Analysis of Bridge Health Detection Based on Data Fusion
By integrating rough set theory and neural network theory, this study combined their advantages. Drawing on the existing theoretical results for bridge influencing factors, a method for numerical simulation and data fusion was used in the application of multifactor data fusion for cable-stayed bridg...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2022-01-01
|
| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/6893160 |
| _version_ | 1797995268810997760 |
|---|---|
| author | Ying Chen Jiuhong Zhang Yanfeng Li Jialong Li |
| author_facet | Ying Chen Jiuhong Zhang Yanfeng Li Jialong Li |
| author_sort | Ying Chen |
| collection | DOAJ |
| description | By integrating rough set theory and neural network theory, this study combined their advantages. Drawing on the existing theoretical results for bridge influencing factors, a method for numerical simulation and data fusion was used in the application of multifactor data fusion for cable-stayed bridge safety evaluation. Based on studying existing bridge safety evaluation methods, a neural network and rough set theory were combined to perform a safety evaluation of PC cable-stayed bridge cables, which provided a new means for bridge safety evaluation. First, a cable-stayed bridge in Shenyang was used as the engineering background, the safety level of its cables was divided into five levels, and a safety evaluation database was established, clustered by a Kohonen neural network. This provided specific evaluation indicators corresponding to the five safety levels. A rough neural network algorithm integrating the rough set and neural network was applied to data fusion of the database, with the attribute-reduction function of the rough set used to reduce the input dimension of the neural network. Conclusions. The neural network was then trained and the resulting trained network was applied to the safety evaluation of the cables of the cable-stayed bridge. Four specific attribute index values, corresponding to the bridge cables, were directly input to obtain the safety status of the bridge and provide corresponding management suggestions. |
| first_indexed | 2024-04-11T09:57:41Z |
| format | Article |
| id | doaj.art-d246a587955c437caac12a3ae23b6fef |
| institution | Directory Open Access Journal |
| issn | 1687-8094 |
| language | English |
| last_indexed | 2024-04-11T09:57:41Z |
| publishDate | 2022-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj.art-d246a587955c437caac12a3ae23b6fef2022-12-22T04:30:30ZengHindawi LimitedAdvances in Civil Engineering1687-80942022-01-01202210.1155/2022/6893160Analysis of Bridge Health Detection Based on Data FusionYing Chen0Jiuhong Zhang1Yanfeng Li2Jialong Li3JangHo ArchitectureJangHo ArchitectureSchool of Transportation and Geomatics EngineeringSchool of Transportation and Geomatics EngineeringBy integrating rough set theory and neural network theory, this study combined their advantages. Drawing on the existing theoretical results for bridge influencing factors, a method for numerical simulation and data fusion was used in the application of multifactor data fusion for cable-stayed bridge safety evaluation. Based on studying existing bridge safety evaluation methods, a neural network and rough set theory were combined to perform a safety evaluation of PC cable-stayed bridge cables, which provided a new means for bridge safety evaluation. First, a cable-stayed bridge in Shenyang was used as the engineering background, the safety level of its cables was divided into five levels, and a safety evaluation database was established, clustered by a Kohonen neural network. This provided specific evaluation indicators corresponding to the five safety levels. A rough neural network algorithm integrating the rough set and neural network was applied to data fusion of the database, with the attribute-reduction function of the rough set used to reduce the input dimension of the neural network. Conclusions. The neural network was then trained and the resulting trained network was applied to the safety evaluation of the cables of the cable-stayed bridge. Four specific attribute index values, corresponding to the bridge cables, were directly input to obtain the safety status of the bridge and provide corresponding management suggestions.http://dx.doi.org/10.1155/2022/6893160 |
| spellingShingle | Ying Chen Jiuhong Zhang Yanfeng Li Jialong Li Analysis of Bridge Health Detection Based on Data Fusion Advances in Civil Engineering |
| title | Analysis of Bridge Health Detection Based on Data Fusion |
| title_full | Analysis of Bridge Health Detection Based on Data Fusion |
| title_fullStr | Analysis of Bridge Health Detection Based on Data Fusion |
| title_full_unstemmed | Analysis of Bridge Health Detection Based on Data Fusion |
| title_short | Analysis of Bridge Health Detection Based on Data Fusion |
| title_sort | analysis of bridge health detection based on data fusion |
| url | http://dx.doi.org/10.1155/2022/6893160 |
| work_keys_str_mv | AT yingchen analysisofbridgehealthdetectionbasedondatafusion AT jiuhongzhang analysisofbridgehealthdetectionbasedondatafusion AT yanfengli analysisofbridgehealthdetectionbasedondatafusion AT jialongli analysisofbridgehealthdetectionbasedondatafusion |