Incremental Digital Twin Conceptualisations Targeting Data-Driven Circular Construction

被引:35
|
作者
Meda, Pedro [1 ]
Calvetti, Diego [1 ]
Hjelseth, Eilif [2 ]
Sousa, Hipolito [1 ]
机构
[1] Univ Porto, Fac Engn, Construct Inst, CONSTRUCT GEQUALTEC, P-4200465 Porto, Portugal
[2] Norwegian Univ Sci & Technol, Dept Civil & Environm Engn, N-7491 Trondheim, Norway
关键词
digitalisation; Construction; 4.0; data templates; building logbook; traceability; concepts overlap; CYCLE; TECHNOLOGIES; IMPACT;
D O I
10.3390/buildings11110554
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The construction industry faces multiple challenges, where transition to circular production is key. Digitalisation is a strategy to increase the sector's productivity, competitiveness, and efficiency. However, digitalisation also impacts environmental goals, such as those concerning more eco-friendly solutions, energy efficiency, products recycling, and sustainability certifications. These strategies rely on data, understood as digital, interoperable, incremental and traceable. Data related concepts, such as digital data templates (DDT) and digital building logbooks (DBL), contribute to "good data ". Despite some research focused on each one, little importance has yet been given to their combination. Relevant relationships and overlaps exist, as they partially share the exact same data through the built environment life cycle. This research aims to provide improved understanding on the role of these concepts and their contribution to a more circular industry. The review develops conceptualisations where DDT and DBL are complementary and framed within an incremental digital twin construction (DTC). Misconceptions or confrontations between these three solutions can therefore stand down, for the benefit of a data-driven priority. To increase understanding and reduce misconceptions, our study developed the "Digital data-driven concept " (D3c). This concept contribution is the ability to structure, store, and trace data, opening way to streamlined digital transformation impacting circular built environment concerns.
引用
收藏
页数:27
相关论文
共 50 条
  • [31] Digital Twin and Data-Driven Quality Prediction of Complex Die-Casting Manufacturing
    Liu, Dong
    Du, Yu
    Chai, Wenjie
    Lu, ChangQi
    Cong, Ming
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2022, 18 (11) : 8119 - 8128
  • [32] Toward a Digital Twin of a Solid Oxide Fuel Cell Microcogenerator: Data-Driven Modelling
    Testasecca, Tancredi
    Maniscalco, Manfredi Picciotto
    Brunaccini, Giovanni
    Airo Farulla, Girolama
    Ciulla, Giuseppina
    Beccali, Marco
    Ferraro, Marco
    [J]. ENERGIES, 2024, 17 (16)
  • [33] Intelligent feedrate optimization using a physics-based and data-driven digital twin
    Kim, Heejin
    Okwudire, Chinedum E.
    [J]. CIRP ANNALS-MANUFACTURING TECHNOLOGY, 2023, 72 (01) : 325 - 328
  • [34] Optimal Location Assignment for Data-Driven Warehouse Towards Digital Supply Chain Twin
    Erel-Ozcevik, Muge
    Ozcevik, Yusuf
    Bozkaya, Elif
    Bilen, Tugce
    [J]. 2023 IEEE 28TH INTERNATIONAL WORKSHOP ON COMPUTER AIDED MODELING AND DESIGN OF COMMUNICATION LINKS AND NETWORKS, CAMAD 2023, 2023, : 117 - 122
  • [35] A Data-Driven Digital Twin Architecture for Failure Prediction of Customized Automatic Transverse Robot
    Ye, Weiwei
    Liu, Xuepeng
    Zhao, Xinchun
    Fu, Hongbiao
    Cai, Yongbin
    Li, Hong
    [J]. IEEE ACCESS, 2024, 12 : 59222 - 59235
  • [36] Integration of Digital Twin and Circular Economy in the Construction Industry
    Meng, Xianhai
    Das, Simran
    Meng, Junyu
    [J]. SUSTAINABILITY, 2023, 15 (17)
  • [37] Generating synthetic data for data-driven solutions via a digital twin for condition monitoring in machine tools
    Sicard, Brett
    Butler, Quade
    Wu, Yuandi
    Abdolahi, Sepehr
    Ziada, Youssef
    Gadsden, S. Andrew
    [J]. SYNTHETIC DATA FOR ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING: TOOLS, TECHNIQUES, AND APPLICATIONS II, 2024, 13035
  • [38] Data-driven Digital Therapeutics Analytics
    Lee, Uichin
    Jung, Gyuwon
    Park, Sangjun
    Ma, Eun-Yeol
    Kim, Heeyoung
    Lee, Yonggeon
    Noh, Youngtae
    [J]. 2023 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING, BIGCOMP, 2023, : 386 - 388
  • [39] Data-driven Digital Mobility Twins
    Sakr, Mahmoud
    [J]. PROCEEDINGS OF THE 7TH ACM SIGSPATIAL INTERNATIONAL WORKSHOP ON LOCATION-BASED RECOMMENDATIONS, GEOSOCIAL NETWORKS AND GEOADVERTISING, LOCALREC 2023, 2023, : 4 - 5
  • [40] Data-Driven Solutions for Digital Communications
    Branchevsky, Donna
    Casado, Andres Vila
    Grayver, Eugene
    Belhouchat, Adam
    Baney, Douglas
    Braun, Andrew
    [J]. 2020 IEEE AEROSPACE CONFERENCE (AEROCONF 2020), 2020,