1:45 PM - 3:15 PM
[HDS08-P04] Seismic response analysis of truss bridge model using finite element method
Keywords:Finite element method, Bridge, earthquake
Maintaining infrastructure structures such as bridges and evacuation shelter buildings is extremely important for developing plans for transporting supplies and evacuating people when a disaster strikes. However, the aging of infrastructure structures and a declining population have led to an increase in the cost of maintenance and management of infrastructure structures.
Against this background, it has been proposed to reduce maintenance and management costs by constructing digital twins for infrastructure structures based on IoT technology, simulation technology, and large-scale data analysis [1]. More specifically, it is assumed that the state of the infrastructure structure will be monitored steadily and that inspection and repair will be carried out with priority depending on the deteriorated region in the structure obtained from computer simulations [1].
Since the digital twin requires modeling the shape of real objects and simulating their actual behavior under external forces as much as possible, the use of the finite element method, which can take into account the shape and mechanical properties of objects, is one of the essential technologies for constructing an advanced digital twin.
As the fundamental part of the digital twin for a truss bridge, I attempted to quantitatively estimate the seismic response of the three-dimensional Pratt truss bridge model [2] [3] using the finite element method implemented on the COMSOL Multiphysics platform. The vibration pattern of the bridge obtained from the seismic response analysis is discussed and compared with the results of the eigenmode analysis conducted separately, visualizing the regions where the amplitude of strain is predominant.
References
[1] Tokyo Metropolitan Government: Roadmap for the Social Implementation of Digital Twin First Edition, https://info.tokyo-digitaltwin.metro.tokyo.lg.jp/roadmap/ (accessed Feb 7, 2023).
[2] COMSOL Multiphysics: site for Pratt truss bridge models, https://www.comsol.jp/model/pratt-truss-bridge-8511 (accessed Feb 7, 2023).
[3] Hidetaka Saomoto and Takashi Miyamoto: Generating machine learning datasets on damage identification using finite element bridge model, Journal of Japan Society of Civil Engineers, Ser. A1 (Structural Engineering & Earthquake Engineering (SE/EE)), 2022 (in Japanese).
Against this background, it has been proposed to reduce maintenance and management costs by constructing digital twins for infrastructure structures based on IoT technology, simulation technology, and large-scale data analysis [1]. More specifically, it is assumed that the state of the infrastructure structure will be monitored steadily and that inspection and repair will be carried out with priority depending on the deteriorated region in the structure obtained from computer simulations [1].
Since the digital twin requires modeling the shape of real objects and simulating their actual behavior under external forces as much as possible, the use of the finite element method, which can take into account the shape and mechanical properties of objects, is one of the essential technologies for constructing an advanced digital twin.
As the fundamental part of the digital twin for a truss bridge, I attempted to quantitatively estimate the seismic response of the three-dimensional Pratt truss bridge model [2] [3] using the finite element method implemented on the COMSOL Multiphysics platform. The vibration pattern of the bridge obtained from the seismic response analysis is discussed and compared with the results of the eigenmode analysis conducted separately, visualizing the regions where the amplitude of strain is predominant.
References
[1] Tokyo Metropolitan Government: Roadmap for the Social Implementation of Digital Twin First Edition, https://info.tokyo-digitaltwin.metro.tokyo.lg.jp/roadmap/ (accessed Feb 7, 2023).
[2] COMSOL Multiphysics: site for Pratt truss bridge models, https://www.comsol.jp/model/pratt-truss-bridge-8511 (accessed Feb 7, 2023).
[3] Hidetaka Saomoto and Takashi Miyamoto: Generating machine learning datasets on damage identification using finite element bridge model, Journal of Japan Society of Civil Engineers, Ser. A1 (Structural Engineering & Earthquake Engineering (SE/EE)), 2022 (in Japanese).