Structural Experts Call for a Shift from Visual Inspections to Smart Monitoring with AI and Sensor
India’s vast infrastructure network, comprising thousands of bridges, tunnels, and dams, is aging and facing increasing pressure from traffic, pollution, and climate conditions. Leading structural engineering experts now warn that the traditional method of inspecting infrastructure visually is no longer sufficient. At a seminar on ‘Capacity Augmentation & Service Life Enhancement of Bridges’ organized by the International Road Federation (IRF) India Chapter, experts urged the urgent adoption of (Artificial Intelligence) AI and Sensor to monitor and maintain critical infrastructure across the country.
The Growing Risk to India’s Bridges and Tunnels
India has over 1.5 lakh bridges, including more than 100,000 railway bridges and critical road structures. Many of these are decades old and were not designed to handle the current load of vehicles and environmental wear. Experts highlighted that environmental stress, water seepage, air pollution, and increased traffic contribute to the faster deterioration of infrastructure assets.
Dr. N. Anandavalli, Director of the CSIR-Structural Engineering Research Centre (SERC) in Chennai, stressed the vulnerability of Indian bridges and tunnels under current inspection systems. “Bridges are the lifelines of our transport networks. Maintaining them in good condition is crucial for both safety and accessibility,” she noted.
The Limitations of Visual Inspections
Traditional visual inspection techniques, while commonly practiced, are time-consuming, labor-intensive, and highly subjective. These methods rely on trained personnel identifying visible damage such as cracks, rust, or deformation. However, internal structural damage, which can be a sign of serious long-term weakening, often goes unnoticed.
“Relying only on visual inspections is risky,” Anandavalli warned. “They often fail to detect hidden damage and cannot provide real-time or predictive data. We need smarter and faster methods to assess the true health of these structures.”
Structural Health Monitoring (SHM): A Smart Alternative
The use of embedded sensors, real-time data collection, and AI-based analytics, known collectively as Structural Health Monitoring (SHM), has been gaining ground globally as an advanced infrastructure management technique. SHM systems use sensors to detect changes in vibration, strain, temperature, and other variables. AI algorithms then interpret the data to identify patterns and alert engineers to early signs of structural distress.
Key Benefits of SHM Systems:
- Early damage detection: Identifies hidden or internal damage before it becomes dangerous.
- Predictive maintenance: AI models forecast future issues, allowing proactive repairs.
- Data-driven decisions: Engineers rely on concrete data rather than subjective observations.
- Longer infrastructure lifespan: Timely intervention prevents failures and extends service life.
- Cost savings: Preventing catastrophic failures reduces emergency repair costs.
The Role of AI in Infrastructure Monitoring
AI can enhance SHM by analyzing large datasets collected from sensors over time. Using machine learning algorithms, AI systems can differentiate between minor irregularities and serious threats, predict failure timelines, and prioritize maintenance schedules. This is particularly important for a country like India, where infrastructure load is heavy and maintenance budgets are limited.
In countries like Japan, the US, and Germany, AI-based infrastructure monitoring has become standard practice in metro systems, highways, and bridges. India must follow suit if it is to ensure the safety of its population and the durability of its infrastructure.
The Need for Indigenous Sensor Development
One of the biggest challenges in adopting SHM in India is the high cost of importing advanced sensors. Dr. Anandavalli pointed out that India’s dependence on foreign technology makes large-scale deployment expensive and unsustainable.
“At present, India imports critical sensors at a very high cost. It is time we invest in developing our own sensor technology to reduce dependence on imports,” she said. Building indigenous capabilities in sensor design, manufacturing, and calibration would make SHM systems more accessible and scalable across states and urban centers.
Industry Voices Support Urgent Adoption
Dr. Gopal Rai, CEO of Dhirendra Group of Companies, echoed Anandavalli’s call for smarter solutions. He acknowledged the effectiveness of SHM systems but also highlighted the cost barriers preventing widespread use.
“These technologies are essential for ensuring the safety and extending the lifespan of our bridges and tunnels,” he said. “But they must become more accessible and affordable for wider adoption. We need public-private partnerships to build this ecosystem.”
Rai further emphasized that standardized SHM protocols, trained manpower, and government policy support are necessary to integrate these technologies at scale.
Examples of SHM Success Globally
Millau Viaduct, France:
SHM systems monitor vibrations and structural loads in one of the world’s tallest bridges, allowing real-time response to environmental changes.
Tsing Ma Bridge, Hong Kong:
Equipped with over 350 sensors, this bridge has an SHM system that tracks wind, strain, temperature, and traffic load, significantly enhancing maintenance.
New York Subway Tunnels:
AI-based inspection robots and embedded sensors reduce inspection time and prevent closures caused by unseen tunnel defects.
India’s Path Forward: Building an AI-Driven Infrastructure Framework
India must take several key steps to transform infrastructure monitoring:
- Invest in domestic R&D to create cost-effective sensors and data collection tools.
- Develop AI-based software platforms for real-time analysis and visualization.
- Launch pilot projects in metros and key corridors to demonstrate effectiveness.
- Establish national SHM standards and include them in public infrastructure tenders.
- Train engineers and technicians in AI tools, data science, and sensor maintenance.
- Foster academic-industry collaborations to keep innovation ongoing.
Government Incentives and Policies Needed
For mass adoption of AI and SHM in India, government support is critical. Incentives such as tax breaks for sensor manufacturers, grant funding for pilot programs, and integration of SHM requirements in infrastructure contracts can accelerate change. Agencies like the National Highways Authority of India (NHAI) and Indian Railways must lead by example.
Policies should also be updated to encourage data sharing, cybersecurity for infrastructure systems, and long-term budgeting for technology-driven maintenance.
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Conclusion: A Call for Smarter Infrastructure Management
India stands at a crossroads. With its infrastructure aging and demands rising, the time for reactive maintenance is over. Only through adopting AI-driven Structural Health Monitoring, developing domestic technology, and empowering engineers with real-time data, can the nation secure the future of its bridges, tunnels, and dams.
This transition will not just prevent disasters; it will save lives, reduce economic loss, and ensure uninterrupted growth. India has the talent, the urgency, and the scale. Now, it needs the vision to build an infrastructure system that is intelligent, sustainable, and resilient.
