Abstract
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.
Highlights
Numerous slope failures have been recorded with respect to human activities, making slope stability analysis a continued and interesting topic in the field of geotechnical engineering [1,2]
In order to overcome the serious and complex challenges of geohazards induced by construction work, monitoring techniques and early-warning methods have become of interest in dealing with slope failure [3,4,5,6]
Images of a slope can be obtained by high-resolution cameras periodically and the entire slope stability and displacement of feature points can be analyzed by image recognition technology [21,22,23,24,25,26]
Summary
Numerous slope failures have been recorded with respect to human activities, making slope stability analysis a continued and interesting topic in the field of geotechnical engineering [1,2]. The acquisition of environmental factors, spatial analysis and data management of deformation prediction of unstable slopes were mainly based on monitoring technology Despite these studies, the application of artificial intelligence models for deformation prediction is still a new multidisciplinary research field. This study proposes a comprehensive monitoring system based on the IoT and other devices, such as GPS benchmarks, inclinometers, tilt sensors, crack meters, etc., to control the safety of the excavated unstable slope in the emergency stage It introduces an artificial intelligence algorithm based on time series and probabilistic forecasting for the prediction of deformation based on real-time displacement and rainfall data. The applicability and accuracy of the monitoring system and prediction model are verified against the field data from an excavated slope along the Qili connecting line of the Hangchang high-speed motorway in Quzhou City, Zhejiang Province, China.
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