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Beijing Sewer Corrosion Monitoring: Fusing Digital Twin and Predictive AI for Urban Infrastructure Resilience

Predictive analytics and IoT reduce pump failures by over 20%, increase production up to 10%, and cut labor costs by 40% in extreme oilfield environments.

To modernize the corrosion management of Beijing’s aging sewer infrastructure by building a real-time, predictive monitoring system powered by Digital Twin technology and AI. The goal was to go beyond detection—enabling prediction, automated mitigation, and long-term asset protection.

Multi-Sensor Field Network

Deployed a network of sensors measuring pH, flow velocity, pressure, temperature, and gas concentration at corrosion-prone nodes.

Digital Twin Platform Integration

Created a dynamic, geospatial model of the underground sewer grid, continuously updated with real-time field telemetry and inspection data.

AI-Powered Corrosion Forecasting

AI-Powered Corrosion Forecasting Built hybrid corrosion models combining electrochemical theory and machine learning to forecast degradation rates based on material, flow, and environmental conditions.

Smart Cathodic Protection Control

Enabled closed-loop feedback to adjust cathodic protection current levels in response to predicted corrosion acceleration.

Achieved Results

01

40% reduction in emergency maintenance dispatches

02

5–7 year extension in projected lifespan of high-risk pipeline segments

03

60% faster response to corrosion events with AI-prioritized alerts

04

Improved ESG compliance with digitized, traceable corrosion logs and minimized environmental impact from dig-and-repair methods

The Real-World Challenge That Drove Our AI Breakthrough

Our journey began with a real-world need—something no off-the-shelf solution could fix. This complexity pushed us to design a tailored AI model capable of adapting to unpredictable variables in dynamic environments.

Fragmented or outdated records of underground pipe materials and layouts

Variable corrosion patterns due to mixed materials, flow rates, and environmental conditions

Signal drift and data loss in high-humidity, high-acidity environments

Approaches

Digitized historical sewer maps using GIS and 3D pipeline reconstruction

Clustered sewer segments by environmental and material risk using unsupervised ML

Developed corrosion forecasting models using:
- Sensor inputs
- Pipe material characteristics
- Environmental context

Integrated with cathodic protection controllers for real-time mitigation

Benefits & Outcomes

01

Reduced unnecessary site visits, enabling focused, high-value interventions

02

Early detection prevented leaks, blockages, and health risks from sewer failures

03

Decreased emergency repair costs and deferred major replacements

04

Lower emissions and excavation from optimized inspection and repair routes

05

System architecture supports expansion into other municipal networks like water supply, gas, and district heating