Stability Monitoring of Soil Slope via Big Data Technology in the Context of the Internet of Things

• Published in: International journal of grid and high performance computing Vol. 17; no. 1; pp. 1 - 18
• Main Authors: Xu, Jin, Zhao, Yanna
• Format: Journal Article
• Language: English
• Published: Hershey IGI Global 01.01.2025
• Subjects: Accuracy; Algorithms; Artificial intelligence; Big Data; Cloud computing; Context; Crack propagation; Damage; Data analysis; Datasets; Deformation; Disasters; Emergency communications systems; Engineering; Friction; Geology; Internal friction; Internet of Things; Landslides; Landslides & mudslides; Machine learning; Monitoring; Neural networks; Remote sensing; Satellite communications; Sensors; Slope stability; Soil investigations; Soil stability; Soils; Surface water; Technology; Trends
• ISSN: 1938-0259; 1938-0267; 1938-0267
• DOI: 10.4018/IJGHPC.373908

The stability of rock and soil masses has become increasingly critical due to large-scale expansion and landfilling, resulting in frequent landslides that pose significant threats to safety and property. Consequently, soil slope stability monitoring is essential. To mitigate slope instability risks, this study investigates soil slope stability monitoring using big data technology within the context of the internet of things. The research examines slope monitoring techniques and summarizes various methods for detecting slope deformation. By monitoring displacement and deformation, the operational status of slopes can be assessed, safety evaluated, disasters prevented, and adverse social impacts avoided. Collected geological data support the development of slope models, enabling analysis under different damage conditions. The findings indicate that 50% damage corresponds to a warning threshold, while 80% damage triggers an alarm. Simulation results show that slope stability increases with higher internal friction angle and cohesion but decreases as the slope angle increases.