Highway Slope

Scene Introduction

As highways extend into mountainous and hilly areas, landslides and collapses caused by extreme rainfall, earthquakes, and engineering excavation have become frequent, threatening safety. Slope instability is often hidden and sudden, making it difficult for manual inspections to detect deep creep, slip surfaces, and groundwater anomalies. Ruhr IoT utilizes intelligent sensors, IoT, and AI to monitor high-risk slopes and protective structures, achieving precise perception, risk location, tiered early warning, and coordinated response to surface displacement, deep displacement, cracks, and rainfall, ensuring highway safety.

Demand Analysis

Frequency and Severe Slope Disasters

Highways traverse mountains and valleys, making them highly susceptible to landslides and collapses caused by heavy rainfall, earthquakes, and slope cutting. These collapses result in road burial, bridge and culvert damage, and traffic disruptions, threatening lives, causing significant economic losses and adverse social impacts.

Hard to Identify Risk Factor

Slope instability begins with deep creep, slip surface penetration, and abnormal groundwater levels; initial deformation is weak and concealed. Manual inspections are insufficient to detect internal changes in a timely manner. By the time surface cracks appear, disaster is already imminent, and the early warning window for prevention has been lost.

Maintenance and Management Pressure

The number of high-risk slopes along mountain roads is surging, making maintenance a challenging task. Traditional periodic inspections rely on manual labor, are time-consuming, costly, and have many blind spots, creating a significant conflict with limited funding. This makes continuous, 24/7 monitoring and accurate early warning impossible, failing to meet the demands of safe operation.

Program Highlights

Three-Dimensional Real-Time Early Warning

24/7 monitoring of surface displacement, deep deformation, cracks, water levels, rainfall, and stress on high slopes such as high embankments and deep cuts. Multi-dimensional analysis enables early identification and prediction of landslides and collapses.
Intelligent Slope Early Warning System

A multi-source data-driven intelligent slope system is constructed, integrating monitoring, geological, and meteorological data. Based on models, it deduces deformation trends, identifies landslide patterns, dynamically assesses risks, and automatically generates reports and tiered early warnings, forming a self-learning closed loop and reducing response time.
Precise Maintenance Eliminates Hidden Dangers

Targeting potential hazards such as abnormal deformation, crack expansion, and rising water levels, precise solutions including drainage, anchoring, and load reduction are proposed based on risk evolution paths. This facilitates a shift from periodic inspections to data-driven, differentiated management, improving cost-effectiveness.
Empowering Scientific Decision-Making

Leveraging regional monitoring data and AI analysis, the system grasps slope development patterns, disaster-causing modes, and evolution trends. This provides quantitative data for planning, route selection, reconstruction, traffic maintenance, and disaster prevention, empowering intelligent decision-making throughout the entire lifecycle.

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No. 53 Qiyi Lane, Wuchang Sub-district, Yuhang District, Hangzhou City, Ruhr IoT Headquarters Park

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