Repetitive Identification of Structural Systems Using a Nonlinear Model Parameter Refinement Approach
This paper proposes a statistical confidence interval based nonlinear model parameter refinement approach for the health monitoring of structural systems subjected to seismic excitations. The developed model refinement approach uses the 95% confidence interval of the estimated structural parameters...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2009-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.3233/SAV-2009-0463 |
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| _version_ | 1832549099374641152 |
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| author | Jeng-Wen Lin Hung-Jen Chen |
| author_facet | Jeng-Wen Lin Hung-Jen Chen |
| author_sort | Jeng-Wen Lin |
| collection | DOAJ |
| description | This paper proposes a statistical confidence interval based nonlinear model parameter refinement approach for the health monitoring of structural systems subjected to seismic excitations. The developed model refinement approach uses the 95% confidence interval of the estimated structural parameters to determine their statistical significance in a least-squares regression setting. When the parameters' confidence interval covers the zero value, it is statistically sustainable to truncate such parameters. The remaining parameters will repetitively undergo such parameter sifting process for model refinement until all the parameters' statistical significance cannot be further improved. This newly developed model refinement approach is implemented for the series models of multivariable polynomial expansions: the linear, the Taylor series, and the power series model, leading to a more accurate identification as well as a more controllable design for system vibration control. Because the statistical regression based model refinement approach is intrinsically used to process a “batch” of data and obtain an ensemble average estimation such as the structural stiffness, the Kalman filter and one of its extended versions is introduced to the refined power series model for structural health monitoring. |
| format | Article |
| id | doaj-art-6e6ed953e55f440a937ddc0ff672700b |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2009-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-6e6ed953e55f440a937ddc0ff672700b2025-02-03T06:12:08ZengWileyShock and Vibration1070-96221875-92032009-01-0116322924010.3233/SAV-2009-0463Repetitive Identification of Structural Systems Using a Nonlinear Model Parameter Refinement ApproachJeng-Wen Lin0Hung-Jen Chen1Department of Civil Engineering, Feng Chia University, 407 Taichung, TaiwanGraduate Institute of Civil and Hydraulic Engineering, Feng Chia University, 407 Taichung, TaiwanThis paper proposes a statistical confidence interval based nonlinear model parameter refinement approach for the health monitoring of structural systems subjected to seismic excitations. The developed model refinement approach uses the 95% confidence interval of the estimated structural parameters to determine their statistical significance in a least-squares regression setting. When the parameters' confidence interval covers the zero value, it is statistically sustainable to truncate such parameters. The remaining parameters will repetitively undergo such parameter sifting process for model refinement until all the parameters' statistical significance cannot be further improved. This newly developed model refinement approach is implemented for the series models of multivariable polynomial expansions: the linear, the Taylor series, and the power series model, leading to a more accurate identification as well as a more controllable design for system vibration control. Because the statistical regression based model refinement approach is intrinsically used to process a “batch” of data and obtain an ensemble average estimation such as the structural stiffness, the Kalman filter and one of its extended versions is introduced to the refined power series model for structural health monitoring.http://dx.doi.org/10.3233/SAV-2009-0463 |
| spellingShingle | Jeng-Wen Lin Hung-Jen Chen Repetitive Identification of Structural Systems Using a Nonlinear Model Parameter Refinement Approach Shock and Vibration |
| title | Repetitive Identification of Structural Systems Using a Nonlinear Model Parameter Refinement Approach |
| title_full | Repetitive Identification of Structural Systems Using a Nonlinear Model Parameter Refinement Approach |
| title_fullStr | Repetitive Identification of Structural Systems Using a Nonlinear Model Parameter Refinement Approach |
| title_full_unstemmed | Repetitive Identification of Structural Systems Using a Nonlinear Model Parameter Refinement Approach |
| title_short | Repetitive Identification of Structural Systems Using a Nonlinear Model Parameter Refinement Approach |
| title_sort | repetitive identification of structural systems using a nonlinear model parameter refinement approach |
| url | http://dx.doi.org/10.3233/SAV-2009-0463 |
| work_keys_str_mv | AT jengwenlin repetitiveidentificationofstructuralsystemsusinganonlinearmodelparameterrefinementapproach AT hungjenchen repetitiveidentificationofstructuralsystemsusinganonlinearmodelparameterrefinementapproach |