Novel Method for Bridge Structural Full-Field Displacement Monitoring and Damage Identification
Currently, measurement points in bridge structural health monitoring are limited. Consequently, structural damage identification is challenging due to sparse monitoring data. Hence, a structural full-field displacement monitoring and damage identification method under natural texture conditions is p...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/3/1756 |
| _version_ | 1797625137052254208 |
|---|---|
| author | Xin Duan Xi Chu Weizhu Zhu Zhixiang Zhou Rui Luo Junhao Meng |
| author_facet | Xin Duan Xi Chu Weizhu Zhu Zhixiang Zhou Rui Luo Junhao Meng |
| author_sort | Xin Duan |
| collection | DOAJ |
| description | Currently, measurement points in bridge structural health monitoring are limited. Consequently, structural damage identification is challenging due to sparse monitoring data. Hence, a structural full-field displacement monitoring and damage identification method under natural texture conditions is proposed in this work. Firstly, the feature points of a structure were extracted via image scale-invariant feature transform. Then, the mathematical model was analyzed respecting the relative position change of the feature points before and after deformation, and a calculation theory was proposed for the structure’s full-field displacement vector (FFDV). Next, a test beam was constructed to obtain the FFDV calculation results for the beam under different damage conditions. Validation results showed that the maximum length error of the FFDV was 0.48 mm, while the maximum angle error was 0.82°. The FFDV monitoring results for the test beam showed that the rotation angle of the displacement vector at the damage location presented abnormal characteristics. Additionally, a damage identification index was proposed for the rotation-angle change rate. Based on the validation test, the index was proven to be sensitive to the damage location. Finally, a structural damage identification program was proposed based on the FFDV monitoring results. The obtained results will help to expand structural health monitoring data and fundamentally solve damage identification issues arising from sparse monitoring data. This study is the first to implement structural full-field displacement monitoring under natural texture conditions. The proposed method exhibits outstanding economic benefits, efficiency, and visualization advantages compared with the conventional single-point monitoring method. |
| first_indexed | 2024-03-11T09:52:31Z |
| format | Article |
| id | doaj.art-e034de7912134a4a842a6665dc3a8fd1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T09:52:31Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e034de7912134a4a842a6665dc3a8fd12023-11-16T16:10:00ZengMDPI AGApplied Sciences2076-34172023-01-01133175610.3390/app13031756Novel Method for Bridge Structural Full-Field Displacement Monitoring and Damage IdentificationXin Duan0Xi Chu1Weizhu Zhu2Zhixiang Zhou3Rui Luo4Junhao Meng5College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, ChinaCollege of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, ChinaCollege of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, ChinaCollege of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, ChinaState Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaState Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaCurrently, measurement points in bridge structural health monitoring are limited. Consequently, structural damage identification is challenging due to sparse monitoring data. Hence, a structural full-field displacement monitoring and damage identification method under natural texture conditions is proposed in this work. Firstly, the feature points of a structure were extracted via image scale-invariant feature transform. Then, the mathematical model was analyzed respecting the relative position change of the feature points before and after deformation, and a calculation theory was proposed for the structure’s full-field displacement vector (FFDV). Next, a test beam was constructed to obtain the FFDV calculation results for the beam under different damage conditions. Validation results showed that the maximum length error of the FFDV was 0.48 mm, while the maximum angle error was 0.82°. The FFDV monitoring results for the test beam showed that the rotation angle of the displacement vector at the damage location presented abnormal characteristics. Additionally, a damage identification index was proposed for the rotation-angle change rate. Based on the validation test, the index was proven to be sensitive to the damage location. Finally, a structural damage identification program was proposed based on the FFDV monitoring results. The obtained results will help to expand structural health monitoring data and fundamentally solve damage identification issues arising from sparse monitoring data. This study is the first to implement structural full-field displacement monitoring under natural texture conditions. The proposed method exhibits outstanding economic benefits, efficiency, and visualization advantages compared with the conventional single-point monitoring method.https://www.mdpi.com/2076-3417/13/3/1756bridge structurecomputer visionstructural health monitoringdisplacement vectorsafety evaluationdamage identification |
| spellingShingle | Xin Duan Xi Chu Weizhu Zhu Zhixiang Zhou Rui Luo Junhao Meng Novel Method for Bridge Structural Full-Field Displacement Monitoring and Damage Identification Applied Sciences bridge structure computer vision structural health monitoring displacement vector safety evaluation damage identification |
| title | Novel Method for Bridge Structural Full-Field Displacement Monitoring and Damage Identification |
| title_full | Novel Method for Bridge Structural Full-Field Displacement Monitoring and Damage Identification |
| title_fullStr | Novel Method for Bridge Structural Full-Field Displacement Monitoring and Damage Identification |
| title_full_unstemmed | Novel Method for Bridge Structural Full-Field Displacement Monitoring and Damage Identification |
| title_short | Novel Method for Bridge Structural Full-Field Displacement Monitoring and Damage Identification |
| title_sort | novel method for bridge structural full field displacement monitoring and damage identification |
| topic | bridge structure computer vision structural health monitoring displacement vector safety evaluation damage identification |
| url | https://www.mdpi.com/2076-3417/13/3/1756 |
| work_keys_str_mv | AT xinduan novelmethodforbridgestructuralfullfielddisplacementmonitoringanddamageidentification AT xichu novelmethodforbridgestructuralfullfielddisplacementmonitoringanddamageidentification AT weizhuzhu novelmethodforbridgestructuralfullfielddisplacementmonitoringanddamageidentification AT zhixiangzhou novelmethodforbridgestructuralfullfielddisplacementmonitoringanddamageidentification AT ruiluo novelmethodforbridgestructuralfullfielddisplacementmonitoringanddamageidentification AT junhaomeng novelmethodforbridgestructuralfullfielddisplacementmonitoringanddamageidentification |