Top Mistakes in Pavement Projects That Violate IRC SP:21 — And How AI Prevents Them

Introduction

The Indian Roads Congress (IRC) has long established technical guidelines to ensure long-lasting and safe pavement infrastructure across India. One of its most critical codes, IRC SP:21, outlines the design, construction, and maintenance standards for flexible pavements.

Despite the existence of these standards, many infrastructure projects in India face deterioration, cracking, rutting, and safety issues — often due to non-compliance with IRC SP:21. However, the integration of AI pavement condition monitoring and digital road maintenance systems is now enabling better enforcement and accuracy in meeting these standards.

This article outlines the top mistakes that violate IRC SP:21 and explains how AI-powered road asset management systems in India help prevent them.

What is IRC SP:21?

IRC SP:21 is a Special Publication by the Indian Roads Congress providing technical specifications for design and construction of flexible pavements. It includes standards for bitumen content, layer thickness, drainage, cracking tolerance, and condition monitoring practices aligned with Indian road classifications.

Any pavement project not adhering to these standards runs the risk of premature failure and safety non-compliance.

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Top Mistakes That Violate IRC SP:21 Guidelines

1. Inadequate Crack Identification and Classification

IRC SP:21 categorizes cracking into types like alligator, longitudinal, transverse, and edge cracks, each requiring unique treatments.

Common Mistake:
Contractors often generalize or overlook minor cracks which later expand and cause structural failure.

How AI Helps:
AI pavement condition monitoring detects and classifies every crack type using high-resolution imagery and machine learning models, ensuring corrective action based on IRC guidelines.

2. Ignoring Skid Resistance and Surface Quality

IRC SP:21 specifies Skid Number (SN) requirements:

• Minimum 60 SN for highways
• Minimum 65 SN for urban roads

Common Mistake:
Smooth or bleeding surfaces are not flagged early, increasing accident risks.

How AI Helps:
AI-enabled sensors measure skid resistance and surface texture in real time. Combined with image-based analytics, the digital road maintenance system sends alerts for low-skid zones, even before accidents occur.

3. Improper Pavement Condition Surveys

IRC SP:21 recommends condition surveys at least twice annually (pre- and post-monsoon) using Proforma-1 and visual distress classification.

Common Mistake:
Manual inspections are delayed, inconsistent, or skip critical stretches.

How AI Helps:
Smart road survey India tools like RoadVision AI use automated cameras, GPS, and thermal sensors to conduct consistent, unbiased surveys at scale. The reports are instantly categorized by severity and location.

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4. Non-compliance in Rut Depth and Roughness Thresholds

IRC specifies maximum rut depths (10mm) and roughness values (1800mm/km for highways). Exceeding this can lead to complete pavement failure.

Common Mistake:
Surveyors miss localized ruts or fail to document data properly, leading to unaddressed structural weaknesses.

How AI Helps:
AI road sensors and condition indexing detect minute variations in rutting or IRI values and track them over time. Data is geotagged and stored for audit trails, helping enforce compliance monitoring roads efficiently.

5. Poor Water Drainage Assessment

Water accumulation leads to disintegration, ravelling, and bitumen stripping — all highlighted in Section 7 of IRC SP:21.

Common Mistake:
Projects miss hidden water-logging issues, especially during off-monsoon periods.

How AI Helps:
Drones and thermal vision integrated in AI traffic survey systems detect moisture pockets invisible to the naked eye. Early diagnosis prevents costly repairs and material loss.

6. Missing Preventive Maintenance Scheduling

IRC mandates early detection of distress before it reaches periodic renewal stage.

Common Mistake:
Roads are left to deteriorate until failure, requiring full rehabilitation.

How AI Helps:
RoadVision AI’s predictive pavement lifecycle models alert municipalities in advance when roads begin showing micro-level signs of fatigue. Digital road maintenance systems help automate and prioritize interventions.

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Role of RoadVision AI in IRC SP:21 Compliance

RoadVision AI supports compliance monitoring roads in India by providing:

• IRC-compliant PCI score indexing
• Automated distress detection and classification
• Real-time reporting in Proforma-1 format
• GIS dashboards for engineers and urban planners
• Integration with road safety audit requirements
• Case studies that prove effectiveness at scale

Explore our blog for more updates on IRC compliance through AI.

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Conclusion

As India modernizes its transportation networks, ensuring compliance with IRC SP:21 is crucial for sustainable pavement life and public safety. By avoiding these top technical mistakes and embracing AI-based road asset management systems in India, road authorities can meet national standards faster, more efficiently, and with long-term cost savings.

RoadVision AI is revolutionizing road infrastructure development and maintenance by leveraging cutting-edge AI in road safety and computer vision technology. Through advanced digital twin technology, the platform performs comprehensive road safety audits, enabling early detection of potholes, cracks, and other surface issues, ensuring timely repairs and improved road conditions. It also enhances traffic surveys by providing data-driven insights to address challenges like traffic congestion and optimize road usage. With a focus on building smart roads, RoadVision AI ensures full compliance with IRC Codes, empowering engineers and stakeholders to reduce costs, minimize risks, and improve the overall road safety and transportation experience.

Looking to achieve full IRC compliance with smart AI tools?
Book a demo with RoadVision AI and transform your infrastructure monitoring today.

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FAQs

Q1. What is IRC SP:21 used for?

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IRC SP:21 provides guidelines for the construction and maintenance of flexible bituminous pavements in India.

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Q2. How does AI help in IRC compliance?

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AI detects road defects, analyzes pavement health, and generates reports aligned with IRC standards automatically.

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Q3. Is AI monitoring cost-effective for rural roads?

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Yes, AI scales efficiently across rural, MDR, and urban roads with minimal manual intervention and higher accuracy.

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