Local damage-sensing method for structural members based on stress correlation characteristics

Abstract Structures are subjected to various external factors during the service life, the structure may have potential safety hazards caused by local damage. In order to accurately perceive the structural safety and identify issues in real-time, it is essential to develop local damage-sensing method for structural members using monitoring data. Therefore, this study proposes a local damage-sensing method for structural components based on stress correlation characteristics. This method relies on stress monitoring data, takes temperature influence into account, and achieves high accuracy in identifying local damage sensitivity. First, a strain response expression method is proposed based on the unit impulse response function and the external excitation function. The strain correlation function for any two measurement points on the structure is derived, and the correlation function between the strain at the measurement point and the strain at a reference point is calculated. The correlation function magnitude vector is constructed from its absolute values, and a component-state perception index is proposed based on the residuals of the magnitude vector of the stress correlation function. The monitoring data of Zhuhai Grand Theatre for 30 d within 5 months are analysed. By applying the proposed method to the Zhuhai Grand Theatre, the slowly varying temperature effects are effectively separated, the local damage identification result is in accordance with the presupposed situation, where the feasibility and accuracy of the proposed method is confirmed.

Authors

Institutions

Publication Details

Journal
Measurement Science and Technology
Published
2024-11-29
DOI
https://doi.org/10.1088/1361-6501/ad98ae
Primary Topic
Structural Health Monitoring Techniques
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Local damage-sensing method for structural members based on stress correlation characteristics

Xiaoyang Tan, Wei Lu, Weihua Hu, Jun Teng et al.
1 links
Measurement Science and Technology
Structural Health Monitoring Techniques
article

Local damage-sensing method for structural members based on stress correlation characteristics

Xiaoyang Tan, Wei Lu, Weihua Hu, Jun Teng, Yan Cui
article en

Abstract

Abstract Structures are subjected to various external factors during the service life, the structure may have potential safety hazards caused by local damage. In order to accurately perceive the structural safety and identify issues in real-time, it is essential to develop local damage-sensing method for structural members using monitoring data. Therefore, this study proposes a local damage-sensing method for structural components based on stress correlation characteristics. This method relies on stress monitoring data, takes temperature influence into account, and achieves high accuracy in identifying local damage sensitivity. First, a strain response expression method is proposed based on the unit impulse response function and the external excitation function. The strain correlation function for any two measurement points on the structure is derived, and the correlation function between the strain at the measurement point and the strain at a reference point is calculated. The correlation function magnitude vector is constructed from its absolute values, and a component-state perception index is proposed based on the residuals of the magnitude vector of the stress correlation function. The monitoring data of Zhuhai Grand Theatre for 30 d within 5 months are analysed. By applying the proposed method to the Zhuhai Grand Theatre, the slowly varying temperature effects are effectively separated, the local damage identification result is in accordance with the presupposed situation, where the feasibility and accuracy of the proposed method is confirmed.

Measurement Science and TechnologyVol. 36(1)
Harbin Institute of Technology (CN), Guangdong Institute of Intelligent Manufacturing (CN), Shenzhen Municipal People's Government (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 76%
Structural Health Monitoring Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.