India’s road network, now the second largest in the world, is under continuous pressure due to rising traffic, axle loads, climatic variations and rapid urbanisation. For sustainable maintenance, highway authorities increasingly depend on road asset management India, where accurate pavement condition data is essential for planning and budgeting. A critical component of this evaluation is roughness measurement, as defined in IRC:82, which sets standards for measuring pavement riding quality and determining long-term serviceability.
With traffic loads rising and traditional inspection methods becoming inadequate, modern digital tools such as AI pavement condition monitoring, digital road safety audits, and automated road inventory inspection are reshaping how India evaluates its pavements. AI-driven systems not only support AI-based pavement roughness analysis but also improve compliance with IRC performance guidelines.
This blog explains India’s pavement condition challenges, the importance of IRC:82 roughness measurement, and how AI-backed systems enhance accuracy, speed and repeatability across the entire evaluation process.
IRC:82 lays down the prescribed methods for measuring road roughness in India, including equipment requirements, calibration rules, survey frequencies, and acceptance criteria. Roughness is expressed in terms of the International Roughness Index (IRI), which represents the longitudinal unevenness of the pavement surface.
Under IRC:82, roughness assessment is crucial because it:
Despite having well-defined standards, implementation challenges persist due to limited manpower, inconsistent survey frequencies, and outdated manual methods. This is where AI-based systems transform the process.
Indian pavements deteriorate faster due to overloaded vehicles, climatic fluctuations, and poor drainage. Traditional methods struggle to track rapid deterioration across long corridors.
Conventional roughness tests using bump integrators and profilometers require field teams, slow movement, and strict calibration. This limits coverage and frequency.
Most state highway agencies still maintain paper-based or low-resolution datasets, making long-term trend analysis difficult.
Without reliable roughness data, agencies often conduct reactive rather than predictive maintenance, increasing lifecycle costs.
Due to varied survey quality and resource constraints, many road sections do not get measured as regularly or as accurately as IRC guidelines require.
These challenges make AI-driven digital pavement monitoring systems essential for nationwide consistency.
AI-powered inspection technologies transform roughness measurement through automation, precision and real-time analytics. They enable agencies to conduct AI highway maintenance planning backed by reliable condition data.
AI-based tools capture continuous pavement imagery and sensors track vibrations, deflections and surface irregularities. Machine learning models calculate roughness values comparable to IRI, eliminating manual interpretation errors.
Through high-speed survey vehicles, drones or mobile-mounted cameras, AI systems provide seamless network-wide roughness evaluation. This supports compliance with IRC:82’s recommended survey frequencies.
AI algorithms detect longitudinal unevenness, rutting, cracking, depressions and patchwork variations that directly influence roughness values. This helps agencies correlate riding quality with underlying structural issues.
AI roughness data feeds into centralised road asset management India dashboards, enabling engineers to study multi-year trends, forecast deterioration and optimise maintenance budgets.
AI ensures roughness measurements align with IRC:82 classifications by validating calibration settings, survey speeds, equipment quality and location accuracy.
Compared to traditional tools, AI delivers instant roughness outputs, geo-tagged distress datasets and interactive condition maps, speeding up decision-making cycles for national and state highway authorities.
Road inspection automation tools dramatically advance how roughness is evaluated.
AI tools classify pavement sections into good, fair, poor and very poor based on IRC threshold values, helping authorities prioritise rehabilitation.
Machine learning forecasts how roughness may change over upcoming years, allowing proactive maintenance and better budgeting.
Automated models build digital twins of corridors, combining roughness, safety audit scores from digital road safety audit, and traffic demand from automated traffic surveys to create a holistic corridor health profile.
AI ensures objective measurements for performance-based contracts, reducing disputes and improving transparency in pavement upkeep.
The future of Indian highway maintenance depends on how quickly agencies can transition from traditional surveys to digital evaluation. AI delivers:
In a country where millions of road users rely on smooth pavements every day, AI-informed decisions are no longer optional—they are essential.
AI-backed solutions are transforming how India measures pavement roughness and manages its expanding road network. By combining automated data collection, digital imaging, and predictive modelling, these systems make it easier for agencies to comply with IRC:82 standards while planning maintenance with precision. With advanced technologies such as digital twins and computer vision, platforms today support better pothole detection, roughness evaluation, and traffic analytics. They also align with IRC requirements and international roadway guidelines, enabling more reliable and long-lasting road networks. To explore how these solutions can support your projects, you can book a demo with us.
It helps determine pavement ride quality, identifies deterioration and guides timely maintenance planning as per IRC:82.
AI automates data collection, increases accuracy, speeds up analysis and ensures compliance with IRC performance standards.
Yes. AI-based digital inspection systems enable full-network evaluation at high speed, replacing slow and labour-intensive methods.
The Autonomous Road Engineer
AI that is transforming road infrastructure, from reactive to predictive.
