Integrated representation of geospatial data, model, and knowledge for digital twin railway

Li, Hankan; Zhu, Qing; Zhang, Liguo; Ding, Yulin; Guo, Yongxin; Wu, Haoyu; Wang, Qiang; Rui, Zhou; Liu, Mingwei; Zhou, Yan

doi:10.1080/17538947.2022.2127949

Cited by 11 publications

(3 citation statements)

References 19 publications

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“…• Knowledge integrated data modelling: This technique can be used to explicitly describe the spatiotemporal and interaction relationships among railway features through a conceptual knowledge graph [77]. This can help in understanding the complex relationships among different railway features and their interactions with each other, which can aid in decision-making and optimization.…”

Section: Tools For Data Collectionmentioning

confidence: 99%

“…Several studies have shown evidence that DL can be applied to build classifiers with limited data [9]. But those models have been evaluated on smaller and particular datasets [77]. Thus, a less computationally expensive and reusable AI model architecture is needed to handle the diverse nature of data in the railway system.…”

Section: ) Integration and Synchronizationmentioning

confidence: 99%

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Digital Twin for Railway: A Comprehensive Survey

Ghaboura,

Ferdousi,

Laamarti

et al. 2023

IEEE Access

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Digital transformation has been prioritized in the railway industry to bring automation to railway operations. Digital Twin (DT) technology has recently gained attention in the railway industry to fulfill this goal. Contemporary researchers argue that DT can be advantageous in Railway manufacturing logistics to planning and scheduling. Although underlying technologies of DT, e.g., modelling, computer vision, and the Internet of Things, have been studied for various railway industry applications, the DT has been least explored in the context of railways. Thus, in this paper, we aim to understand the state-of-the-art of DT for railway (DTR), for advanced railway systems. Besides, this survey clarifies how DT can serve the railway twin system designers and developers. As DTR is still in its early adoption stage, there is hardly any clear direction to identify the technologies for specific DTR applications. Therefore, based on our findings we present a taxonomy for DTR for designers and developers. Finally, we describe potential challenges, pitfalls, and opportunities in DTR for future researchers.

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Section: Tools For Data Collectionmentioning

confidence: 99%

Section: ) Integration and Synchronizationmentioning

confidence: 99%

Digital Twin for Railway: A Comprehensive Survey

Ghaboura,

Ferdousi,

Laamarti

et al. 2023

IEEE Access

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“…Especially in recent years, high‐speed railway (HSR) infrastructure is rapidly developing in numerous countries owing to its high efficiency in passenger movement and relatively low costs (Bian et al, 2018; Zhou et al, 2019). The construction of slope toes and tunnels along HSRs can severely impact slope stability (Laimer, 2017; Li et al, 2022). Under the superimposed amplification effect of extreme rainfall and earthquake, the slopes may slip and form landslides.…”

Section: Introductionmentioning

confidence: 99%

Early detection of potential landslides along high‐speed railway lines: A pressing issue

Zhu,

Qiu,

Cui

et al. 2023

Earth Surf Processes Landf

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Early detection of landslides is important for prevention and mitigation of landslide disasters. Especially, accurately identifying potential landslides along high‐speed railway is becoming a pressing issue for operation safety of high‐speed railway. Here, 161 Sentinel‐1A satellite images from March 2017 to September 2022 were acquired to detect potential landslides along the Lanzhou‐Urumqi high‐speed railway (LUHR) in Qinghai. Results show that rainfall is the main cause for the transition from secondary creep (steady state) to tertiary creep (accelerated) at the Jiujiawan landslide. The hot spot analysis method was used to cluster the average line‐of‐sight deformation velocity along the LUHR, effectively extracting five deformation zones that may pose a threat. Optical satellite image analysis revealed four potential landslides and one unstable slope. Furthermore, the rescaled range analysis documented that the Hurst exponent is greater than 0.8 for all four potential landslide instability points, implying that the identified potential landslide areas are still in motion and may pose a threat to the operation of the LUHR. On this basis, we propose a framework for early identification and monitoring of landslides along the high‐speed railway lines that supports landslide hazard managing with the goal to reduce landslide risk.

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