Naiwei Lu

An attention-enhanced residual network framework for structural damage detection based on structural health monitoring data

Local damage in steel structures often produces only subtle differences in vibration responses, and measurement noise can further reduce identification robustness. To address these challenges, this study proposes a structural damage identification method that combines Markov tran...

A Multidomain Feature Fusion–Based GRU-Transformer Framework for Damage Identification of Truss Bridges

Abstract In complex operational and environmental conditions, structural vibration signals are typically characterized by inherent non-stationarity and nonlinearity. Traditional convolutional neural networks (CNNs) with single-feature extraction struggle to comprehensively captur...

Digital twin-driven cross-domain damage identification for experimental cable-stayed bridge models under sparse sensing

Reliable damage identification is paramount to structural integrity and operational safety of infrastructure. However, conventional structural health monitoring (SHM) methods often encounter implementation bottlenecks, primarily constrained by data labeling and the inherent discr...