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- Shu, Jiangpeng, 1987, et al.
(author)
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Automated generation of FE model for digital twin of concrete structures from segmented 3D point cloud
- 2019
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In: Structural Health Monitoring 2019: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT) - Proceedings of the 12th International Workshop on Structural Health Monitoring. - Lancaster, PA : DEStech Publications, Inc.. ; 1, s. 428-434
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Conference paper (peer-reviewed)abstract
- To achieve real-time structural health monitoring (SHM), a concept of digital twin - a digital copy of a structure has been brought up and investigated. It provides an up-to-date virtual model of structures, with the integration of physical as well as data information. The goal of this research is to provide faster and more accurate procedures to capture the spatial information required by a digital twin of a concrete structure using 3D point cloud data. Given that the method is intended for real-scale structures, such as bridges, the work can be divided to 3 steps: (1) to segment and extract geometric information for structural components; (2) to convert the geometry information to FE mesh with consideration of element types; (3) to assign material property as well as boundary conditions based on extracted components type. Linear FE analyses have been carried out to evaluate the structural performance based on the FE model created from the point cloud. The automation of such a process is an essential part of the creation of a digital twin of infrastructures.
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| 2. |
- Zandi, Kamyab, 1981, et al.
(author)
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A framework for digital twin of civil infrastructure-challenges and opportunities
- 2019
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In: Structural Health Monitoring 2019: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT) - Proceedings of the 12th International Workshop on Structural Health Monitoring. - Lancaster, PA : DEStech Publications, Inc.. ; 1, s. 1627-1633
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Conference paper (peer-reviewed)abstract
- All rights reserved. Digital Twin of an infrastructure is a living digital simulation that brings all the data and models together and updates itself from multiple sources to represent its physical counterpart. The primary focal point of the present study is to propose a framework for Digital Twin of infrastructure and to demonstrate it in the context of a next-generation condition assessment method. The proposed framework is based on the optimized integration of: (1) Structural Inspection: Autonomous Data Collection using drones to minimize intrusion on the transport flow, cover large areas in a minimum of time, access to hard-to-reach areas and minimize exposure to safety hazards for inspectors and users; (2) Damage Quantification: Automated Data Interpretation using data-driven techniques to detect and quantify geometrical and visual anomalies, e.g. cracking and spalling, on the surface and sub-surface of concrete infrastructure; and (3) Performance Prediction: Advanced Structural Simulation combined with physics-based deterioration models to calculate structural performance. The outcome of the study is expected to radically transform the current practices by leveraging drones for inspection, data-driven models for damage quantification, and physics-based models for performance prediction, all seamlessly connected to a living simulation platform "Digital Twin" which updates itself after each inspection round.
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