In a major leap forward for infrastructure maintenance, researchers from the Indian Institute of Technology (IIT) Mandi have developed a method to monitor the health of ageing bridges using traffic data and digital modelling. This innovative approach allows real-time monitoring of bridges by placing sensors in strategically vulnerable spots, enabling quick interventions without the need for extensive equipment or traffic disruption.
The research team, which includes Dr. Subhamoy Sen, IIT Mandi and his research scholar, Eshwar Kuncham, has addressed the critical issue of fatigue-induced damage and gradual deterioration in ageing bridges. Their findings, published in the journal Structural Health Monitoring, focus on a more targeted method of bridge assessment by honing in on critical areas rather than the entire structure. This significantly enhances the efficiency and safety of bridge maintenance while reducing costs.
Their novel approach creates a digital model of a bridge, a detailed virtual replica built after an initial study of the physical structure. This model predicts how traffic patterns over time impact different parts of the bridge, enabling experts to identify zones most at risk of damage. Sensors are then placed in these critical areas, capturing real-time data on stress and vibrations as traffic flows over the structure. This data is continuously compared with traffic patterns from the digital model, allowing experts to assess how the bridge is affected over time. When necessary, adjustments can be made to traffic flow and speed to ensure the bridge’s safety.
“By focusing on monitoring only the critical zones of a bridge, we significantly reduce costs and the need for extensive equipment,” said Dr. Sen. “This method allows for timely interventions, ensuring bridge safety and longevity without major traffic disruptions.”
Bridges experience constant wear and tear due to cyclic loads from traffic, wind, and environmental factors throughout their lifespan. These repeated stresses can weaken a bridge’s integrity, leading to potential failures. Addressing this fatigue is crucial for preventing catastrophic incidents, making IIT Mandi’s research a vital contribution to infrastructure management.
Traditionally, assessing a bridge’s Remaining Useful Life (RUL) involved methods that provided broad, often inaccurate, safety margins. While techniques such as rain flow counting and finite element analysis (FEA) improved this process, they still required extensive equipment and might miss hidden issues in older bridges. Machine learning and statistical methods further refined the predictions but often came with high costs and complexity.
In contrast, the method developed by IIT Mandi offers a more practical and cost-effective solution by focusing on the most vulnerable zones. This approach also proves invaluable in emergencies such as earthquakes or floods, enabling rapid assessments and quicker decision-making to ensure public safety. Once the system is set up, routine monitoring can be carried out by personnel with basic training, reducing the need for highly specialized operators and making it scalable across multiple bridges.
For government agencies and transportation departments, this breakthrough offers a highly efficient way to manage aging infrastructure. By optimizing budget allocation to high-risk zones and reducing the need for comprehensive inspections, officials can make quicker, informed decisions, enhancing public safety without causing major traffic disruptions.
