Structural engineering community require the experience of experts in design, simulation and structural health monitoring (SHM) of existing structures. Currently, the training process of structural engineers may take more than 10 years from undergraduate to expert. The economic design currently relies on the experience of engineers, which may not reach the optimized design outcome. In addition, high-fidelity simulation and SHM are still challenging and practical applications of the nonlinear structural simulation and SHM are mostly limited to researchers, instead of practical engineers. Conventional structural engineering widely adopts finite element solvers based on CPUs, which may be time consuming. The computing resources of GPU accelerators and GPU-based supercomputers cannot be fully utilized due to the lack of GPU-based simulation platforms.
The project develops deep-learning-based intelligent structural design, simulation and structural health monitoring platform. For structural design, dataset is collected for structural design input parameters and structural design drawings, the generative models are learned to generate preliminary structural design drawings of buildings and bridges. For structural simulation, physics-informed neural networks are developed to replicate the spatial discretization and temporal discretization of conventional finite element solvers. For SHM, the state-of-the-art neural operator is trained on finite element simulation dataset of vehicle-bridge interaction (VBI) system and fine-tuned on experimental dataset to infer the damage distribution field based on structural response field. The project can inspire the undergraduate and graduate students to learn more about the challenges and future developments of structural engineering.
Dr Jiaji Wang joined the Department of Civil Engineering at the University of Hong Kong as Assistant Professor in Jan 2023. He obtained his Ph.D. supervised by Professor Jianguo Nie at Tsinghua University and served as JSPS postdoctoral fellow at Kyoto University and research associate at University of Houston from. Dr Wang’s researches are mainly focused on Data-driven and physics-informed operator learning for solving partial differential equations and AI-based structural health monitoring. Dr. Wang has been awarded the outstanding award of China Steel Construction Society in 2019 and published 35 journal papers as first or corresponding author in leading journals of structural engineering and computational mechanics.
Clean water and clean air are vital for public health. This project focuses on developing high-efficiency and environmentally sustainable filters for removing harmful air/water pollutants. The team has developed novel architectures and functionalities for the filters to achieve high permeance, high removal efficiency, and excellent reusability.
