Examining the quality and management of non-geometric building information modelling data at project hand-over. (4th July 2019)

• Record Type: Journal Article
• Authors: Fitz, Daryn V.; Saleeb, Noha
• Is Part Of: Architectural engineering and design management. Volume 15:Number 4(2019)
• Page Start: 297
• Page End: 310
• Publication Date: 2019-07-04
• Subjects: Data quality -- construction -- data management -- building information modelling -- BIM; Building -- Periodicals; Architectural design -- Periodicals; Construction industry -- Management -- Periodicals; 720
• DOI: 10.1080/17452007.2018.1560243
• Languages: English
• ISSNs: 1745-2007

Abstract

ABSTRACT: Through the exponential global increase of Building Information Modelling (BIM) adoption across the Construction industry, and the emergence of inter-connected, strategic and data-rich solutions; such as Big Data, the Internet of Things and Smart Cities, the importance associated with activities and decisions reliant on exact data input, transaction, analysis, and resulting actions becomes exponentially magnified. The supply of inaccurate BIM data may negatively impact on systems and processes that require fully assured data of appropriate quality/veracity, to support informed decision making, deliver functionality, facilitate services, or direct strategic actions within the built environment. This preliminary research intends to provide a catalyst for discussion, analysis and information retrieval relating to Building Information Modelling (BIM) processes where non-geometric data errors may; or are predicted to occur within a project environment. This may result in the delivery of data that cannot be described as representing truth or of good quality, and therefore of little value or use to the data user. The broader aspects of this research investigates specifically non-geometric data veracity & associated dimensions of data quality; in order to discover and explore future solutions to resolve current industry data quality assessment challenges. This paper provides feedback from the research focusing on the current state, presenting existing industry challenges and proposes further research areas based on initial findings.