BIM Cloud Score: Building Information Model and Modeling Performance Benchmarking

被引:26
作者
Liu, Rui [1 ]
Du, Jing [2 ]
Issa, Raja R. A. [1 ]
Giel, Brittany [3 ]
机构
[1] Univ Florida, ME Rinker Sr Sch Construct Management, POB 115703, Gainesville, FL 32611 USA
[2] Texas A&M Univ, Dept Construct Sci, 334 Francis Hall, College Stn, TX 77843 USA
[3] Whiting Turner Contracting Co, 300 East Joppa Rd, Baltimore, MD 21204 USA
关键词
Building information modeling (BIM); Performance evaluation; Productivity; Benchmarking; Information technologies;
D O I
10.1061/(ASCE)CO.1943-7862.0001251
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The rapid adoption of building information modeling (BIM) in the architecture, engineering, construction, and operations (AECO) industry in recent years has resulted in large differences in the level of BIM adoption and application among organizations. AECO stakeholders, including owners, general contractors, and designers, do not have a benchmark system for them to understand their own performance and know how to improve themselves. A cloud-based BIM performance benchmarking application, BIM Cloud Score (BIMCS), has been proposed to collect BIM performance data from BIM users nationwide in order to allow them to compare their BIM performance with the results collected from their industry peers. This research aims to develop and validate an initial list of metrics that are suitable for the proposed BIM benchmarking application. A survey was conducted in the AECO industry and included BIM authors (architects) and users (contractors) to validate the proposed initial list of metrics. The responses from the survey were used for initializing the metric weights for the benchmarking system. In addition, an analysis of the differences in perception among different groups of respondents showed that out of 26 variables used, the perceived importance of 6 variables was statistically different between BIM authors and BIM users. This result indicates that the surveyed architects and contractors were in agreement with the importance level for most of the variables proposed including model usefulness, model economy, and model productivity. Future adjustments of the metric weights might be needed in the implementation based on the different intended purposes for the BIM models.
引用
收藏
页数:9
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