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Scene Introduction Demand Analysis Program Highlights Core Products Typical Case

Scene Introduction

Ruhr IoT Bridge Structural Health Monitoring System leverages the company's extensive experience in industrial IoT and structural diagnostics, integrating high-precision intelligent sensing, edge computing, and digital twin technologies. The system enables full lifecycle monitoring of bridges, from construction to operational maintenance. By capturing environmental loads (wind, rain, temperature, humidity) and structural responses (displacement, strain, vibration) in real time, it provides managers with scientific risk assessment and decision support.

Demand Analysis

Structural Instability Happens

Due to environmental degradation, traffic loads, wind, earthquakes, and human factors, bridge structures can suffer varying degrees of damage and deterioration. In extreme cases, this may lead to catastrophic events.

Overloading

Overloading increases the fatigue stress range of bridges, aggravates structural damage, and can lead to catastrophic failure when reaching a certain level.

Outdated Maintenance Management

Maintenance management relies primarily on regular manual inspections, with relatively long intervals between consecutive inspections.

Program Highlights

Real-time Safety Sensing

Real-time Security Situation Awareness involves continuously monitoring the displacement, deformation, and vibration of structural stress conditions, as well as environmental factors such as wind and rain, to timely assess the safety conditions of bridges.
Early Warning System For Abnormal Situations

By conducting real-time analysis on the monitoring data of the bridge, the system will assess the bridge's health condition, and upon detecting any abnormalities, send notifications to relevant person.
Maintenance Guidance for Higher Efficiency

Based on the assessment results, provide guidance to assist bridge managers in developing more efficient plans for maintenance .
Support for Anomaly Data Tracing

By building the system, once special or abnormal situations occur, it is possible to trace back through the closed-loop management of data and processes to identify incident causes and reinforce responsibilities.