How AI Can Improve Pavement Strength Evaluation in India: Insights from IRC 37?

India’s highway network is expanding rapidly, and ensuring long-term structural performance has become a national priority. Pavements fail early due to inadequate structural assessment, lack of continuous monitoring, inconsistent onsite quality checks and delays in detecting distress. The Indian Roads Congress (IRC) provides detailed methodologies for pavement design and evaluation, and IRC 37 remains the most referenced guideline for flexible pavement design.

With the emergence of AI pavement evaluation tools and digital survey systems, India now has the opportunity to evaluate pavement strength more accurately, more frequently and with objective, data-driven insights. This blog explains how artificial intelligence can transform pavement assessment in line with IRC 37 principles and strengthen road asset management in India.

Understanding IRC 37 and its Relevance for Pavement Strength Evaluation

IRC 37 is the core Indian guideline for designing flexible pavements based on traffic loading, material strength and environmental conditions. The document specifies structural design methods, cumulative standard axle calculations, layer thickness requirements, subgrade CBR considerations and fatigue and rutting criteria.

However, while IRC 37 provides robust design frameworks, real-time verification and continuous monitoring of in-service pavement layers remain limited. This is where AI-based pavement condition analysis can fill the gap, offering a scientific means to validate how pavements behave under actual traffic and climatic conditions over time.

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Why India Needs AI for Pavement Strength Assessment?

Traditional pavement evaluation methods in India rely on manual inspections, Benkelman Beam Deflection tests, CBR checks and visual surveys. These techniques are informative but also labor-intensive and sometimes subjective. AI solves these gaps through:

- High-speed data capture from cameras and sensors
- Automated distress detection without human bias
- Objective analysis of pavement layers, fatigue, rutting and deflection behaviour
- Predictive deterioration modelling

By integrating these capabilities into the workflows of highway authorities, contractors and quality audit teams, AI ensures continuous compliance with IRC 37 and improves the long-term performance of national and state highways.

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How AI Tools Enhance Pavement Strength Evaluation in Alignment with IRC 37?

1. Subgrade and Material Strength Assessment

IRC 37 emphasizes the importance of subgrade CBR and material characterization. AI systems help by analysing field-captured data to detect signs of structural weakness such as early rutting, cracking patterns that indicate poor load distribution and moisture-induced deterioration.

These insights provide a real-world validation of the assumed design parameters recommended by IRC 37.

2. AI-enabled Fatigue and Rutting Evaluation

Fatigue life and rutting are central components of IRC 37’s design philosophy.
Through high-resolution road imaging, AI algorithms detect fatigue cracks, rutting depth progression and layer deformation at a network scale.
This enables engineers to review deterioration trends continuously rather than waiting for periodic inspections.

3. Layer-wise Structural Behaviour Insights

AI tools process thousands of pavement images to classify distress types such as alligator cracking, longitudinal cracking, edge failures and surface defects.
This helps determine whether distress patterns align with design assumptions, construction quality or loading patterns mentioned in IRC 37.

4. Network-wide Monitoring for Road Asset Management

When integrated into road asset management India platforms, AI allows authorities to track pavement performance across entire corridors.
This helps prioritise maintenance actions, schedule rehabilitation and reduce life cycle costs.

5. Real-time Pavement Distress Identification

Using advanced machine learning and image analytics, AI systems automatically identify and map distresses such as potholes, block cracking, bleeding, raveling and edge failures.
These insights improve the accuracy of pavement distress identification and reduce the need for field teams to manually inspect every section.

6. Quality Verification in Road Construction

AI-powered verification platforms also support AI tools for road construction quality checks.
They ensure that the structural layers built on ground match the design specifications derived from IRC 37.
This reduces early failure risks and extends pavement life.

7. Safety Insights and Risk Reduction

Continuous structural evaluation reduces safety risks associated with pavement failures.
By integrating strength data with automated road safety audit systems, authorities can address critical hazards proactively.

8. Traffic Influence Assessment

IRC 37 requires accurate prediction of cumulative standard axle loads.
Using AI-based traffic survey tools, real-time traffic data is captured, helping engineers refine pavement load estimations and understand stress distribution more accurately.

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Benefits of AI-driven Pavement Strength Assessment in India

- Improved compliance with IRC design assumptions
- Accurate measurement of real-world structural performance
- Better prediction of long-term deterioration
- Reduced maintenance and rehabilitation costs
- Lower safety risks through early detection
- Transparent and evidence-based decision-making for highway agencies
- Digitisation of inspection processes
- Greater accountability for contractors during execution and maintenance periods

These advantages collectively help Indian highways advance toward smarter, safer and longer-lasting road networks.

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How RoadVision AI Supports IRC 37-based Pavement Strength Evaluation?

Platforms like RoadVision AI are transforming the way India evaluates and manages road infrastructure.
Its automated systems integrate imaging, AI analytics and GIS mapping to deliver fully digital AI pavement strength assessment. With its expertise, RoadVision AI helps government agencies adopt IRC 37 standards more efficiently across national, state and rural highways.

Explore more research and insights:
RoadVision Blog
RoadVision Case Studies

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Conclusion

AI is revolutionizing pavement strength evaluation in India by making assessments faster, more scientific and aligned with IRC 37 design principles. As highway expansion continues, adopting AI-based monitoring systems will be essential for long-lasting pavements, reduced maintenance costs and improved public safety. By integrating digital surveys, structural analytics and predictive modelling, India can ensure that its road infrastructure meets the highest performance standards.\

RoadVision AI is revolutionizing roads and transforming infrastructure development and maintenance with its innovative solutions in AI in roads. By leveraging advanced computer vision, the platform conducts thorough road safety audits, ensuring the early detection of potholes and other surface issues for timely repairs and improved road conditions. The integration of potholes detection and data-driven insights through AI also enhances traffic surveys, addressing congestion and optimizing road usage. Focused on creating smarter roads, RoadVision AI ensures compliance with IRC Codes, empowering engineers and stakeholders to reduce costs, minimize risks, and elevate road safety and transportation efficiency.

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FAQs

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Q1. What is the role of IRC 37 in pavement design

IRC 37 provides the official Indian methodology for designing flexible pavements based on traffic loads, material strength and environmental conditions. AI helps verify real-world performance in line with these guidelines.

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Q2. How does AI improve pavement strength evaluation

AI automates distress detection, analyses structural behaviour, monitors layer conditions continuously and supports predictive deterioration modelling.

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Q3. Can AI replace manual pavement surveys

AI does not replace field engineers but enhances accuracy, reduces errors and speeds up large-scale inspections.

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