Gradual Failure of a Rainfall-Induced Creep-Type Landslide and an Application of Improved Integrated Monitoring System: A Case Study

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 22; p. 7409
• Main Authors: Guo, Jun, Meng, Fanxing, Guo, Jingwei
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 20.11.2024; MDPI
• Subjects: Case studies; Cracks; Deformation; Drainage; Emergency communications systems; failure mechanism; field exploration; Geology; Groundwater; Internet of Things; Landslides; Landslides & mudslides; LoRa Internet of Things; monitoring; Onsite; Precipitation; Rain; Rain and rainfall; rainfall-induced landslide; Rocks; Surface water; Wind; China; Hunan China; rainfall-induced landslide; LoRa Internet of Things; monitoring; field exploration; failure mechanism
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24227409

Landslides cause severe damage to life and property with a wide-ranging impact. Infiltration of rainfall is one of the significant factors leading to landslides. This paper reports on a phase creep landslide caused by long-term rainfall infiltration. A detailed geological survey of the landslide was conducted, and the deformation development pattern and mechanism of the landslide were analyzed in conjunction with climatic characteristics. Furthermore, reinforcement measures specific to the landslide area were proposed. To monitor the stability of the reinforced slope, a Beidou intelligent monitoring and warning system suitable for remote mountainous areas was developed. The system utilizes LoRa Internet of Things (IoT) technology to connect various monitoring components, integrating surface displacement, deep deformation, structural internal forces, and rainfall monitoring devices into a local IoT network. A data processing unit was established on site to achieve preliminary processing and automatic handling of monitoring data. The monitoring results indicate that the reinforced slope has generally stabilized, and the improved intelligent monitoring system has been able to continuously and accurately reflect the real-time working conditions of the slope. Over the two-year monitoring period, 13 early warnings were issued, with more than 90% of the warnings accurately corresponding to actual conditions, significantly improving the accuracy of early warnings. The research findings provide valuable experience and reference for the monitoring and warning of high slopes in mountainous areas.