Built environment data modelling: a review of current approaches and standards supporting Asset Management

被引:3
作者
Moretti, Nicola [1 ,2 ]
Xie, Xiang [1 ,2 ]
Garcia, Jorge Merino [1 ,2 ]
Chang, Janet [1 ]
Parlikad, Ajith Kumar [1 ,2 ]
机构
[1] Univ Cambridge, Inst Mfg, Dept Engn, 17 Charles Babbage Rd, Cambridge CB3 0FS, England
[2] Univ Cambridge, Ctr Digital Built Britain, Cambridge, England
来源
IFAC PAPERSONLINE | 2022年 / 55卷 / 19期
基金
英国科研创新办公室;
关键词
Data Modelling; Information Management; Asset Management; AECO; built environment; digital twin; INDUSTRY;
D O I
10.1016/j.ifacol.2022.09.212
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Information management processes allow to access the needed data, in the right format, and enables the development of cross-domain Asset Management services. Several studies can be found in literature, on the capabilities and applications of digital tools in Architecture Constructions and Operations (AECO), demonstrating how digitisation has changed the traditional processes. Moreover, many data modelling approaches and standards can be used to support digital Asset Management applications. Due to the advancement of Digital Twin related research, the interest for the ontological and data modelling approaches have increased in recent years. This article aims at organising the body of knowledge on data modelling in AECO, through a critical review of existing approaches and standards The literature is studied and the main trends are highlighted. The most relevant articles are selected and a contents analysis is carried out. The study shows that digital Asset Management applications require interdisciplinarity and data access across different domain; two main approaches for the development of intermediate data models can be identified in literature and a unique data model able to represent multiple scales and domains cannot be found. Thus, some future research works are proposed as a conclusion of the literature review. Copyright (C) 2022 The Authors.
引用
收藏
页码:229 / 234
页数:6
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