The conventional model updating based on sensitivity analysis generally employs l 1-norm
regularizer to characterize the sparsity of the structural damage. However, the l 1-norm …

This paper focuses on utilizing physics-informed neural networks (PINNs) to model
nearshore wave transformation. The nearshore wave nets (NWnets), which integrate the …

Two elements are essential in structural health monitoring utilizing dynamic responses:
response measurement with high-frequency contents, ie, small characteristic wavelengths …

Sparse Bayesian learning methods have been successfully applied to the community of
structural damage identification, which commonly assumes that the uncertainties from …

Most load identification methods require that the load location is known in advance. A
sparse Bayesian framework is proposed in this study to identify the force location and time …

从中心频率的角度出发, 深入分析变分模式分解算法中不同初始中心频率的分解特性;
利用分解特性对变分模式分解中使用的初始中心频率进行合理更新, 在没有先验知识的情况下自 …

The new metro crossing the existing metro will cause the settlement or floating of the existing
structures, which will have safety problems for the operation of the existing metro and the …

Sparse Bayesian learning (SBL) has been proved to be an effective damage detection
strategy. In this research, a structural damage detection technique with two-stage modal …

Recognizing that a multiple-degree-of-freedom mass–spring–damper system can be viewed
as a group of connected nodes, this paper presents a novel computational framework that …

The sparse regularization (SReg) model is widely used for structural damage identification
by employing the sparsity peculiarity of the structural damage. The conventional SReg …