Cloud-Based Digital Twinning for Structural Health Monitoring Using Deep Learning

Dang, Viet-Hung; Tatipamula, Mallik; Nguyen, Huan X.

doi:10.1109/tii.2021.3115119

Cited by 123 publications

(38 citation statements)

References 26 publications

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“…Then, the bridge DT can be updated with each inspection and online monitoring to support decision-making for bridge maintenance. Dang et al (Dang, Tatipamula and Nguyen, 2021) proposed a cloud-based DT framework (cDTSHM) for real-time SHM and proactive maintenance of bridges, which was demonstrated via both model and real bridges using deep learning for damage detection with high accuracy, but it requires advanced communication such as 5G, which brings in high service costs (infrastructure building, data charges, etc.) and is not suitable for bridges in remote areas without stable cellular networks.…”

Section: Bridge Digital Twinmentioning

confidence: 99%

An Efficient and Resilient Digital-twin Communication Framework for Smart Bridge Structural Survey and Maintenance

Gao¹,

LI²,

Xiong³

2022

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

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A bridge digital twin (DT) is expected to be updated in near real time during inspection and monitoring but is usually subject to massive heterogeneous data and communication constraints. This work proposes an efficient framework for a bridge DT with decreased communication complexity to achieve updates synchronously and provide feedback to the physical bridge in time. The integrated edge computing and non-cellular long-distance wireless communication enable DT resilience when cloud servers become unresponsive due to the loss of internet connection. This framework is validated by different scenarios for DTs in support of bridge inspection and monitoring. It is demonstrated that the framework can enable dynamic interaction between on-site inspection and online bridge DT during the survey as well as knowledge transfer among different sectors in time. It can also support local decision-making on a single bridge as well as regional dynamic coordination for multiple bridges without cloud-server involvement.

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Section: Bridge Digital Twinmentioning

confidence: 99%

An Efficient and Resilient Digital-twin Communication Framework for Smart Bridge Structural Survey and Maintenance

Gao¹,

LI²,

Xiong³

2022

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

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“…These are basically improvements in the computational decision method of the power system, which is determined by the current database. The real significant value of AI depends on whether a Digital Twins (DT) system for the grid is built (Bellavista et al, 2021;Dang et al, 2022). Currently, key technologies for DT include efficient simulation, hybrid modeling, integrated data perception, transmission and lifecycle data management, etc.…”

Section: Information Physics Fusionmentioning

confidence: 99%

A review of energy internet research considering interactive energy: The blockchain perspective

et al. 2022

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With the proposal of carbon peak and neutrality goals in China, new technologies such as multi-energy synergy technology, cyber physical systems, and multi-market integration technology have ushered in unprecedented opportunities and challenges. Energy Internet (EI) technology considering interactive energy has come into being. Interactive energy is a mechanism to achieve system balance by integrating economic means and power grid control technology, using “value” as a coordination method. It can promote the development and utilization of distributed renewable energy in the energy internet technology, so as to promote the clean, low-carbon, and intelligent development of energy. Blockchain can provide effective support for the development and application of EI because of its decentralized, open, autonomous, and information-immutable characteristics. In order to better cope with the challenges existing in EI, this article designs an analysis framework for EI from the blockchain perspective, which contains five dimensions, including engineering dimension, technical dimension, economic dimension, environmental dimension, and social dimension. It further refines the five dimensions aiming to comprehensively summarize the research status of the EI, which can promote its application in the development of all industries in society.

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“…Deep-learning algorithms together with digital visualization techniques of reality are used in many applications. To achieve the safety standards currently required of civil structures, the authors propose a monitoring system based on a digital model that constantly exchanges data with the real one [48]. As the review [49] demonstrates, the problem of safety is fundamental in various sectors; that is why, given the enormous reliability of deep-learning systems, they demonstrate increasingly performing results in the most variegate applications.…”

Section: Application Servicesmentioning

confidence: 99%

An Outlook on the Future Marine Traffic Management System for Autonomous Ships

Martelli¹,

Virdis

Gotta

et al. 2021

IEEE Access

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In the shipping digitalisation process, the peak will be reached with the advent of a wholly autonomous and at the same time safe and reliable ship. Full autonomy could be obtained by two linked Artificial-Intelligence systems representing the ship navigator and the ship engineer that possess sensing and analysis skills, situational awareness, planning, and control capabilities. Many efforts have been made in developing onboard systems; however, the shore facilities are not ready yet to deal with these new technologies. The paper aims to present the innovative technologies and methodologies needed to develop a futuristic Vessel Traffic System. The proposed systems will aim at faultless data acquisition and processing, provide input to decision-making systems, and suggest evasive manoeuvre; to deal with hazards and systems failure without human intervention onboard. The system is composed of three different and interacting layers. The first is an artificially intelligent tool to detect and control autonomous ships, thanks to situation recognition and obstacle avoidance strategies. The second is an orchestration and management platform designed to coordinate the sensing/actuation infrastructure and the AI algorithms' results made available by multiple ships, mustering edge, and distributed computing techniques to fulfil the specific harsh requirements of the sea environment. The final part is a holistic guidance-navigation-control framework to manage autonomous ships' navigation in a crowded area. Eventually, a cyber-physical scenario, using both a ship digital-twin and a real model-scale ship, is suggested to test and validate the innovative system without the availability of a full-scale scenario.

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Cloud-Based Digital Twinning for Structural Health Monitoring Using Deep Learning

Abstract: Cloud-based digital twinning for structural health monitoring using deep learning. IEEE Transactions on Industrial Informatics .

Cited by 123 publications

References 26 publications

An Efficient and Resilient Digital-twin Communication Framework for Smart Bridge Structural Survey and Maintenance

An Efficient and Resilient Digital-twin Communication Framework for Smart Bridge Structural Survey and Maintenance

A review of energy internet research considering interactive energy: The blockchain perspective

An Outlook on the Future Marine Traffic Management System for Autonomous Ships

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