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

Abstract

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.

Research progress and prospect of digital twin in bridge engineering

“…For example, a digital twin of a real machine can be built on a cloud platform and use the collected data and existing knowledge to simulate health conditions (Lee et al, 2013). Despite these advancements, research on digital twin was still relatively limited, and application development was hindered (Lu and Brilakis, 2019) 101 Automation in construction E (Broo et al, 2022) 18 Automation in construction F, E (Pregnolato et al, 2022) 9 Automation in construction F (Song et al, 2023) 3 Automation in construction F (Yang et al, 2022) 2 Automation in construction E (Pantoja-Rosero et al, 2023) 1 Automation in construction E (Gao et al, 2023) -Automation in construction F, O&M (Bittencourt et al, 2021) 1 Proceedings of the 10th international conference on bridge maintenance, safety and management, IABMAS 2020 F, O&M (Lai et al, 2022) 4 Case studies in construction materials O&M (Lu et al, 2020) 1 Construction research congress 2020 E (Febrianto et al, 2022) 3 Data-centric engineering E (Sánchez-Rodríguez et al, 2020) 6 EG-ICE 2020 workshop on intelligent computing in engineering, proceedings E (Jiang et al, 2021b) 28 Engineering structures O&M (Guo and Fang, 2023) -Engineering with computers E (Broo and Schooling, 2021) 11 IEEE access F (Dang et al, 2022) 22 IEEE transactions on industrial informatics F, O&M (Dan et al, 2021) 12 IEEE transactions on intelligent transportation systems F, O&M (Shim et al, 2019b) 15 International conference on smart infrastructure and construction 2019 O&M (Van Nimmen et al, 2021) 7 Journal of bridge engineering O&M (Omer et al, 2021) 7 Journal of bridge engineering E, O&M (Zhao et al, 2022) 7 Journal of bridge engineering O&M (Ye et al, 2020b) 26 Journal of civil structural health monitoring O&M (Baisthakur and Chakraborty, 2021) 7…”

“…The synchronization of data between the physical and virtual systems plays a crucial role in these approaches. Gao et al (2023) proposed a communication framework that integrates edge devices and cloud servers to achieve efficient and low-latency data transmission. Similarly, Broo et al (2022), Broo and Schooling (2021) developed a 4layer framework comprising the physical, cyber, integration, and service layers.…”

Integration of Bridge Health Monitoring System With Augmented Reality Application Developed Using 3D Game Engine–Case Study

“…Additionally, [35] used AR devices to detect potential problems in the target structures using thermal images. [36] studied the time delay of bridge Digital Twins (DT) services and proposed an Artificial Intelligence of Things (AIoT)-based DT communication framework that can support smart bridge operation and maintenance. These works too missed the immersive nature of detected damages.…”

Study on the application of deep learning technology and BIM model in the quality management of bridge design and construction stage

“…Neural network deep learning is a modern tool for data interpretation and result prediction, which can quickly read information and extract data feature values, and input calculation results after training with embedded algorithms. This learning approach is currently combined with various fields, and the use of a deep learning approach can help us to quickly make predictions about the results and greatly improve efficiency [15][16][17][18][19]. If the deep learning approach is applied to the quality inspection in the bridge construction process, not only the rate of problem solving can be improved but also the accuracy of calculation results will be enhanced.…”