期刊
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
卷 22, 期 1, 页码 540-561出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/14759217221098998
关键词
Structural health monitoring; hybrid approach; damage detection; machine learning; support vector machine; finite element model; statistical model development; experimental study; bridge; system identification; stochastic subspace identification; modal parameters; fatigue
This paper presents a novel hybrid structural health monitoring (SHM) framework for damage detection in bridges using numerical and experimental data. The framework combines a calibrated numerical finite element (FE) model and a machine learning algorithm to establish relevant structural damage based on the evaluation of different cases.
This paper presents a novel hybrid structural health monitoring (SHM) framework for damage detection in bridges using numerical and experimental data. The framework is based on the hybrid SHM approach and combines the use of a calibrated numerical finite element (FE) model to generate data from different structural state conditions under varying environmental conditions with a machine learning algorithm in a supervised learning approach. An extensive experimental benchmark study is performed to obtain data from a local and global sensor setup on a real bridge under different structural state conditions, where structural damage is imposed based on a comprehensive investigation of common types of steel bridge damage reported in the literature. The experimental data are subsequently tested on the machine learning model. It is demonstrated that relevant structural damage can be established based on the hybrid SHM framework by separately evaluating different cases considering natural frequencies, mode shapes, and mode shape derivatives. Consequently, the work presented in this study represents a significant contribution toward establishing SHM systems that can be applied to existing steel bridges. The proposed framework is applicable to any bridge structure in which relevant structural damage can be simulated and experimental data obtained.
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