Chun-Ming Zhang

TL;DR: The proposed approach has been applied to a 7-degree of freedom building model for structural damage detection, and proves to be feasible, efficient and satisfactory, and shows that the identification accuracy can be boosted with the proposed approach instead of FNN models alone.

TL;DR: A 5-phase complex structural damage detection method by integrating data fusion and PNN is developed and implemented and shows that the proposed method is feasible and effective for damage identification.

TL;DR: The numerical simulations conducted by applying the proposed hybrid data-fusion system for damage detection by integrating the data fusion technique, probabilistic neural network (PNN) models and measured modal data show that the hybrid system cannot only reliably identify damage with different noise levels, but also have excellent anti-noise capability and robustness.

TL;DR: A new eigen-level data fusion model, whereby rough set data and a probabilistic neural network are integrated using a data fusion technique, is proposed for structural damage detection, which not only has good damage detection capability and noise tolerance, but also significantly reduces data storage memory requirements and saves runtime.

TL;DR: The results show that the proposed model not only has good damage detection capability and noise tolerance, but also significantly reduces the data storage requirement and saves computing time.