دانلود مقاله ISI انگلیسی شماره 68826
ترجمه فارسی عنوان مقاله

تجزیه و تحلیل کمی از هشدار در مدل سازی اطلاعات ساختمان

عنوان انگلیسی
Quantitative analysis of warnings in building information modeling (BIM)
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
68826 2015 9 صفحه PDF
منبع

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

Journal : Automation in Construction, Volume 51, March 2015, Pages 23–31

ترجمه کلمات کلیدی
مدلسازی اطلاعات ساختمان؛ اشتباهات طراحی؛ تصمیم سازی؛ مدیریت طراحی؛ تحلیل پارتو
کلمات کلیدی انگلیسی
Building information modeling; Design errors; Decision making; Design management; Pareto analysis
پیش نمایش مقاله
پیش نمایش مقاله  تجزیه و تحلیل کمی از هشدار در مدل سازی اطلاعات ساختمان

چکیده انگلیسی

Building information modeling (BIM) provides automatic detection of design-related errors by issuing warning messages for potential problems related to model elements. However, if not properly managed, the otherwise useful warning feature of BIM can significantly reduce the speed of model processing and increase the size of models. As the first study of its kind, this study proposes to apply the Pareto analysis to investigate BIM warnings in terms of type and frequency. Based on warning data collected from three California healthcare projects, the analysis revealed that the 15–80 rule applies across the case projects and their design phases—15% of the warning messages are responsible for nearly 80% of the warnings. Two other noteworthy findings include the following: (1) only the schematic design phase indicates a different Pareto rule of 25–80, as well as warning pattern from other design phases due to its unique purpose; and (2) the decisions of individual design teams are a major variable in the pattern of warning types. Lastly, time estimation for warning corrections is proposed based on learning curve theory to support efficient BIM warning management practices. The results and warning classifications presented in this study are expected to contribute to the design management and modeling practices of design teams involved in large, complex projects.