Digital twin and its potential applications in construction industry: State-of-art review and a conceptual framework

Su, Shuaiming; Zhong, Ray Y.; Jiang, Yishuo; Song, Jinqing; Fu, Yang; Cao, Hongrui

doi:10.1016/j.aei.2023.102030

Cited by 40 publications

(6 citation statements)

References 200 publications

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“…With the proposal of the development of intelligent construction strategies in various countries, the proposal of intelligent construction theory, and the integrated application of BIM, the Internet of Things, and other information technologies in building construction, the integration of digital twin technology and the building construction process has been remarkably promoted [5]. Digital twin technology, with its high-fidelity simulation of the real world and realization of intelligent closed-loop control through virtual-real interactions, has received extensive attention and gradually become a research hotspot [24]. The concept of digital twin technology was first proposed by Professor Michael Grieves in 2002 [25], and it was initially applied in the aerospace field [26].…”

Section: Literature Reviewmentioning

confidence: 99%

Digital Twin Model and Its Establishment Method for Steel Structure Construction Processes

Liu,

Lin

2024

Buildings

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At present, the informatization level in the construction process of steel structures is relatively low. Meanwhile, digital twin technology, with better interactive features, provides a new development direction for the intelligent construction of steel structures. Therefore, this paper introduces the concept of a digital twin into the steel structure construction process, analyzes the connotation and characteristics of the digital twin model, and proposes the digital twin model architecture for steel structure construction processes. Furthermore, a method for establishing a digital twin model for steel structure construction processes is presented, which includes three stages: the acquisition and transmission of physical space data, the construction of a digital twin virtual model, and information exchange in the digital twin model. Based on these concepts, this paper describes a digital twin system architecture for the steel structure construction process from the perspective of data flow in the digital twin model. Finally, with the application of information technology in the steel structure construction process of the university park library project in Xiongan New Area and the reconstruction and expansion project of the Nanchong Gaoping airport, the digital twin model and its establishment method methods are analyzed practically and demonstrated effectively in this study.

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Section: Literature Reviewmentioning

confidence: 99%

Digital Twin Model and Its Establishment Method for Steel Structure Construction Processes

Liu,

Lin

2024

Buildings

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“…Owing to its capacity for interaction with the physical realm, DT has been applied to various engineering scenarios (e.g., manufacturing (Xiang et al, 2019), process industry (Zhu and Ji, 2022)) to support collaboration, information access, and decision-making (Altair Engineering 2023). More detailed DT applications can be found in literature reviews (Lim et al, 2020;Su et al, 2023).…”

Section: Literature Reviewmentioning

confidence: 99%

A Systematic Online Update Method for Reduced-Order-Model-Based Digital Twin

Tang,

Sajadi,

Dehaghani

et al. 2024

Preprint

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A digital twin (DT) is a model that mirrors a physical system and is continuously updated with real-time data from the physical system. Recent implementations of reduced-order-model-based DT (DT-ROM) have been applied in aerodynamics and structural health monitoring, where partial differential equations (PDEs) are utilized to update reduced bases and coefficients. However, these methods are not directly applicable when the PDEs of the system are unknown. This paper addresses the online update challenge for DT-ROM in scenarios lacking known PDEs of the system. To tackle the challenge, a systematic online update and application method is proposed. During the online update, the projection residual of online data on the reduced bases determines the necessity of updating reduced bases, while the prediction residual of online data obtained by the current DT-ROM is used to decide whether to update the coefficient model. By sequentially evaluating both criteria, the method selectively incorporates essential online data for the online DT model update. During the online application, a criterion defined based on online data is adopted to determine whether the offline DT-ROM or the online one is applied to output final predictions. The capability of the proposed method is tested through three numerical and three engineering problems. Results indicate that the proposed online update method consistently reduces both projection and prediction residuals, thereby progressively enhancing the performance of the online DT-ROM on test data. Meanwhile, the online application method provides a prediction performance better than using offline DT-ROM only. Both demonstrate that the proposed work could be applied to online DT update where the PDEs of the system are unknown.

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“…These include, machine learning, digital twins, and the Internet of Things (IoT), and described them as critical components that enhance operations within sustainable building life cycles. Examples of those researchers were, De Wilde [47], Petri [65], Yvan [62], Boje [68], Baduge [71], Adu-Amankwa [72], Lucchi [73], Su [75], Alanne [58], Kineber [59], Boje [86], and Arowoiya [79]. While direct application of AI may be limited, these technologies served as crucial connections that enable effective enhancement of operations.…”

Section: Ai In the Operation Stage Of Sustainable Building Lifecyclementioning

confidence: 99%

Application of Artificial Intelligence (AI) in Sustainable Building Lifecycle; A Systematic Literature Review

Adewale,

Ene,

Ogunbayo

et al. 2024

Preprint

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With buildings accounting for a significant portion of global energy consumption and greenhouse gas emissions, the application of artificial intelligence (AI) holds promise for enhancing sustainability in the building lifecycle. This systematic literature review addresses the current understanding of AI's potential to optimize energy efficiency and minimize environmental impact in building design, construction, and operation. A comprehensive literature review and synthesis were conducted to identify AI technologies applicable to sustainable building practices, examine their influence, and analyze the challenges of implementation. The review was guided by a meticulous search strategy utilizing keywords related to AI application in sustainable building design, construction, and operation. The findings reveal AI's capabilities in optimizing energy efficiency through intelligent control systems, enabling predictive maintenance, and aiding design simulation. Advanced machine learning algorithms facilitate data-driven analysis and prediction, while digital twins provide real-time insights for informed decision-making. Furthermore, the review identifies barriers to AI adoption, including cost concerns, data security risks, and challenges in implementation. AI presents a transformative opportunity to enhance sustainability in the built environment, offering innovative solutions for energy optimization and environmentally conscious practices. However, addressing technical and practical challenges will be crucial for the successful integration of AI in sustainable building practices.

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Digital twin and its potential applications in construction industry: State-of-art review and a conceptual framework

Cited by 40 publications

References 200 publications

Digital Twin Model and Its Establishment Method for Steel Structure Construction Processes

Digital Twin Model and Its Establishment Method for Steel Structure Construction Processes

A Systematic Online Update Method for Reduced-Order-Model-Based Digital Twin

Application of Artificial Intelligence (AI) in Sustainable Building Lifecycle; A Systematic Literature Review

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