Structural Health Monitoring of Steel Garage Model with Stochastic Subspace Identification–Covariance Variance Analysis

Traditional methods of determining structural dynamic parameters produce destructive and unreliable results. Therefore, in this study, operational modal analysis method, which is a non-destructive and advanced method, was used to determine structural dynamic parameters. In this study, the dynamic parameters of the steel garage model were determined by the operational modal analysis (OMA), method and the current status of the structure was evaluated. The finite element model (FEM) of the steel garage was created with SAP2000 software, and then the experimental data were analyzed with Artemis Modal Pro software. When the FEM and OMA results were compared, it was seen that the frequency values obtained for the first five modes were quite close. When the damping ratios were examined, significant differences were detected between the theoretical and experimental values in some modes. It was determined that using the experimental damping ratios instead of the constant damping ratio assumed in the FEM would give more accurate results. Provided that the modeling and measurement stages are carried out meticulously, the operational modal analysis method as a non-destructive testing method is recommended to be used to obtain dynamic parameters in structures similar to the steel garage model.

Authors

Institutions

Publication Details

Journal
Buildings
Published
2025-03-17
DOI
https://doi.org/10.3390/buildings15060944
Citations
4
Primary Topic
Structural Health Monitoring Techniques
Type
article
Field-Weighted Citation Impact
3.81
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Structural Health Monitoring of Steel Garage Model with Stochastic Subspace Identification–Covariance Variance Analysis

Furkan Günday
4 citations
Buildings
Structural Health Monitoring Techniques
3.81
article

Structural Health Monitoring of Steel Garage Model with Stochastic Subspace Identification–Covariance Variance Analysis

Furkan Günday
article en
4 citations

Abstract

Traditional methods of determining structural dynamic parameters produce destructive and unreliable results. Therefore, in this study, operational modal analysis method, which is a non-destructive and advanced method, was used to determine structural dynamic parameters. In this study, the dynamic parameters of the steel garage model were determined by the operational modal analysis (OMA), method and the current status of the structure was evaluated. The finite element model (FEM) of the steel garage was created with SAP2000 software, and then the experimental data were analyzed with Artemis Modal Pro software. When the FEM and OMA results were compared, it was seen that the frequency values obtained for the first five modes were quite close. When the damping ratios were examined, significant differences were detected between the theoretical and experimental values in some modes. It was determined that using the experimental damping ratios instead of the constant damping ratio assumed in the FEM would give more accurate results. Provided that the modeling and measurement stages are carried out meticulously, the operational modal analysis method as a non-destructive testing method is recommended to be used to obtain dynamic parameters in structures similar to the steel garage model.

BuildingsVol. 15(6)
Giresun University (TR)
Openalex Percentile: Top 8%
Structural Health Monitoring Techniques
3.81
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.

Structural Health Monitoring of Steel Garage Model with Stochastic Subspace Identification–Covariance Variance Analysis — Furkan Günday · Buildings (2025) | TGRS Research Map | TGRS