.@en