Real‐time prediction method of fatigue life of bridge crane structure based on digital twin

Dong, Qing; He, Bin; Qi, Qisong; Xu, Gening

doi:10.1111/ffe.13489

Cited by 18 publications

(7 citation statements)

References 31 publications

(39 reference statements)

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“…Prediction is another requirement for the bridge DT framework, aiming to foresee the future behavior and health status of the bridge infrastructure. Predictive modeling for structural life prediction underscores the immediate applicability of predictions to trigger actions on the physical side in response to anticipated events [142]. This predictive capability allows for proactive measures, enhancing the overall resilience and reliability of the bridge.…”

Section: Predictionmentioning

confidence: 99%

Evolution of Digital Twin Frameworks in Bridge Management: Review and Future Directions

Mousavi,

Rashidi,

Mohammadi

et al. 2024

Remote Sensing

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Over the last decade, the digital twin (DT) concept has effectively revolutionized conventional bridge monitoring and management. Despite their overall success, current bridge DTs encounter conceptual ambiguities, hindering their inherent potential for practical implementation. Moreover, intelligent decision support models have not been properly considered as a component of the bridge DTs framework to enhance the reliability of decisions for asset maintenance. Therefore, this paper conducts a scientometric analysis and a comprehensive state-of-the-art review, exploring current bridge DT research trends and architectures and introducing an enhanced conceptual framework for bridge DTs. To this end, more than 480 research publications have been reviewed, compared, and analyzed. The research result encompasses the redevelopment of a multilayer DT framework, fostering its implementation in the full lifecycle of bridge infrastructure while exploring the potential integration of decision support systems and data fusion from advanced technologies to improve the overall efficiency of implementing DT technology in bridges.

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Section: Predictionmentioning

confidence: 99%

Evolution of Digital Twin Frameworks in Bridge Management: Review and Future Directions

Mousavi,

Rashidi,

Mohammadi

et al. 2024

Remote Sensing

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“…frontiersin.org problem analysis, while welding tracking belongs to nonlinear problems (Dong et al, 2021). Thus, converting the nonlinear problem in the KCF model into a linear problem involves a strategy where the nonlinear transformation φ(x) maps the input features into a high-dimensional feature space.…”

Section: Frontiers In Mechanical Engineeringmentioning

confidence: 99%

Welding robot automation technology based on digital twin

Kang,

Chen

2024

Front. Mech. Eng.

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In the era of intelligence and automation, robots play a significant role in the field of automated welding, enhancing efficiency and precision. However, challenges persist in scenarios demanding complexity and higher precision, such as low welding planning efficiency and inaccurate weld seam defect detection. Therefore, based on digital twin technology and kernel correlation filtering algorithm, a welding tracking model is proposed. Firstly, the kernel correlation filtering algorithm is used to train the filter on the first frame of the collected image, determine the position of image features in the region, extract histogram features of image blocks, and then train the filter using ridge regression to achieve welding trajectory tracking. Additionally, an intelligent weld seam detection model is introduced, employing a backbone feature network for feature extraction, feature fusion through a feature pyramid, and quality detection of weld seams through head classification. During testing of the tracking model, the maximum tracking error is −0.232 mm, with an average absolute tracking error of 0.08 mm, outperforming other models. Comparatively, in tracking accuracy, the proposed model exhibits the fastest convergence with a precision rate of 0.845, surpassing other models. In weld seam detection, the proposed model excels with a detection accuracy of 97.35% and minimal performance loss at 0.023. In weld seam quality and melt depth error detection, the proposed model achieves errors within the range of −0.06 mm, outperforming the other two models. These results highlight the outstanding detection capabilities of the proposed model. The research findings will serve as technical references for the development of automated welding robots and welding quality inspection.

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“…One can observe in the Figure 4 that the selected papers from the Construction domain are focused on a few specific applications (asset management, production planning, predictive maintenance, and state monitoring) which are based essentially on monitoring and anticipating. In the context of the construction domain, this mainly concerns monitoring and anticipating the building mechanical state and its behavior for maintenance optimization 22,23 on the one hand, and building logistics management 24,25 on the other hand. Papers presenting twins applications in the Energy Systems domain were focused on using the digital twin as a tool for state monitoring energy in grids [26][27][28] .…”

Section: Application Domains Of Digital Twinsmentioning

confidence: 99%

“…Regarding the product lifecycle, as shown in Figure 6, the applications of DT are mostly centered on the Manufacturing and Operational phases. The applications of biggest interest are the ones focused on monitoring and control (Real-time monitoring -26, Production control -21, State monitoring -15), on optimization (Production planning -34, Process evaluation and optimization -20), and on predictive maintenance (23). These groups of applications appear to be the main focus of research on digital twins.…”

Section: Distribution Of Digital Twins Use Cases Along the Lifecyclementioning

confidence: 99%

Digital Twins along the product lifecycle: A systematic literature review of applications in manufacturing

Pronost¹,

Mayer²,

Camargo³

et al. 2023

digitaltwin

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Background: The evolution of product expectations in the era of mass customization implies an improvement and a better control of individualized creation and production processes throughout the product lifecycle. The application of the digital twin (DT) seems to be a favoured solution in this context, but its study during the lifecycle of a product has only been partially evoked in the literature. Methods: The purpose of this state-of-the-art article is to identify current trends of applications of the digital twin concept in the literature under two main dimensions: its nature regarding the interaction structure between digital and physical objects (defined as: Pre-Digital Twin, digital model, digital shadow, and digital twin), but also its applications, along the product lifecycle (Design, Production, Operation, Disposal). To achieve this analysis a systematic literature review was carried out. The publications selection was based on the presence in this of a case of application of a digital twin with a focus in the Manufacturing sector. Within this review, 188 scientific papers were compiled and analyzed. Results: Results showed that although the term digital twin is widely used, the deployment of DT technologies in manufacturing is still at an early stage as most of the digital twin applications were in fact prototypes focused on the real-time observability of the physical system, either for optimization or predictive maintenance. Moreover, regarding the product lifecycle, most of the applications have been focused on the production and operational phases whereas those at the design and disposal phases are still limited. Conclusions: This paper presents an original approach to the study of digital twins, not focusing on a single application area or lifecycle phase but which aims to establish future perspectives on the use of digital twins along the lifecycle.

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Real‐time prediction method of fatigue life of bridge crane structure based on digital twin

Cited by 18 publications

References 31 publications

Evolution of Digital Twin Frameworks in Bridge Management: Review and Future Directions

Evolution of Digital Twin Frameworks in Bridge Management: Review and Future Directions

Welding robot automation technology based on digital twin

Digital Twins along the product lifecycle: A systematic literature review of applications in manufacturing

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