Nonlinear Regression Based Health Monitoring of Hysteretic Structures under Seismic Excitation
This paper presents a health monitoring method using measured hysteretic responses. Acceleration and infrequently measured displacement are integrated using a multirate Kalman filtering method to generate restoring force-displacement hysteresis loops. A linear/nonlinear regression analysis based two...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2015/193136 |
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| author | C. Xu J. Geoffrey Chase Geoffrey W. Rodgers |
| author_facet | C. Xu J. Geoffrey Chase Geoffrey W. Rodgers |
| author_sort | C. Xu |
| collection | DOAJ |
| description | This paper presents a health monitoring method using measured hysteretic responses. Acceleration and infrequently measured displacement are integrated using a multirate Kalman filtering method to generate restoring force-displacement hysteresis loops. A linear/nonlinear regression analysis based two-step method is proposed to identify nonlinear system parameters. First, hysteresis loops are divided into loading/unloading half cycles. Multiple linear regression analysis is applied to separate linear and nonlinear half cycles. Preyielding stiffness and viscous damping coefficient are obtained in this step and used as known parameters in the second step. Then, nonlinear regression analysis is applied to identified nonlinear half cycles to yield nonlinear system parameters and two damage indicators: cumulative plastic deformation and residual deformation. These values are closely related to structural status and repair costs. The feasibility of the method is demonstrated using a simulated shear-type structure with different levels of added measurement noise and a suite of ground motions. The results show that the proposed SHM method effectively and accurately identifies physical system parameters with up to 10% RMS added noise. The resulting damage indicators can robustly and clearly indicate structural condition over different earthquake events. |
| format | Article |
| id | doaj-art-ae75931c342140c8a0529b6a608149aa |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-ae75931c342140c8a0529b6a608149aa2025-02-03T01:03:12ZengWileyShock and Vibration1070-96221875-92032015-01-01201510.1155/2015/193136193136Nonlinear Regression Based Health Monitoring of Hysteretic Structures under Seismic ExcitationC. Xu0J. Geoffrey Chase1Geoffrey W. Rodgers2School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaDepartment of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New ZealandDepartment of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New ZealandThis paper presents a health monitoring method using measured hysteretic responses. Acceleration and infrequently measured displacement are integrated using a multirate Kalman filtering method to generate restoring force-displacement hysteresis loops. A linear/nonlinear regression analysis based two-step method is proposed to identify nonlinear system parameters. First, hysteresis loops are divided into loading/unloading half cycles. Multiple linear regression analysis is applied to separate linear and nonlinear half cycles. Preyielding stiffness and viscous damping coefficient are obtained in this step and used as known parameters in the second step. Then, nonlinear regression analysis is applied to identified nonlinear half cycles to yield nonlinear system parameters and two damage indicators: cumulative plastic deformation and residual deformation. These values are closely related to structural status and repair costs. The feasibility of the method is demonstrated using a simulated shear-type structure with different levels of added measurement noise and a suite of ground motions. The results show that the proposed SHM method effectively and accurately identifies physical system parameters with up to 10% RMS added noise. The resulting damage indicators can robustly and clearly indicate structural condition over different earthquake events.http://dx.doi.org/10.1155/2015/193136 |
| spellingShingle | C. Xu J. Geoffrey Chase Geoffrey W. Rodgers Nonlinear Regression Based Health Monitoring of Hysteretic Structures under Seismic Excitation Shock and Vibration |
| title | Nonlinear Regression Based Health Monitoring of Hysteretic Structures under Seismic Excitation |
| title_full | Nonlinear Regression Based Health Monitoring of Hysteretic Structures under Seismic Excitation |
| title_fullStr | Nonlinear Regression Based Health Monitoring of Hysteretic Structures under Seismic Excitation |
| title_full_unstemmed | Nonlinear Regression Based Health Monitoring of Hysteretic Structures under Seismic Excitation |
| title_short | Nonlinear Regression Based Health Monitoring of Hysteretic Structures under Seismic Excitation |
| title_sort | nonlinear regression based health monitoring of hysteretic structures under seismic excitation |
| url | http://dx.doi.org/10.1155/2015/193136 |
| work_keys_str_mv | AT cxu nonlinearregressionbasedhealthmonitoringofhystereticstructuresunderseismicexcitation AT jgeoffreychase nonlinearregressionbasedhealthmonitoringofhystereticstructuresunderseismicexcitation AT geoffreywrodgers nonlinearregressionbasedhealthmonitoringofhystereticstructuresunderseismicexcitation |