Mega Infrastructure Projects Today Are Central To Accelerating Economic Growth And Social Development. As Scale And Complexity Increase, So Does The Need For Faster Execution, Optimal Resource Utilization, And Strict Quality And Safety Compliance. In This Conversation, Shri Bharat Yadav, Managing Director, MPRDC, Outlines To Muskan Jaiswal Of Elets News Network How Artificial Intelligence (AI), Advanced Analytics, And Digital Ecosystems Are Reshaping Infrastructure Planning, Execution, Governance, And Lifecycle Management In India. Edited Excerpts
How can AI transform infrastructure planning, execution, and governance in India?
Mega infrastructure projects are critical for accelerating economic growth and social development, and remain a key priority for both the Central and State Governments. The scale and complexity of such projects demand faster execution, optimal resource utilization, and strict compliance with quality and safety standards. In this context, the adoption of Artificial Intelligence (AI) and advanced data-driven analytics is essential to enhance planning accuracy, improve on-site performance, optimize equipment and material usage, and enable effective coordination among multiple stakeholders. These intelligent interventions support proactive decision-making, minimize time and cost overruns, and ensure efficient and timely delivery of large-scale infrastructure programmes.
MPRDC is leveraging the PM GatiShakti Portal as a strategic tool for integrated and optimized infrastructure planning. The portal brings together more than 1,463 GIS-based data layers, including land records, forest clearances, utilities, and existing infrastructure, enabling evidence-based decision-making for finalization of optimal road alignments.
NHAI has taken a significant step towards digitised and intelligent construction by formally launching the Automated and Intelligent Machine-Aided Construction (AIMC) Policy. This initiative represents a major advancement in digitally driven, high-precision road construction, wherein heavy construction equipment such as graders, pavers, and compactors are integrated with 3D digital design models, Global Navigation Satellite System (GNSS) based positioning, and robotic total station technologies. The policy aims to enhance construction accuracy, productivity, quality control, and overall project efficiency through the adoption of advanced automation and intelligent systems. AIMC has been successfully used in the construction of the Lucknow-Kanpur Expressway.
China has gone a level up and has rebuilt and resurfaced a 158 km stretch of the Beijing-Hong Kong- Macao Expressway using a fleet of autonomous machines that were centrally controlled by AI. It was done without any human intervention on-site. This project serves as a model for future advancements in road construction worldwide. The integration of AI and autonomous machinery can revolutionise the industry by significantly reducing costs, shortening project timelines, and improving construction quality.
Considering our Ministry’s strategic focus on AI-driven transformation of the construction process, advancements in Artificial Intelligence are expected to fundamentally reshape the planning and execution of infrastructure projects over the next five years. AI has the potential to shift infrastructure delivery from a reactive and document-centric process to a predictive, integrated, and digitally automated construction ecosystem. This transformation is anticipated to have a far-reaching impact across key stages of the infrastructure lifecycle.
Planning & DPR Preparation
- AI-driven traffic modeling using satellite imagery, mobile data, and GIS can forecast demand more accurately.
- Automated land use, environmental and utility conflict analysis to reduce DPR revisions and delays.
- AI-enabled GIS and satellite analytics can improve alignment selection, traffic forecasting, land use analysis, risk identification and scenario simulation for alignment options, climate risks and cost– benefit trade-offs.
Project Execution
- Computer vision-based monitoring (drones + AI) to track construction progress, quality, and safety in near real time.
- AI-enabled contract and milestone tracking to flag schedule slippages and cost overruns early.
- Intelligent resource allocation (materials, machinery, labor) based on predictive analytics.
Governance & Oversight
- Unified AI dashboards integrating data from contractors, PMUs, and field sensors.
- Automated compliance checks for safety, environmental, and contractual obligations.
- Reduced discretion and enhanced transparency in decision-making.
- Overall, AI can help in delivering faster, cheaper, safer, and more resilient infrastructure.
How can AI enable Predictive Maint enan c e , As se t Lifecycle Management, and Cost Optimisation?
AI enables a transition from reactive repairs to predictive maintenance by accurately forecasting pavement distress and structural deterioration. Advanced data is captured using drones and high-resolution UAVs and records detailed surface conditions, such as cracks, contours, and deformations, to generate precise 3D models and digital twins of road assets.
Predictive Maintenance
- Road condition monitoring using vehicle-mounted sensors, cameras, and drones.
- AI models predict pavement distress, bridge fatigue, and culvert failure before visible damage.
- Shift from “repair after failure” to “repair before failure.”
Asset Lifecycle Management
- Digital twins of roads, bridges, and tunnels to simulate deterioration under traffic and climate stress.
- Optimized r en e w al an d rehabilitation schedules based on asset health scores.
Cost Optimization
- Reduced emergency repairs and lifecycle costs.
- Smarter budgeting through multi- year predictive O&M planning.
- Better prioritization of limited public funds.
Similarly, condition assessment of Bridge health can also be done using Sensors and developing numerical simulations for undertaking timely corrective measures required, if any, for increasing their lifecycle.
Could you share one flagship AI or data-led initiative undertaken or envisioned by MPRDC that has the potential for replication across other states?
1. Lok-path App is being used by MPRDC which utilises AI as one of its core technological upgrades to enhance road maintenance across Madhya Pradesh. AI integration in the App helps in:
- Automated pothole detection and classification
- Geospatial Intelligence for cross- referencing the Geo-tagged and time-stamped images with historical road data.
- Drone-based High Resolution Road Surveys for AI-based automated defect detection on roads.
2. Lok Nirman Sarvekshan App developed by BISAG-N, enables scientific identification of low- lying government land within a 2 km radius of construction sites for sourcing sub-grade soil through contour-based analysis. The extracted soil is utilized in project earthwork, and the resulting excavated areas are systematically developed into rainwater harvesting ponds, known as Lok Kalyan Sarovar. This approach promotes sustainable resource utilization, groundwater recharge, and the creation of water bodies without any additional financial burden on the Government.
Also Read | AI-Driven Urban Governance in Madhya Pradesh
How important are Public- Private Partnerships (PPP) in scaling AI solutions for infrastructure and governance?
The private sector contributes advanced AI expertise, computing capabilities, scalable platforms, and rapid innovation cycles, while the public sector owns and operates critical infrastructure assets. Public-private partnerships are therefore essential to combine these complementary strengths and enable the effective implementation and scaling of AI solutions in infrastructure projects.
Few models can be adopted for scaling AI solutions through PPP such as:
- Build–Operate–Transfer (BOT) for digital platforms, where vendors develop and operate AI systems initially.
- Sandbox models, allowing start- ups to pilot solutions on live government datasets.
PPP Models which have worked well
Virtual Singapore (PPP model) integrates government data with private tech to create a real-time digital twin that helps with planning, mobility, flood planning, energy, and emergency responses in Singapore.
How can states ensure that AI solutions remain ethical, transparent, and aligned with public interest?
While automation can significantly enhance efficiency, it also introduces critical ethical and governance challenges. States can ensure responsible AI by adopting few safeguards such as:
Clear Governance Frameworks
- Defined accountability for AI-driven decisions.
- Human-in-the-loop for critical approvals (land acquisition, compensation, safety).
Transparency
- Explainable AI models for audits and public review.
Data Protection and Fairness
- Compliance with India’s Digital Personal Data Protection framework.
- Bias testing in AI models, especially in land, resettlement, and contractor evaluation.
What role can states like Madhya Pradesh play in contributing to India’s National AI Mission?
Madhya Pradesh is uniquely positioned due to its central geography and logistics importance. It can contribute to the National AI mission in various manners such as:
- Develop a state-level Road and Logistics Digital Twin.
- Act as a testbed for AI for roads and bridges.
- Establish Centers of Excellence in collaboration with IITs, NITs, and private players (local as well as Global).
Also Read | Madhya Pradesh’s AI-Driven School Education Model
Any additional recommendations for building a robust and inclusive AI ecosystem?
Madhya Pradesh can build a robust and inclusive AI ecosystem through a phased, data-first approach, beginning with targeted pilot projects in planning, monitoring, and maintenance and scaling successful solutions statewide and nationally. Public-private collaboration, aligned with global best practices, will be critical for solution development, implementation, and capacity building. By positioning AI as a decision support tool that strengthens engineering judgment and governance, the state can enable faster, safer, and more cost-effective infrastructure delivery, creating a scalable model for India.