Digital Twin in Construction Safety and Its Implications for Automated Monitoring and Management

Shariatfar, Moeid; Deria, Anisha; Lee, Yong-Cheol

doi:10.1061/9780784483961.062

Cited by 4 publications

(6 citation statements)

References 5 publications

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“…Use of Digital Twin to support heritage sites maintenance has also been explored by monitoring relative humidity inside the sites using IoT devices and developing ventilation system by running simulation in the Digital Twin environment (Zhang et al, 2022). Further research has also been conducted in the use of Digital Twin in aiding healthcare facility management (Song and Li, 2022), construction safety (Shariatfar et al, 2022), and audio-based autonomous management of roadway construction (Deria et al, 2022).…”

Section: Classification Levels Of Digital Twins and Current State Of ...mentioning

confidence: 99%

“…Blockchain-based smart contract (Hunhevicz et al, 2022) Risk assessment (Kamari and Ham, 2022) Facility Management/Operation and Maintenance (Lu et al, 2020), (Seghezzi et al, 2021), (Lu et al, 2019), (Xie et al, 2020), (Abdelrahman et al, 2022), (Abdelrahman and Miller, 2022), (Song and Li, 2022) Project Management (Salem and Dragomir, 2022), (Deria et al, 2022) Smart Cities (Bujari et al, 2021), (Kim and Ham, 2022) Construction safety (Shariatfar et al, 2022), (Li et al, 2021), (Teizer et al, 2022) Augmentation: The created Digital Twin should be cohesive enough to support the future development of Digital Twin research and expansion to its initial scope of use. While creating a Digital Twin, users need to make sure that additional future functionality can be added while maintaining performance.…”

Section: Research Domain Literaturementioning

confidence: 99%

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A tool-based system architecture for a digital twin: a case study in a healthcare facility

Harode¹,

Thabet²,

Dongre³

2023

ITcon

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Changes in the local and global markets are forcing A/E/C/FM (Architecture, Engineering, Construction, and Facility Management) organizations to deliver more robust and innovative operational BIMs (Building Information Models). It is hypothesized that BIMs will transform from a static 3D model to a Digital Twin providing a truly digital representation of the physical asset or the building it represents. This transformation to a dynamic Digital Twin will allow the A/E/C/FM industry to visualize, monitor, and optimize operational assets and processes to support better inspection and analysis for a more efficient facility operations and maintenance. To support the adoption and implementation of Digital Twin in A/E/C/FM, the authors have defined two clear objectives. First, we discuss requirements for a functionality-based canonical architecture to create a digital twin followed by proposing two tool-based system architecture options for its implementation. Second, we use a case study approach to develop a proof-of-concept Digital Twin of an operating room in a healthcare facility using Power BI Desktop and Azure Services. The prototype aims to monitor room air quality as per INAIL (National Institute for Insurance against Accidents at Work) and ISO (International Organization for Standards) standards. Multiple sensors connected to a Raspberry Pi 4 are used to capture real-time data for various air quality parameters including temperature, humidity, airflow, particulate contamination, and Nitrous Oxide (N2O) gas. Multiple dashboards are also created to visualize, monitor, and analyze the data harnessed from the OR sensors. The implementation addresses critical issues including security, data storage, visualization, processing, data streaming, collection, and analysis. As an initial validation, the Digital Twin prototype was presented and discussed with a healthcare BIM manager. Initial feedback from the industry expert indicated that the prototype could decrease the required time to respond to facility maintenance issues such as decreased air flow due to possible obstructions.

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Section: Classification Levels Of Digital Twins and Current State Of ...mentioning

confidence: 99%

Section: Research Domain Literaturementioning

confidence: 99%

A tool-based system architecture for a digital twin: a case study in a healthcare facility

Harode¹,

Thabet²,

Dongre³

2023

ITcon

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“…The lifting path is created after the worker obtains the material ID. Similarly, using 4D BIM models, cloud computing, database platforms, and real-time field data acquired by sensors and analyzed by artificial intelligence algorithms, [23] created a digital twin system to increase real-time safety monitoring. A complete database and integrated 4D crane simulation and onsite operation management system for numerous contemporaneous mobile crane operations were designed in [24].…”

Section: A) Digital Twin (Dt)mentioning

confidence: 99%

“…Through a variety of self-operative functions, DT supports the connection necessary for such advances. It can be used to leverage construction data [23]. However, DT has not been distinguished from existing computation or virtual models and simulations [32].…”

Section: Technological Integarationmentioning

confidence: 99%

Overview of Emerging Technologies for Improving the Performance of Heavy-Duty Construction Machines

et al. 2022

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Construction equipment is one of the most significant resources in every large construction project, accounting for a considerable portion of the project budget. Improving heavy machinery performance can increase efficiency and reduce costs. However, research on boosting machine efficiency is limited. This study adopts mix review methodology (systematic review and bibliometric analysis) and evaluates emerging technologies like Digital twin, Cyber physical systems, Geo-graphic information systems, Global navigation satellite system, Onboard instrumentation system, Radio frequency identification, Internet of things, Telematics, Machine learning, Deep learning, and Computer vision for machine productivity and provides insights into how they can be used to improve the performance of construction equipment. The article defined three major equipment operating areas-monitoring and control, tracking and navigation, and data-driven performance optimization-classified technologies respectively, and explored how they can increase machine productivity. Other circumstantial issues affecting machine operation as well as loopholes in existing innovative tools used in machine processes were also highlighted. This study contributes to the goals of deploying digital tools and outlines future directions for the development of automated machines to optimize project efficiency.

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“…As simulation, big data, and computer technologies continue to advance, digital twin applications have proliferated in fields such as construction machinery structural optimization, trajectory control, and health monitoring [1][2][3]. In theory, it is feasible to construct digital twin models for construction machinery hydraulic systems.…”

Section: Introductionmentioning

confidence: 99%

Study of Error Flow for Hydraulic System Simulation Models for Construction Machinery Based on the State-Space Approach

Su,

Rao,

Wang

et al. 2023

Actuators

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This study presents an error flow research method for simulation models of hydraulic systems in construction machinery based on the state-space approach, aiming to ensure the reliable application of digital twin models. Initially, a comprehensive analysis of errors in the simulation modeling of hydraulic systems in construction machinery was conducted, highlighting simulation model parameters as the primary error sources. Subsequently, a set of metrics for assessing the accuracy of simulation models was developed. Following this, an error flow analysis method for simulation models of hydraulic systems in construction machinery was explored based on the state space approach, delving into the sources, transmission, and accumulation of errors in the simulation modeling of valve-controlled cylinder systems. The research results unequivocally indicate that the spring stiffness, viscous damping coefficient, and hydraulic cylinder external leakage coefficient are critical parameters affecting the accuracy of valve-controlled cylinder system simulation models. Furthermore, it was observed that the simulation model of the control valve has a significantly greater impact on the errors in the valve-controlled cylinder system simulation model than the hydraulic cylinder model. In conclusion, the reliability of the error flow model was confirmed through simulation experiments, revealing a maximum relative error of only 3.73% between the error flow model and the results of the simulation experiments.

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Digital Twin in Construction Safety and Its Implications for Automated Monitoring and Management

Cited by 4 publications

References 5 publications

A tool-based system architecture for a digital twin: a case study in a healthcare facility

A tool-based system architecture for a digital twin: a case study in a healthcare facility

Overview of Emerging Technologies for Improving the Performance of Heavy-Duty Construction Machines

Study of Error Flow for Hydraulic System Simulation Models for Construction Machinery Based on the State-Space Approach

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