From BIM to Web3: A critical interpretive synthesis of present and emerging data management approaches in construction informatics

被引：0

作者：

Bucher, David F. ^{[1
]}

Hunhevicz, Jens J. ^{[1
,2
]}

Soman, Ranjith K. ^{[3
]}

Pauwels, Pieter ^{[4
,5
]}

Hall, Daniel M. ^{[6
]}

机构：

[1] Institute of Construction and Infrastructure Management, ETH Zurich, Zurich, Switzerland

[2] Urban Energy Systems Lab, EMPA, Dübendorf, Switzerland

[3] Integral Design and Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, Netherlands

[4] Faculty of Engineering and Architecture, Department of Architecture and Urban Planning, Ghent University, Ghent, Belgium

[5] Department of the Built Environment, Information Systems in the Built Environment, TU Eindhoven, Eindhoven, Netherlands

[6] Design and Construction Management, Faculty of Architecture and the Built Environment, Delft University of Technology, Delft, Netherlands

来源：

Advanced Engineering Informatics | 2024年 / 62卷

关键词：

D O I：

10.1016/j.aei.2024.102884

中图分类号：

学科分类号：

摘要：

The field of construction informatics is fragmented and lacks clarity in understanding the interconnection of different data management strategies. This makes it challenging to address industry-specific data management issues. Using a critical interpretive synthesis, this study reviews and integrates both present and emerging data management approaches in construction informatics. The review is meant to be comprehensive, encompassing technologies and concepts such as Open Schema, Information Container, Common Data Environments, Linked Data, as well as cutting-edge Web3 technologies such as blockchain and decentralized data protocols. The different approaches are identified and classified into five categories and mapped into a two-dimensional framework that considers data storage and data processing modes. The systematic categorization provides a simple, but comprehensive understanding of data management strategies in construction informatics. Moreover, the framework allows to identify the state of the art and trends of data management approaches, providing guidance for future research perspectives, especially in the intersection with Web3 technologies. © 2024 The Authors

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