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NCKU Yushan Fellow Project Team
Goal of the Project
Goal of the Project
To develop integrated machine learning and AI-enhanced computational models and algorithms, advanced sensor materials, and monitoring technologies for the prediction, damage assessment, and mitigation of critical infrastructures subjected to natural disasters.
* group leader: KC Lin
* group leader: YC Wang
* group leader: CH Yu
* group leaders: HC Kan and YC Tai
* group leader : TC Hou
* group leader : CC Hung
Task 1: Meshfree formulation for modeling multi-scale damages and failure mechanisms in infrastructures subjected to natural disasters (Group 1)
Task 2: Neural network enhanced computational method for modeling strain localization and fractures in reinforced concrete structures and foundations under extreme loadings (Group 2, 3)
Task 3: Thermodynamics-based machine learning algorithms for modeling hydro-mechanical path and rate-dependent soil behavior (Group 1, 3)
Task 4: Reduced order modeling of infrastructural failure processes subjected to natural disasters (Group 1, 4)
Task 5: Advanced sensor materials and monitoring technology development by the NCKU team for simulation model calibration and validation (Group 5)
Task 6: Infrastructure Damage Mitigation and Retrofitting (Group 6)
Task 7: Multi-physics multi-scale machine learning assisted digital twins for critical infrastructures and the associated hazard assessments (All Groups)
Modeling of Seepage-Induced Levee Failure Using a Naturally Stabilized Semi-Lagrangian RKPM Formulation for Multiphase Porous Media
Modeling of Seepage-Induced Levee Failure Using a Naturally Stabilized Semi-Lagrangian RKPM Formulation for Multiphase Porous Media
Wei, H., Chen, J. S., and Hillman, M., “A Stabilized Nodally Integrated Meshfree Formulation for Fully Coupled Hydro-Mechanical Analysis of Fluid-Saturated Porous Media”, Computers and Fluids, Vol. 141, pp. 105–115, 2016.
Wei, H., Chen, J. S., Beckwith, H., Baek, J.,“A Naturally Stabilized Semi-Lagrangian Meshfree Formulation for Multiphase Porous Media with Application to Landslide Modeling,” Journal of Engineering Mechanics, Vol.146 (4), 04020012, 2020.
ANSI-Platform:
A 3D user-interactive GPU-accelerated illustration system, in which GPU-accelerated Simulation Tools (based on Tai et al, 2019, JGR; Wong et al., 2024, AWR, etc.) are integrated for computing the results.
A 3D user-interactive GPU-accelerated illustration system, in which GPU-accelerated Simulation Tools (based on Tai et al, 2019, JGR; Wong et al., 2024, AWR, etc.) are integrated for computing the results.
Questions?
Questions?
Contact em63100@email.ncku.edu.tw to get more information on the project.
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