Distributed sensors and neural network driven building earthquake resistance mechanism
The anti-seismic support and hanger are firmly connected to the building structure and are anti-seismic support equipment with seismic force as the main load. Real-time and accurate acquisition of the service status of the seismic support and hanger to check and judge whether the seismic support and...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2022-11-01
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| Series: | AIMS Geosciences |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/geosci.2022040?viewType=HTML |
| _version_ | 1797958216505622528 |
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| author | Pingping Chen Mingyang Qi Long Chen |
| author_facet | Pingping Chen Mingyang Qi Long Chen |
| author_sort | Pingping Chen |
| collection | DOAJ |
| description | The anti-seismic support and hanger are firmly connected to the building structure and are anti-seismic support equipment with seismic force as the main load. Real-time and accurate acquisition of the service status of the seismic support and hanger to check and judge whether the seismic support and hanger are in a normal working state is of great significance for practical engineering applications. In this paper, based on distributed sensor technology, a set of intelligent monitoring systems for seismic support and hanger of buildings is established. The sensing equipment installed on the seismic support and hanger senses the signal, and then the data collection, storage and processing are used to accurately judge the seismic support and hanger. Service performance status. To effectively fuse multi-source data in distributed sensor environment, an improved method based on wavelet and neural network data fusion is proposed. Compared with the existing methods, the experimental results show that the proposed method has good robustness. Besides, it has better performance in building seismic multi-source monitoring data fusion and is less affected by the data overlap ratio. |
| first_indexed | 2024-04-11T00:16:55Z |
| format | Article |
| id | doaj.art-d50d09d9f1084269bbdaa32acfc03556 |
| institution | Directory Open Access Journal |
| issn | 2471-2132 |
| language | English |
| last_indexed | 2024-04-11T00:16:55Z |
| publishDate | 2022-11-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Geosciences |
| spelling | doaj.art-d50d09d9f1084269bbdaa32acfc035562023-01-09T01:51:34ZengAIMS PressAIMS Geosciences2471-21322022-11-018471873010.3934/geosci.2022040Distributed sensors and neural network driven building earthquake resistance mechanismPingping Chen0Mingyang Qi1Long Chen 21. Liao Yuan Vocational Technical College, Liaoyuan 136200, China2. Jilin Agricultural Science and Technology University, Jilin 132101, China1. Liao Yuan Vocational Technical College, Liaoyuan 136200, ChinaThe anti-seismic support and hanger are firmly connected to the building structure and are anti-seismic support equipment with seismic force as the main load. Real-time and accurate acquisition of the service status of the seismic support and hanger to check and judge whether the seismic support and hanger are in a normal working state is of great significance for practical engineering applications. In this paper, based on distributed sensor technology, a set of intelligent monitoring systems for seismic support and hanger of buildings is established. The sensing equipment installed on the seismic support and hanger senses the signal, and then the data collection, storage and processing are used to accurately judge the seismic support and hanger. Service performance status. To effectively fuse multi-source data in distributed sensor environment, an improved method based on wavelet and neural network data fusion is proposed. Compared with the existing methods, the experimental results show that the proposed method has good robustness. Besides, it has better performance in building seismic multi-source monitoring data fusion and is less affected by the data overlap ratio.https://www.aimspress.com/article/doi/10.3934/geosci.2022040?viewType=HTMLbuilding aseismicitydistributed sensorsdata fusionneural network |
| spellingShingle | Pingping Chen Mingyang Qi Long Chen Distributed sensors and neural network driven building earthquake resistance mechanism AIMS Geosciences building aseismicity distributed sensors data fusion neural network |
| title | Distributed sensors and neural network driven building earthquake resistance mechanism |
| title_full | Distributed sensors and neural network driven building earthquake resistance mechanism |
| title_fullStr | Distributed sensors and neural network driven building earthquake resistance mechanism |
| title_full_unstemmed | Distributed sensors and neural network driven building earthquake resistance mechanism |
| title_short | Distributed sensors and neural network driven building earthquake resistance mechanism |
| title_sort | distributed sensors and neural network driven building earthquake resistance mechanism |
| topic | building aseismicity distributed sensors data fusion neural network |
| url | https://www.aimspress.com/article/doi/10.3934/geosci.2022040?viewType=HTML |
| work_keys_str_mv | AT pingpingchen distributedsensorsandneuralnetworkdrivenbuildingearthquakeresistancemechanism AT mingyangqi distributedsensorsandneuralnetworkdrivenbuildingearthquakeresistancemechanism AT longchen distributedsensorsandneuralnetworkdrivenbuildingearthquakeresistancemechanism |