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Artificial Intelligence assisted professional work in BIM: a machine reasoning extension

Xu, S., Li, W. ORCID:, Tang, L. C. M., Lin, Y. and Tang, Q. (2018) Artificial Intelligence assisted professional work in BIM: a machine reasoning extension. In: Creative Construction Conference 2018, 30 June - 3 July 2018, Radisson Blu Plaza, Ljubljana, Slovenia, pp. 16-23,

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To link to this item DOI: 10.3311/CCC2018-003


A rule based semantic method is utilised as a model and is demonstrated to tackle the problems of analytical processes. The investigation of BIM-based cost estimation confirmed that industry foundation classes (IFC) can provide construction project semantics but incapable of relating domain semantics and pragmatics. Our model provided the rules that three components are necessary to gain a full awareness of the domain which is being computerised; the information type which is to be assessed for compatibility (syntax), the definition for the pricing domain (semantics), and the precise implantation environment for the standards being considered (pragmatics). This paper outlines the way in which the proposed approach has been verified, by employing a selection of codes created by the prototype of the data based model. The standards of practice which have been established are then verified, in accordance with the actual building information gained from IFC. The utilisation of this approach has significantly advanced the procedure of automating professional costing practice within BIM. These justified outcomes demonstrate that, by implementing this model, the reasoning ability can be used by the BIM context and the restrictions around the application of BIM will be reduced. The BIM platform is directly affected by the IFC file that is housed within the ontological structure which has similarities to the Semantic Web and Logic Programming. The adoption of this methodology has greatly advanced the process of automating complex sets of construction standard, allowing the automation of analytical processes. It also outlines the possible connection between machine learning and machine reasoning in order to facilitate wider adoption of computer aided professional practice.

Item Type:Conference or Workshop Item (Paper)
Divisions:Henley Business School > Business Informatics, Systems and Accounting
ID Code:77088

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