دانلود مقاله ISI انگلیسی شماره 151592
کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
151592 2018 17 صفحه PDF سفارش دهید 13906 کلمه
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عنوان انگلیسی
Interpretation of structural analytical models from the coordination view in building information models
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Automation in Construction, Volume 90, June 2018, Pages 117-133

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چکیده انگلیسی

Structural design/analysis is one of the most needed uses of Building Information Modeling (BIM). Transforming a building information model to an engineering analytical model is tedious and time-consuming. In addition to geometry transformation, extensive modifications and interpretations are required to make the complex transformed model ready for analysis. Despite such a recognized need, Industry Foundation Classes (IFC) has not been developed sufficiently in engineering analysis uses of BIM as much as it is in some other uses such as design coordination and facility management. As a contribution to addressing this void, development of a new mechanism is discussed in this paper for transformation of IFC building information models in the Coordination View to their equivalent structural models in IFC Structural Analysis View. Considering IFC as the input and output file formats of the mechanism significantly increases the level of interoperability in the proposed model interpretation process. This mechanism is designed to automate the required transformation, modification, and additions operations during such information exchanges. To illustrate feasibility of its implementation, a tool is introduced to automate the developed Interpreted Information Exchange (IIE) mechanism, and its application through a case study serves as validation of the mechanism. The designed IIE mechanism can be extended to automate additional structural modeling tasks. The IIE concept is also applicable to other uses of BIM, especially engineering analysis uses, in order to automate creation of analytical modeling from building information models.

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