Building Information Models’ data for machine learning systems in construction management (original) (raw)
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Purpose of this paper The paper starts from the analysis of the benefits achievable and expected from the use of Building Information Management (BIM) in construction and aims at identifying and prioritising expected trends in the area. Design/methodology/approach The methodology adopted starts from a systematic review of current academic literature, for the identification of the state of the art, and it then moves to the identification of trends by the application of two further biblio-metric techniques; the first one provides reference to government and institutional policies in the area of construction and discusses their correlation with the benefits identified in the first analysis and the second one analyses the corpus of the US National Science Foundation grant awarded in the area of data-driven science to identify efficient principles to be adopted by players interested in the development of data-driven exploitation of Integrated Project Delivery and BIM for construction value. Findings and value The paper lists strategic criteria to drive the development for the next generation of integrated speciality IT components in consideration of readiness factors for artificial intelligence supported value extraction. Originality/value of paper The paper is original in the identifications of extremely recent trends and technologies and does so on quantitative grounds.
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The construction industry suffers from inadequate data management of current and past projects. Since, many projects result in problems such as exceeded budgets, time delays, quality problems, conflicts and missing data transfer between stakeholders, Building Information Modeling (BIM) was adopted in the industry to overcome those problems. With the help of BIM, information and analysis results of a project can be collected and used in various decision-making processes. However, a BIM model that consists of semantic and geometric information needs to be integrated with other methods to both manage and achieve necessary information without creating a software bottleneck. Besides, facility managers using BIM in the operational phases of a project need to gather dynamic data that produces too much information. In this context, Big Data Analytics has come into prominence to overcome the data volume, variety, and velocity in building the data resource for BIM model. Thus, in this study i...