Graph-based retrieval of building information models for supporting the early design stages

Langenhan, Christoph; Weber, Markus; Liwicki, Marcus; Petzold, Frank; Dengel, Andreas

doi:10.1016/j.aei.2013.04.005

Cited by 65 publications

(25 citation statements)

References 22 publications

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“…In [8], the topology of spatial configurations is extracted from building information models and represented as graphs, which are used for indexing and querying spatial configurations along with architectural sketches, to support early design activities. Analyses such as similarity checking are performed on the static representation of buildings, and are based on adjacency and access relationships.…”

Section: Related Workmentioning

confidence: 99%

Adding static and dynamic semantics to building information models

Tsigkanos

Kehrer

Ghezzi

et al. 2016

Proceedings of the 2nd International Workshop on Software Engineering for Smart Cyber-Physical Systems

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Smart cyber-physical spaces indicate spatial environments which include both cyber and physical elements interacting with each other. In the construction industry, Building Information Models are the de facto standard for specifying complex information about building infrastructures, a representation which can also be extended for the specification of cyber-physical spaces. By providing formal static and dynamic semantics in terms of topological concepts of locality and connectivity of entities it is possible to support many forms of advanced analyses typically performed in software engineering. Static semantics aim to broadly support reasoning about latent qualities of a design. Dynamic semantics aim to deal with the dynamism that a space exhibits when additionally considering the ways it may change along with entities inhabiting it. Motivated by the setting of a smart hospital, we show how both qualitative and quantitative properties can be specified and verified.

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Section: Related Workmentioning

confidence: 99%

Adding static and dynamic semantics to building information models

Tsigkanos

Kehrer

Ghezzi

et al. 2016

Proceedings of the 2nd International Workshop on Software Engineering for Smart Cyber-Physical Systems

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show abstract

“…Several recent efforts explore layout representations whose spatial relation graphs are derived from IFC space data. Application domains include code compliance checking of pedestrian circulation systems, indoor navigation, construction planning, and case-based design [23,[41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Operations on network-based space layouts for modeling multiple space views of buildings

Suter¹,

Petrushevski²,

Šipetić³

2014

Advanced Engineering Informatics

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“…The major focus is on developing spatial filter strategies. Examples of these efforts include the Spatial query language [3], QL4BIM Spatio-semantic query language [10]; graph-based BIM retrieval [11], and topological querying [12]. The second group is to enhance the human-readability of data schema by utilizing an ontology approach to transform relations among data entities from implicit to explicit.…”

Section: Partial Digital Model Extractionmentioning

confidence: 99%

Technical Term Similarity Model for Natural Language Based Data Retrieval in Civil Infrastructure Projects

Le¹,

Jeong²

2016

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-Recent advances in data and information technologies have enabled extensive digital datasets to be available to decision makers during the life cycle of a civil infrastructure project. However, much of the data is not yet fully reused due to the challenging and time consuming process of extracting the desired data for a specific purpose. Digital datasets are presented only in computer-readable formats and they are mostly complicated. In order to accurately extract a required subset of data, end users need to have deep understanding of the structure of the data schema, the meaning of each data entity and a query language. Thus, to truly facilitate the reuse of digital project data, a computational platform is needed to allow users to present their data needs in natural language. One of the critical requirements for a computer to perform this task is the ability to understand and interpret users' natural language inputs where keywords are a basic linguistic component. This research aims to collect technical terms commonly used in the civil infrastructure domain and develop a semantic similarity model that can measure the meaning relatedness/similarity between terms. Natural Language Processing (NLP) techniques and C-value method are used to automatically extract terms from text documents. A machine learning model called Skip-gram model is then employed to learn the semantic relatedness between technical terms using an unlabeled highway corpora as the input data. The input corpus includes 10 million words mainly collected from roadway design guidelines across the U.S. The model is evaluated by comparing the mapping results performed by a computer and a human.

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Graph-based retrieval of building information models for supporting the early design stages

Cited by 65 publications

References 22 publications

Adding static and dynamic semantics to building information models

Adding static and dynamic semantics to building information models

Operations on network-based space layouts for modeling multiple space views of buildings

Technical Term Similarity Model for Natural Language Based Data Retrieval in Civil Infrastructure Projects

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