Deformation Prediction of Unstable Slopes Based on Real-Time Monitoring and DeepAR Model
With increased urbanization, accidents related to slope instability are frequently encountered in construction sites. The deformation and failure mechanism of a landslide is a complex dynamic process, which seriously threatens people’s lives and property. Currently, prediction and early warning of a...
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| Format: | Article |
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MDPI AG
2020-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/1/14 |
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| author | Mei Dong Hongyu Wu Hui Hu Rafig Azzam Liang Zhang Zengrong Zheng Xiaonan Gong |
| author_facet | Mei Dong Hongyu Wu Hui Hu Rafig Azzam Liang Zhang Zengrong Zheng Xiaonan Gong |
| author_sort | Mei Dong |
| collection | DOAJ |
| description | With increased urbanization, accidents related to slope instability are frequently encountered in construction sites. The deformation and failure mechanism of a landslide is a complex dynamic process, which seriously threatens people’s lives and property. Currently, prediction and early warning of a landslide can be effectively performed by using Internet of Things (IoT) technology to monitor the landslide deformation in real time and an artificial intelligence algorithm to predict the deformation trend. However, if a slope failure occurs during the construction period, the builders and decision-makers find it challenging to effectively apply IoT technology to monitor the emergency and assist in proposing treatment measures. Moreover, for projects during operation (e.g., a motorway in a mountainous area), no recognized artificial intelligence algorithm exists that can forecast the deformation of steep slopes using the huge data obtained from monitoring devices. In this context, this paper introduces a real-time wireless monitoring system with multiple sensors for retrieving high-frequency overall data that can describe the deformation feature of steep slopes. The system was installed in the Qili connecting line of a motorway in Zhejiang Province, China, to provide a technical support for the design and implementation of safety solutions for the steep slopes. Most of the devices were retained to monitor the slopes even after construction. The machine learning Probabilistic Forecasting with Autoregressive Recurrent Networks (DeepAR) model based on time series and probabilistic forecasting was introduced into the project to predict the slope displacement. The predictive accuracy of the DeepAR model was verified by the mean absolute error, the root mean square error and the goodness of fit. This study demonstrates that the presented monitoring system and the introduced predictive model had good safety control ability during construction and good prediction accuracy during operation. The proposed approach will be helpful to assess the safety of excavated slopes before constructing new infrastructures. |
| first_indexed | 2024-03-10T13:51:23Z |
| format | Article |
| id | doaj.art-35161b80b17d45e2a05b4a9c6ff3c9d7 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T13:51:23Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-35161b80b17d45e2a05b4a9c6ff3c9d72023-11-21T02:06:05ZengMDPI AGSensors1424-82202020-12-012111410.3390/s21010014Deformation Prediction of Unstable Slopes Based on Real-Time Monitoring and DeepAR ModelMei Dong0Hongyu Wu1Hui Hu2Rafig Azzam3Liang Zhang4Zengrong Zheng5Xiaonan Gong6College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, ChinaCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, ChinaHangzhou Ruhr Technology Co., Ltd., Hangzhou 310023, ChinaSino-German Resources Environment and Geo-Hazards Research Center, North China University of Water Resources and Electric Power, Zhengzhou 450056, ChinaDepartment of Engineering Geology and Hydrogeology, RWTH-Aachen University, 52074 Aachen, GermanyHangzhou Ruhr Technology Co., Ltd., Hangzhou 310023, ChinaCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, ChinaWith increased urbanization, accidents related to slope instability are frequently encountered in construction sites. The deformation and failure mechanism of a landslide is a complex dynamic process, which seriously threatens people’s lives and property. Currently, prediction and early warning of a landslide can be effectively performed by using Internet of Things (IoT) technology to monitor the landslide deformation in real time and an artificial intelligence algorithm to predict the deformation trend. However, if a slope failure occurs during the construction period, the builders and decision-makers find it challenging to effectively apply IoT technology to monitor the emergency and assist in proposing treatment measures. Moreover, for projects during operation (e.g., a motorway in a mountainous area), no recognized artificial intelligence algorithm exists that can forecast the deformation of steep slopes using the huge data obtained from monitoring devices. In this context, this paper introduces a real-time wireless monitoring system with multiple sensors for retrieving high-frequency overall data that can describe the deformation feature of steep slopes. The system was installed in the Qili connecting line of a motorway in Zhejiang Province, China, to provide a technical support for the design and implementation of safety solutions for the steep slopes. Most of the devices were retained to monitor the slopes even after construction. The machine learning Probabilistic Forecasting with Autoregressive Recurrent Networks (DeepAR) model based on time series and probabilistic forecasting was introduced into the project to predict the slope displacement. The predictive accuracy of the DeepAR model was verified by the mean absolute error, the root mean square error and the goodness of fit. This study demonstrates that the presented monitoring system and the introduced predictive model had good safety control ability during construction and good prediction accuracy during operation. The proposed approach will be helpful to assess the safety of excavated slopes before constructing new infrastructures.https://www.mdpi.com/1424-8220/21/1/14landslidesmonitoring systemdeformation predictionearly warningDeepAR model |
| spellingShingle | Mei Dong Hongyu Wu Hui Hu Rafig Azzam Liang Zhang Zengrong Zheng Xiaonan Gong Deformation Prediction of Unstable Slopes Based on Real-Time Monitoring and DeepAR Model Sensors landslides monitoring system deformation prediction early warning DeepAR model |
| title | Deformation Prediction of Unstable Slopes Based on Real-Time Monitoring and DeepAR Model |
| title_full | Deformation Prediction of Unstable Slopes Based on Real-Time Monitoring and DeepAR Model |
| title_fullStr | Deformation Prediction of Unstable Slopes Based on Real-Time Monitoring and DeepAR Model |
| title_full_unstemmed | Deformation Prediction of Unstable Slopes Based on Real-Time Monitoring and DeepAR Model |
| title_short | Deformation Prediction of Unstable Slopes Based on Real-Time Monitoring and DeepAR Model |
| title_sort | deformation prediction of unstable slopes based on real time monitoring and deepar model |
| topic | landslides monitoring system deformation prediction early warning DeepAR model |
| url | https://www.mdpi.com/1424-8220/21/1/14 |
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