Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin model

Shim, Chang-Su; Dang, Ngoc-Son; Lon, Sokanya; Jeon, Chi-Ho

doi:10.1080/15732479.2019.1620789

Cited by 202 publications

(107 citation statements)

References 7 publications

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“…In the construction phase, digital twins can be used for cost budgeting [36], quality management [37], construction collaboration [38], and schedule management [39]. In the operation and maintenance phase, digital twins can be used for equipment asset management [40], safety and prevention management, building space management, and building environment management. Shim [40,41] e program combines a 3D information model-based maintenance information management system with a digital detection system that uses image processing to enhance the decision-making process in terms of the bridge maintenance process reliability.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Zhang

et al. 2020

Advances in Civil Engineering

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The existing pavement performance prediction methods are limited to single-factor predictions, which often face the challenges of high cost, low efficiency, and poor accuracy. It is difficult to simultaneously solve the temporal, spatial, and exogenous dependencies between pavement performance data and maintenance, the service life of highways, the environment, and other factors. Digital twin technology based on the building information modeling (BIM) model, combined with machine learning, puts forward a new perspective and method for the accurate and timely prediction of pavement performance. In this paper, we propose a highway tunnel pavement performance prediction approach based on a digital twin and multiple time series stacking (MTSS). This paper (1) establishes an MTSS prediction model with heterogeneous stacking of eXtreme gradient boosting (XGBoost), the artificial neural network (ANN), random forest (RF), ridge regression, and support vector regression (SVR) component learners after exploratory data analysis (EDA); (2) proposes a method based on multiple time series feature extraction to accurately predict the pavement performance change trend, using the highway segment as the minimum computing unit and considering multiple factors; (3) uses grid search with the k-fold cross validation method to optimize hyperparameters to ensure the robustness, stability, and generalization ability of the prediction model; and (4) constructs a digital twin for pavement performance prediction to realize the real-time dynamic evolution of prediction. The method proposed in this study is applied in the life cycle management of the Dalian highway-crossing tunnel in Shanghai, China. A dataset covering 2010–2019 is collected for real-time prediction of the pavement performance. The prediction accuracy evaluation shows that the mean absolute error (MAE) is 0.1314, the root mean squared error (RMSE) is 0.0386, the mean absolute percentage error (MAPE) is 5.10%, and the accuracy is 94.90%. Its overall performance is better than a single model. The results verify that the prediction method based on digital twin and MTSS is feasible and effective in the highway tunnel pavement performance prediction.

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Section: Introductionmentioning

confidence: 99%

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Zhang

et al. 2020

Advances in Civil Engineering

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“…Considering the needs of modern operation, management and maintenance of long-span railway bridges, Zhang et al (2019c) proposed a digital bridge management and maintenance platform design scheme based on BIM multi-source information acquisition and management; it can realize structural intelligent analysis and status assessment. The 3D information and image processing digital inspection system established by Shim et al (2019) can exchange and update continuously all relevant information during the entire life cycle of the bridge. Then Shim has defined the digital twin model to realize preventive maintenance of prestressed concrete bridges.…”

Section: Bridge Scourmentioning

confidence: 99%

Review of annual progress of bridge engineering in 2019

Zhao

Yuan

Wei

et al. 2020

ABEN

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Bridge construction is one of the cores of traffic infrastructure construction. To better develop relevant bridge science, this paper introduces the main research progress in China and abroad in 2019 from 13 aspects, including concrete bridges and the high-performance materials, the latest research on steel-concrete composite girders, advances in box girder and cable-supported bridge analysis theories, advance in steel bridges, the theory of bridge evaluation and reinforcement, bridge model tests and new testing techniques, steel bridge fatigue, wind resistance of bridges, vehicle-bridge interactions, progress in seismic design of bridges, bridge hydrodynamics, bridge informatization and intelligent bridge and prefabricated concrete bridge structures.

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“…Once the data collected by the smart sensors have been filtered and analysed, the DT's algorithms can be used to enrich the knowledge of the monitored system with automatic assessment abilities. Through the use of smart sensors combined with image capture devices, some authors have proposed methodologies to provide damage assessment abilities to the DT [39,42,47,[56][57][58][59] to detect possible deterioration of the building. Shim et al have proposed such a methodology to perform a visual assessment of deterioration on concrete bridges [59] and cable-supported bridges [58] using a coding system to inventory the identified deteriorations.…”

Section: Improvements For Inspectionmentioning

confidence: 99%

“…Through the use of smart sensors combined with image capture devices, some authors have proposed methodologies to provide damage assessment abilities to the DT [39,42,47,[56][57][58][59] to detect possible deterioration of the building. Shim et al have proposed such a methodology to perform a visual assessment of deterioration on concrete bridges [59] and cable-supported bridges [58] using a coding system to inventory the identified deteriorations. Another problem that can have costly consequences and accelerate the deterioration of a building is the deviation of various elements in comparison with the initial conception of the building.…”

Section: Improvements For Inspectionmentioning

confidence: 99%

BIM-Based Digital Twin and XR Devices to Improve Maintenance Procedures in Smart Buildings: A Literature Review

Noblecourt²,

et al. 2021

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In recent years, the use of digital twins (DT) to improve maintenance procedures has increased in various industrial sectors (e.g., manufacturing, energy industry, aerospace) but is more limited in the construction industry. However, the operation and maintenance (O&M) phase of a building’s life cycle is the most expensive. Smart buildings already use BIM (Building Information Modeling) for facility management, but they lack the predictive capabilities of DT. On the other hand, the use of extended reality (XR) technologies to improve maintenance operations has been a major topic of academic research in recent years, both through data display and remote collaboration. In this context, this paper focuses on reviewing projects using a combination of these technologies to improve maintenance operations in smart buildings. This review uses a combination of at least three of the terms “Digital Twin”, “Maintenance”, “BIM” and “Extended Reality”. Results show how a BIM can be used to create a DT and how this DT use combined with XR technologies can improve maintenance operations in a smart building. This paper also highlights the challenges for the correct implementation of a BIM-based DT combined with XR devices. An example of use is also proposed using a diagram of the possible interactions between the user, the DT and the application framework during maintenance operations.

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Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin model

Cited by 202 publications

References 7 publications

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Review of annual progress of bridge engineering in 2019

BIM-Based Digital Twin and XR Devices to Improve Maintenance Procedures in Smart Buildings: A Literature Review

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