IIT Mandi Researchers Develop AI-Based Algorithms for Real-Time Structural Assessment of Bridges

Researchers have developed advanced AI algorithms to accurately assess the structural health of bridges and other structures in real-time.

Indian Institute of Technology Mandi has partnered with INRIA in France to create advanced Artificial Intelligence (AI) and signal processing techniques for accurately predicting the structural health of bridges and other structures.

The research team at IIT Mandi has developed a Deep Learning (DL)-based SHM approach. Their AI algorithms can identify and isolate structural damages by analysing recorded ambient dynamic responses, all without the need for human intervention.

Bridges play a vital role in India's infrastructure, with nearly 13500 of them across the country. These structures undergo natural ageing due to environmental factors like temperature fluctuations, and water and air exposure, compounded by heavy road traffic. Traditionally, assessing bridge conditions has relied on visual inspections, a method considered inadequate by experts. It falls short in detecting all structural issues and is subjective and time-consuming, involving manual analysis of numerous photographs.

Recent advances in instrumentation, data analysis, and artificial intelligence (AI) tools like Deep Learning (DL) hold great promise for structural health monitoring (SHM) of bridges and other structures. These technologies make it easier to detect, measure, understand, and even predict the evolution of defects over time. This, in turn, enables more effective planning of renovation or repair work, ultimately reducing maintenance costs and extending the lifespan and availability of bridges.

Elaborating on their work, Dr Subhamoy Sen, IIT Mandi, said, "We have employed data-driven methods like Machine Learning, AI, and Bayesian statistical inference to estimate a bridge's health and predict its remaining usable life. This outcome has the potential to reduce risks to infrastructure under operational and adverse loading conditions."

Temperature fluctuations can greatly affect a bridge's dynamic traits, especially prestressed concrete, and cable-stayed bridges. It is therefore important to consider these temperature effects in both real-time and AI-based SHM. IIT Mandi's algorithm was rigorously validated on a real bridge in a cold region with extreme annual and daily temperature swings.

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