Value optimisation in construction, from Building Information Models to Big Data

Benghi, Claudio and Willliamson, L. (2014) Value optimisation in construction, from Building Information Models to Big Data. In: IX International Cost Engineering Council 2014 World Congress, 20-22 October 2014, Milan.

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Abstract

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.

Item Type:	Conference or Workshop Item (Keynote)
Uncontrolled Keywords:	multi-dimentional BIM, Integrated Project Delivery, Data driven business, Integration, Big Data
Subjects:	G400 Computer Science K200 Building
Department:	Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User:	Becky Skoyles
Date Deposited:	31 Oct 2018 09:57
Last Modified:	11 Oct 2019 18:45
URI:	http://nrl.northumbria.ac.uk/id/eprint/36464

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