AI Road Asset Management for Cost-Efficient, IRC-Compliant Roads

Introduction

India’s rapidly growing road network demands a modern, scalable, and data-driven approach to road maintenance. With over 6.3 million kilometers of roads, India faces the challenge of maintaining high serviceability while minimizing long-term costs. The Indian Roads Congress (IRC) has developed key codes and standards that ensure quality and safety in design, construction, and maintenance. When combined with AI road asset management, these IRC Codes can be implemented with greater efficiency, accuracy, and cost control.

This blog explores how the integration of artificial intelligence in road inspection and monitoring makes it possible to build IRC-compliant, sustainable, and cost-efficient roads across India.

What Are IRC Codes and Why Are They Important?

The IRC Codes serve as the technical foundation for road construction and maintenance in India. Some of the most relevant codes in the context of road asset management include:

• IRC:82-2015 – Code of Practice for Maintenance of Bituminous Roads
• IRC:115-2014 – Guidelines for Preparation of Maintenance Management Systems
• IRC:81-1997 – Guidelines for Strengthening of Flexible Pavements
• IRC SP:102 – Maintenance of Bituminous Surfaces

These codes standardize how roads should be evaluated, classified, and maintained, with a focus on timely pavement condition surveys, preventive action, and life-cycle cost optimization.

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AI Road Asset Management: Transforming Indian Highways

Traditional road inspection methods depend on manual visual surveys, which are time-consuming, subjective, and prone to human error. AI-powered road asset management, by contrast, uses computer vision, geo-tagged imagery, and cloud analytics to detect, map, and classify road defects automatically.

With AI, authorities can now:

• Monitor road health continuously
• Identify distresses such as cracks, potholes, edge failures, and rutting in real time
• Quantify severity and extent of damage
• Schedule and prioritize interventions before deterioration escalates

Explore how RoadVision AI is enabling this shift in road monitoring technology across India and globally.

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Aligning AI with IRC Maintenance Practices

IRC recommends a system-based approach to maintenance. This includes routine, preventive, and periodic maintenance as per network needs and serviceability ratings.

AI road asset management platforms like RoadVision’s Pavement Condition Survey directly align with IRC’s grading and prioritization methods by:

• Measuring pavement distress accurately
• Automatically assigning condition scores
• Suggesting treatments based on thresholds from IRC:82-2015
• Preventing expensive overlay and reconstruction with timely alerts

This ensures that Indian roads meet prescribed performance levels with lower overall maintenance costs.

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Cost Efficiency and Lifecycle Value

One of the major benefits of AI-based management is cost optimization. According to IRC SP:82 and IRC:115, early interventions can lead to 15–20% economic returns over delayed maintenance. AI platforms help achieve this by:

• Reducing field survey time and labor costs
• Automating compliance checks with roughness, rutting, and skid resistance thresholds
• Avoiding subjective decision-making
• Improving overall budget allocation

These advantages not only extend pavement life but also help agencies plan more effective and data-driven maintenance budgets.

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Enhancing Road Safety with AI and IRC Compliance

IRC Codes also stress the importance of safety audits and inventory inspections. AI-enabled systems provide this capability at scale:

• Road Safety Audits detect geometric inconsistencies, shoulder drop-offs, and other potential hazards
• Road Inventory Inspections monitor guardrails, signage, and road markings for visibility and damage
• Integrated traffic survey data ensures planning is informed by real-time congestion and volume patterns

All these tools work in sync with IRC and MoRTH guidelines to ensure safe, sustainable, and user-friendly roadways.

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Use Cases and Real-World Impact

Several cities and agencies in India and abroad are adopting AI-based systems for IRC-compliant monitoring and maintenance. From national highways to smart cities, AI road asset management has proven to:

• Increase inspection coverage
• Minimize maintenance delays
• Improve accountability and transparency
• Support policy planning and compliance documentation

Visit our case study section to learn how AI is already delivering measurable results.

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Conclusion

India's roads can only remain sustainable, safe, and cost-effective if maintained proactively.

RoadVision AI is transforming road infrastructure development and maintenance with its innovative AI in road maintenance and AI in road construction solutions. By utilizing cutting-edge computer vision technology and digital twin models, the platform conducts comprehensive road safety audits, enabling the early detection of potholes, cracks, and other surface issues for timely repairs and enhanced road conditions. The use of AI in road safety also extends to traffic surveys, providing data-driven insights to tackle challenges like traffic congestion and optimize road usage. Focused on building smart roads, RoadVision AI ensures full compliance with IRC Codes, empowering engineers and stakeholders to reduce costs, minimize risks, and elevate road safety and transportation efficiency.

Book a demo with us and see how RoadVision AI helps you deliver cost-efficient, IRC-compliant road networks.

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FAQs

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Q1. What is AI road asset management?

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It is the use of artificial intelligence to inspect, monitor, and maintain road infrastructure automatically using cameras, sensors, and data analytics.

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Q2. How does AI help with IRC compliance?

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AI tools automate defect detection and condition grading aligned with IRC Codes, improving accuracy, timeliness, and standard compliance.

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Q3. Can AI reduce road maintenance costs?

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Yes. AI reduces survey time, prevents unnecessary overlays, and enables cost-efficient preventive maintenance.

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