Building Information Models’ data for machine learning systems in construction management
Paper i proceeding, 2019

Qualitative and quantitative data are important in construction management. However, despite the capabilities of construction informatics, such data and its sources have scarcely been fully and systematically utilized for predictive purposes. Building Information Models (BIM) are such a data source. Within BIM, information structures enabling interoperability and providing utilizable data throughout the various Levels of Development (LODs) of a building – for example, Industry Foundation Classes (IFCs) – can be fully and meaningfully exploited through data mining, and more particularly, via machine learning. In this paper, the capabilities of the information structures found in IFCs to be used as data sources for developing machine learning predictive models, will be examined. In addition, and by conceptually tying such data with constructability, their suitability for predicting – through such machine learning models – the delivery cost and time overheads of a construction project, will be considered.

Architecture and Civil Engineering

construction informatics

machine learning

Building Information Models

data mining

Arkitektur och samhällsbyggnadsteknik

Industry Foundation Classes

Construction management

Författare

Dimosthenis Kifokeris

Chalmers, Arkitektur och samhällsbyggnadsteknik, Construction Management

Mattias Roupé

Chalmers, Arkitektur och samhällsbyggnadsteknik, Construction Management

Mikael Johansson

Chalmers, Arkitektur och samhällsbyggnadsteknik, Construction Management

Christian Koch

Chalmers, Arkitektur och samhällsbyggnadsteknik, Construction Management

2019 Creative Construction Conference Proceedings, 29 June – 2 July 2019, Budapest, Hungary

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CCC 2019 Creative Construction Conference
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2019-09-23