Title:
A model based framework for semantic interpretation of architectural construction drawings

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dc.contributor.advisor Eastman, Charles M.
dc.contributor.author Babalola, Olubi Oluyomi en_US
dc.contributor.committeeMember Augenbroe, Godfried
dc.contributor.committeeMember Brilakis, Ioannis
dc.contributor.committeeMember Ferguson, Ron W.
dc.contributor.committeeMember Narayanan, Hari N.
dc.contributor.department Architecture en_US
dc.date.accessioned 2013-06-15T02:38:02Z
dc.date.available 2013-06-15T02:38:02Z
dc.date.issued 2012-04-24 en_US
dc.description.abstract The study addresses the automated translation of architectural drawings from 2D Computer Aided Drafting (CAD) data into a Building Information Model (BIM), with emphasis on the nature, possible role, and limitations of a drafting language Knowledge Representation (KR) on the problem and process. The central idea is that CAD to BIM translation is a complex diagrammatic interpretation problem requiring a domain (drafting language) KR to render it tractable and that such a KR can take the form of an information model. Formal notions of drawing-as-language have been advanced and studied quite extensively for close to 25 years. The analogy implicitly encourages comparison between problem structures in both domains, revealing important similarities and offering guidance from the more mature field of Natural Language Understanding (NLU). The primary insight we derive from NLU involves the central role that a formal language description plays in guiding the process of interpretation (inferential reasoning), and the notable absence of a comparable specification for architectural drafting. We adopt a modified version of Engelhard's approach which expresses drawing structure in terms of a symbol set, a set of relationships, and a set of compositional frameworks in which they are composed. We further define an approach for establishing the features of this KR, drawing upon related work on conceptual frameworks for diagrammatic reasoning systems. We augment this with observation of human subjects performing a number of drafting interpretation exercises and derive some understanding of its inferential nature therefrom. We consider this indicative of the potential range of inferential processes a computational drafting model should ideally support. The KR is implemented as an information model using the EXPRESS language because it is in the public domain and is the implementation language of the target Industry Foundation Classes (IFC) model. We draw extensively from the IFC library to demonstrate that it can be applied in this manner, and apply the MVD methodology in defining the scope and interface of the DOM and IFC. This simplifies the IFC translation process significantly and minimizes the need for mapping. We conclude on the basis of selective implementations that a model reflecting the principles and features we define can indeed provide needed and otherwise unavailable support in drafting interpretation and other problems involving reasoning with this class of diagrammatic representations. en_US
dc.description.degree PhD en_US
dc.identifier.uri http://hdl.handle.net/1853/47553
dc.publisher Georgia Institute of Technology en_US
dc.subject Drafting interpretation en_US
dc.subject Diagrammatic reasoning en_US
dc.subject Drawing recognition en_US
dc.subject Knowledge representation en_US
dc.subject BIM en_US
dc.subject Building information modeling en_US
dc.subject.lcsh Architectural drawing
dc.subject.lcsh Mechanical drawing
dc.subject.lcsh Knowledge representation (Information theory)
dc.title A model based framework for semantic interpretation of architectural construction drawings en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.corporatename College of Design
local.contributor.corporatename School of Architecture
local.relation.ispartofseries Doctor of Philosophy with a Major in Architecture
relation.isOrgUnitOfPublication c997b6a0-7e87-4a6f-b6fc-932d776ba8d0
relation.isOrgUnitOfPublication 0533a423-c95b-41cf-8e27-2faee06278ad
relation.isSeriesOfPublication 1e9dd6c5-039f-4195-b3b6-bc27d2df5b9f
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