How Dashcam-Based Road Surveys Help Validate Pavement Design as per IRC SP 35?

India's road network is one of the largest in the world, and ensuring its durability and safety is a top priority for highway agencies and infrastructure planners. The Indian Roads Congress (IRC) has published several guidelines to ensure standardized pavement design and maintenance practices. Among them, IRC SP 35 plays a critical role in setting parameters for evaluating road conditions and validating pavement design based on actual field performance.

With the rapid evolution of AI Road Management technologies, including dashcam-based road surveys, agencies now have access to automated pavement condition surveys and GIS mapping tools that enhance data accuracy and compliance with IRC SP 35 standards. This blog explains how modern technologies help validate pavement design, improve road asset management systems, and optimize long-term maintenance strategies in India.

Understanding IRC SP 35 and Its Role in Pavement Design

IRC SP 35 provides a framework for evaluating road surface conditions, identifying distresses, and determining the necessary maintenance or design validation measures for flexible pavements. It focuses on:

• Pavement distress measurements (cracking, ravelling, potholes, rut depth, patching, settlement)
• Serviceability indicators such as roughness (mm/km) and skid resistance
• Condition rating systems for highways, MDRs, and urban roads to classify pavement quality as Good, Fair, or Poor
• Guidelines for data collection through visual inspections, automated survey vehicles, and manual checks

The data collected as per IRC SP 35 helps agencies validate whether pavement design assumptions (such as expected load-bearing capacity and performance lifespan) are meeting real-world conditions.

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Limitations of Traditional Pavement Surveys

Historically, road condition surveys were performed manually, involving:

• Walking inspections of critical road sections
• Vehicle-based visual assessments at speeds of 5–10 km/hr
• Subjective evaluation of cracks, potholes, and surface distress

These methods are time-consuming, error-prone, and limited in coverage. With India’s rapidly expanding road network, relying solely on manual surveys delays decision-making and increases maintenance costs due to reactive interventions.

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The Shift to Dashcam-Based Automated Pavement Condition Surveys

Modern dashcam-based road survey systems use AI-enabled cameras mounted on regular vehicles or inspection fleets. These systems capture high-resolution road imagery at traffic speeds, process data in real time, and identify pavement defects automatically.

Key benefits include:

1. High-speed data capture without disrupting traffic flow
2. Objective, consistent distress detection based on AI algorithms
3. Quantifiable metrics aligned with IRC SP 35 condition rating tables
4. Integration with GIS platforms for spatial mapping of road defects

With RoadVision AI’s automated pavement condition survey solution, agencies can quickly assess large road networks, calculate distress percentages, and assign weighted ratings as recommended in IRC SP 35.

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Using Dashcam Data to Validate Pavement Design

Validating pavement design involves comparing expected vs. actual field performance of a road section. Dashcam-based surveys streamline this process:

1. Quantifying Distresses as per IRC SP 35

• AI systems analyze imagery to detect cracking, potholes, patchwork, rutting, and settlement.
• Each parameter is assigned a condition rating (1 = Poor, 2 = Fair, 3 = Good) following IRC SP 35 tables.
• Weighted scores provide an objective pavement performance index, essential for verifying design adequacy.

2. Serviceability Indicators for Safety

• Dashcams paired with profilometers estimate roughness levels (in mm/km).
• Skid resistance measurements can be combined with visual surveys for road safety audits.

3. Linking Pavement Data to Road Asset Management Systems

• Data from automated surveys is integrated into RoadVision AI’s Road Asset Management System.
• This allows real-time condition monitoring, predictive analysis, and cost-effective maintenance planning.

4. GIS-Based Pavement Mapping

• AI tools geotag each detected distress and visualize it on a GIS road network map.
• Engineers can overlay traffic volume, past maintenance records, and environmental data to make data-driven design improvements.
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Advantages Over Manual Surveys

• 100% network coverage at lower cost and time
• Compliance with IRC SP 35 methodology for distress quantification
• Data-backed design validation, improving accountability of contractors and designers
• Early detection of failures, allowing preventive treatments before roads deteriorate to poor condition

This technology directly supports periodic condition surveys mandated under IRC SP 35, helping agencies plan renewals, strengthening, or reconstruction based on accurate, timely data.

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Future of AI Road Management in India

As India moves toward smart and sustainable infrastructure, AI-powered automated surveys will be integral to road development. With dashcam-based inspections, government agencies, consultants, and contractors can ensure:

• Pavement designs meet their intended life expectancy
• Maintenance budgets are optimized through evidence-based decisions
• Predictive road asset management reduces costly reconstructions

Companies like RoadVision AI are pioneering these innovations, providing scalable, high-accuracy, and IRC-compliant solutions for road inventory inspection, traffic surveys, and road safety audits.

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Conclusion

Validating pavement design as per IRC SP 35 is no longer limited to manual, subjective surveys. Dashcam-based automated pavement condition surveys, powered by AI and GIS mapping, provide a data-driven, accurate, and cost-effective approach to assess real-world road performance. Adopting these technologies ensures long-lasting pavements, safer roads, and optimized investments in India's transport infrastructure.

RoadVision AI is transforming infrastructure development and maintenance by harnessing artificial intelligence and computer vision AI to revolutionize road safety and management. By leveraging advanced computer vision artificial intelligence and digital twin technology, the platform enables the early detection of potholes, cracks, and other road surface issues, ensuring timely repairs and better road conditions. With a mission to build smarter, safer, and more sustainable roads, RoadVision AI tackles challenges like traffic congestion and ensures full compliance with IRC Codes. By empowering engineers and stakeholders with data-driven insights, the platform reduces costs, minimizes risks, and enhances the overall transportation experience.

‍Book a demo with RoadVision AI to explore our AI-based road inspection solutions tailored for Indian road authorities and contractors.

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FAQs

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Q1: What is IRC SP 35 used for in pavement management?

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It sets guidelines for condition surveys, distress measurements, and serviceability indicators to validate pavement design and plan maintenance activities.

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Q2: How do dashcam-based surveys improve road condition assessment?

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They automate distress detection, provide objective ratings, and comply with IRC SP 35 standards, reducing time and cost versus manual surveys.

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Q3: Can AI road management help reduce road maintenance costs?

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Yes. Predictive analysis from AI surveys enables preventive treatments, extending pavement life and avoiding expensive reconstructions.

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