Kalman Filters in Geotechnical Monitoring of Ground Subsidence Using Data from MEMS Sensors
The fast development of wireless sensor networks and MEMS make it possible to set up today real-time wireless geotechnical monitoring. To handle interferences and noises from the output data, Kalman filter can be selected as a method to achieve a more realistic estimate of the observations. In this...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-07-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/7/1109 |
| _version_ | 1811302313419079680 |
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| author | Cheng Li Rafig Azzam Tomás M. Fernández-Steeger |
| author_facet | Cheng Li Rafig Azzam Tomás M. Fernández-Steeger |
| author_sort | Cheng Li |
| collection | DOAJ |
| description | The fast development of wireless sensor networks and MEMS make it possible to set up today real-time wireless geotechnical monitoring. To handle interferences and noises from the output data, Kalman filter can be selected as a method to achieve a more realistic estimate of the observations. In this paper, a one-day wireless measurement using accelerometers and inclinometers was deployed on top of a tunnel section under construction in order to monitor ground subsidence. The normal vectors of the sensors were firstly obtained with the help of rotation matrices, and then be projected to the plane of longitudinal section, by which the dip angles over time would be obtained via a trigonometric function. Finally, a centralized Kalman filter was applied to estimate the tilt angles of the sensor nodes based on the data from the embedded accelerometer and the inclinometer. Comparing the results from two sensor nodes deployed away and on the track respectively, the passing of the tunnel boring machine can be identified from unusual performances. Using this method, the ground settlement due to excavation can be measured and a real-time monitoring of ground subsidence can be realized. |
| first_indexed | 2024-04-13T07:25:50Z |
| format | Article |
| id | doaj.art-8f600904046b408e93034b081e504bee |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T07:25:50Z |
| publishDate | 2016-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8f600904046b408e93034b081e504bee2022-12-22T02:56:29ZengMDPI AGSensors1424-82202016-07-01167110910.3390/s16071109s16071109Kalman Filters in Geotechnical Monitoring of Ground Subsidence Using Data from MEMS SensorsCheng Li0Rafig Azzam1Tomás M. Fernández-Steeger2Chengdu Engineering Corporation Limited, Chengdu 610072, ChinaDepartment of Engineering Geology and Hydrogeology, RWTH Aachen University, Aachen 52064, GermanyDepartment of Applied Geosciences, TU Berlin University, Berlin 10587, GermanyThe fast development of wireless sensor networks and MEMS make it possible to set up today real-time wireless geotechnical monitoring. To handle interferences and noises from the output data, Kalman filter can be selected as a method to achieve a more realistic estimate of the observations. In this paper, a one-day wireless measurement using accelerometers and inclinometers was deployed on top of a tunnel section under construction in order to monitor ground subsidence. The normal vectors of the sensors were firstly obtained with the help of rotation matrices, and then be projected to the plane of longitudinal section, by which the dip angles over time would be obtained via a trigonometric function. Finally, a centralized Kalman filter was applied to estimate the tilt angles of the sensor nodes based on the data from the embedded accelerometer and the inclinometer. Comparing the results from two sensor nodes deployed away and on the track respectively, the passing of the tunnel boring machine can be identified from unusual performances. Using this method, the ground settlement due to excavation can be measured and a real-time monitoring of ground subsidence can be realized.http://www.mdpi.com/1424-8220/16/7/1109Kalman filterground subsidencerotation matricesaccelerometerinclinometer |
| spellingShingle | Cheng Li Rafig Azzam Tomás M. Fernández-Steeger Kalman Filters in Geotechnical Monitoring of Ground Subsidence Using Data from MEMS Sensors Sensors Kalman filter ground subsidence rotation matrices accelerometer inclinometer |
| title | Kalman Filters in Geotechnical Monitoring of Ground Subsidence Using Data from MEMS Sensors |
| title_full | Kalman Filters in Geotechnical Monitoring of Ground Subsidence Using Data from MEMS Sensors |
| title_fullStr | Kalman Filters in Geotechnical Monitoring of Ground Subsidence Using Data from MEMS Sensors |
| title_full_unstemmed | Kalman Filters in Geotechnical Monitoring of Ground Subsidence Using Data from MEMS Sensors |
| title_short | Kalman Filters in Geotechnical Monitoring of Ground Subsidence Using Data from MEMS Sensors |
| title_sort | kalman filters in geotechnical monitoring of ground subsidence using data from mems sensors |
| topic | Kalman filter ground subsidence rotation matrices accelerometer inclinometer |
| url | http://www.mdpi.com/1424-8220/16/7/1109 |
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