A Physics-Informed Neural Network for damage detection in truss structures from limited-sensor data, benchmarked against Modal Strain Energy and Flexibility Matrix methods paired with TLBO and Democratic Particle-Swarm Optimisation.
A PINN-based optimization method to minimize truss weight under constraints on displacement, stress, and cross-sectional areas — gradient-based and entirely data-free.
Simulated the dynamic response of a 3D truss tower in Python under El Centro ground motion, capturing modal behavior and seismic response.
Trained a Random Forest classifier on healthy and known-damage vibration datasets to detect damage in a beam from vibration data alone.
Compared the seismic response of a building across different shear wall configurations to evaluate their effect on dynamic performance.
Computed and compared storey shear, storey drift, and mode shapes for a G+6 building using two seismic design methods in Staad.Pro.
A physics-informed, differentiable framework for truss size optimization in which a neural network predicts member cross-sectional areas while enforcing structural constraints through a mechanics-based loss function — enabling efficient gradient-based optimization without any training data.