Effective Processing of Radar Data for Bridge Damage Detection
This paper presents the practical results of the evaluation of the data obtained by using ground-based radar interferometer during measurements carried out on bridge structures. Due to the nature of the objects studied, the authors proposed a comprehensive method of data analysis, which identifies w...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2019/2621092 |
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| _version_ | 1832552958605131776 |
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| author | Tomasz Owerko Przemysław Kuras |
| author_facet | Tomasz Owerko Przemysław Kuras |
| author_sort | Tomasz Owerko |
| collection | DOAJ |
| description | This paper presents the practical results of the evaluation of the data obtained by using ground-based radar interferometer during measurements carried out on bridge structures. Due to the nature of the objects studied, the authors proposed a comprehensive method of data analysis, which identifies whether the passage of the vehicle did not damage the bridge. The effective use of vehicles as a source of bridge excitation allowed us to first develop a method for determining the damping parameters resistant to potentially occurring beating frequencies. As a result, it is possible to determine these subsets of data registered with radar, for which it is possible to assume compliance with linear systems. This type of data, often omitted in other works, forms the basis for the second important element of the research—an algorithm based on the ARMA model supporting defect detection. The optimization of the performed calculations, in particular the proposed optimal ARMA model order, the method of fault identification based on the DSF parameter, or fault identification based on a nonmetrical Cook’s distance leads to a robust and scalable method. The method’s low computational complexity allows for implementation in real-time solutions. In addition, the distribution of errors and the sensitivity of classifiers based on the DSF parameter and Cook’s distances leaving them will enable the automation of the classification process using machine learning. The proposed method is universal; in particular, it can be used for radar interferometry methods because it is resistant to potentially variable environmental conditions. |
| format | Article |
| id | doaj-art-ff80798c33514769826a74aea264cdda |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-ff80798c33514769826a74aea264cdda2025-02-03T05:57:27ZengWileyShock and Vibration1070-96221875-92032019-01-01201910.1155/2019/26210922621092Effective Processing of Radar Data for Bridge Damage DetectionTomasz Owerko0Przemysław Kuras1Department of Surveying Engineering and Civil Engineering, AGH University of Science and Technology, Kraków 30-059, PolandDepartment of Surveying Engineering and Civil Engineering, AGH University of Science and Technology, Kraków 30-059, PolandThis paper presents the practical results of the evaluation of the data obtained by using ground-based radar interferometer during measurements carried out on bridge structures. Due to the nature of the objects studied, the authors proposed a comprehensive method of data analysis, which identifies whether the passage of the vehicle did not damage the bridge. The effective use of vehicles as a source of bridge excitation allowed us to first develop a method for determining the damping parameters resistant to potentially occurring beating frequencies. As a result, it is possible to determine these subsets of data registered with radar, for which it is possible to assume compliance with linear systems. This type of data, often omitted in other works, forms the basis for the second important element of the research—an algorithm based on the ARMA model supporting defect detection. The optimization of the performed calculations, in particular the proposed optimal ARMA model order, the method of fault identification based on the DSF parameter, or fault identification based on a nonmetrical Cook’s distance leads to a robust and scalable method. The method’s low computational complexity allows for implementation in real-time solutions. In addition, the distribution of errors and the sensitivity of classifiers based on the DSF parameter and Cook’s distances leaving them will enable the automation of the classification process using machine learning. The proposed method is universal; in particular, it can be used for radar interferometry methods because it is resistant to potentially variable environmental conditions.http://dx.doi.org/10.1155/2019/2621092 |
| spellingShingle | Tomasz Owerko Przemysław Kuras Effective Processing of Radar Data for Bridge Damage Detection Shock and Vibration |
| title | Effective Processing of Radar Data for Bridge Damage Detection |
| title_full | Effective Processing of Radar Data for Bridge Damage Detection |
| title_fullStr | Effective Processing of Radar Data for Bridge Damage Detection |
| title_full_unstemmed | Effective Processing of Radar Data for Bridge Damage Detection |
| title_short | Effective Processing of Radar Data for Bridge Damage Detection |
| title_sort | effective processing of radar data for bridge damage detection |
| url | http://dx.doi.org/10.1155/2019/2621092 |
| work_keys_str_mv | AT tomaszowerko effectiveprocessingofradardataforbridgedamagedetection AT przemysławkuras effectiveprocessingofradardataforbridgedamagedetection |