Research

Jichuan's research sits at the intersection of structural dynamics, scientific machine learning, and structural health monitoring. Recurring threads include neural-operator surrogates for full-field response prediction, multi-fidelity learning for systems where high-fidelity simulation is expensive, and reinforcement-learning controllers for vibration mitigation and real-time hybrid simulation.

Surrogate models for full-field cable-stayed bridge dynamics

  • Neural operators
  • DeepONets
  • Structural dynamics
Full-field DeepONet branch–trunk architecture from the structural-dynamics paper
Full-field DeepONet architecture used as the surrogate for cable-stayed bridge dynamics.

High-fidelity finite-element simulations of cable-stayed bridges resolve full-field dynamic response but are too expensive for design iteration, uncertainty quantification, or real-time decision support. Existing surrogates predict scalar outputs (e.g., peak displacement) and lose the spatial detail that engineers actually use.

We propose a spatio-temporal full-field DeepONet that returns multiple dynamical fields in a single forward pass. The architecture strengthens branch–trunk interactions and inherently encodes spatial correlations between outputs. We benchmark three DeepONet variants on a cable-stayed bridge model; the full-field variant achieves the highest accuracy at the lowest inference cost, making it a practical surrogate for downstream UQ and control studies.

Multi-fidelity surrogates for shipboard shock response

  • Multi-fidelity learning
  • Transfer learning
  • Shock dynamics
DeepONet architecture for multi-fidelity shock-response surrogate
DeepONet residual model for multi-fidelity shock prediction.

High-fidelity shock simulations of shipboard structures capture whipping and resonant response, but their cost rules out the parameter sweeps that designers need. Low-fidelity models are cheap but systematically miss those dynamics, so naïvely substituting them is unsafe.

We characterize the discrepancy between low- and high-fidelity shock models under impulsive loading, then learn a residual correction with Deep Operator Networks (DeepONets) and recurrent architectures (RNN, LSTM, GRU). Combining transfer learning with residual learning, the resulting multi-fidelity surrogate inherits the speed of the low-fidelity model while recovering the accuracy of the expensive one. The work was conducted under an Office of Naval Research technical report.

Denoising wireless MEMS sensors for infrastructure monitoring

  • Wireless sensing
  • MEMS
  • Generative denoising
MEMS accelerometer module used in the wireless sensing study
MEMS accelerometer module used in the GAN-based denoising study.

Wireless MEMS accelerometers are the workhorse of low-cost infrastructure monitoring, but their low-frequency noise floor obscures the modal signatures and subtle defect signals that engineers actually need to detect.

We calibrate MEMS accelerometers against reference sensors, then train a Generative Adversarial Network to denoise the low-frequency response, comparing against empirical mode decomposition and wavelet baselines. A LabVIEW-based host environment supports durability testing (temperature, salt-spray) and field deployment, including synchronized hardware on tunnel diagnosis vehicles for hidden-defect inspection.

Adaptive vibration control of human-loaded footbridges

  • Reinforcement learning
  • TD3
  • Semi-active TMD
Reinforcement-learning vibration-control scheme for a pedestrian footbridge with semi-active TMD
RL-based vibration-control loop for the bridge–pedestrian–STMD system.

Slender pedestrian footbridges are vulnerable to human-induced vibrations that violate serviceability limits. Conventional passive tuned mass dampers cannot adapt to crowd density, gait variability, or human–structure feedback, leaving residual response under realistic loading.

We build a coupled model of bridge, pedestrian, and semi-active tuned mass damper (STMD), then train a TD3 agent to control the STMD under both periodic and stochastic pedestrian loading. The study reports the influence of key hyperparameters (learning rate, discount factor) on closed-loop performance and proposes a general scheme for RL-based STMD control of pedestrian bridges.

Real-time hybrid simulation under wave and earthquake loading

  • Real-time hybrid simulation
  • DDPG
  • Feedforward compensation
DDPG-with-feedforward controller for real-time hybrid simulation
DDPG + feedforward control architecture for the RTHS transfer system.

Real-time hybrid simulation (RTHS) of structures under wave and earthquake loading is limited by servo-hydraulic time delay and tracking error, especially in worst-case loading where delay-induced phase lag can destabilize the test. Model-based controllers compensate only partially and depend on accurate plant identification.

We build a Simulink digital twin of an underwater shaking-table system and validate it against physical experiments. A DDPG controller trained against this twin reduces worst-case time delay by 6.54% and tracking error by 7.52% relative to a model-based controller (and by 123.48% / 89.95% relative to no compensation). Adding feedforward compensation to DDPG yields 3.28% maximum perturbation versus 5.88% (PI) and 10.57% (FF only) on a benchmark RTHS problem.

For the underlying papers and links, see the publications page.