DigiPave: Digital-Twin-Enabled and Data-Driven Pavement Maintenance Planning
DigiPave
DigiPave develops an advanced digital twin platform for fully automated, data-driven road maintenance. Combining real-time IoT monitoring, high-fidelity virtual modelling, and state-of-the-art AI (YOLO, GANs, VAEs, LLMs, JEPA), it delivers crack segmentation, defect detection, pavement condition assessment, crack evolution prediction, and automated PCI/SCI calculation.
Digipave project simple introduction figure
Project information
Project duration
-
Funded by
Business Finland
Funding amount
1 214 038 EUR
Project coordinator
University of Oulu
Contact information
Project leader
- Associate Professor
Contact person
Other persons
- Professor
- Postdoctoral researcher and project manager
- Research Assistant
Researchers
Project description
The project develops a next‑generation digital twin platform for road pavements, enabling fully automated, data‑driven maintenance planning. Building on advanced AI, IoT, and edge-cloud technologies, the project integrates real‑time pavement monitoring with high‑fidelity virtual modelling to create a continuously updated digital representation of the road network. Within this platform, we have developed a suite of state‑of‑the‑art AI pipelines capable of performing complex downstream tasks, including crack segmentation, crack classification, defect detection, and pavement condition assessment. These models leverage cutting‑edge architectures such as generative models (including GANs, VAEs, LLMs), YOLO, and Joint Embedding Predictive Architectures (JEPA) to identify and characterize defects like linear cracks, alligator cracking, potholes, and surface deformation with high accuracy and minimal human intervention.
The DigiPave platform goes beyond detection: it enables crack evolution prediction, automated computation of Pavement Condition Index (PCI) and Surface Condition Index (SCI) and ultimately supports optimized Maintenance and Repair (MnR) decision‑making. By automating the entire workflow from sensing to prediction to planning, the project significantly reduces operational costs, enhances maintenance timing, and improves long‑term pavement performance. Overall, DigiPave demonstrates how digital twins and AI can transform road asset management into a proactive, scalable, and resource‑efficient process.
The project is funded by Business Finland, with the University of Oulu as the main research partner, supported by Volvo, Unikie, Satel, Infrakit, and the Finnish Meteorological Institute.
Project results
| List of authors | Title | Venue | Link |
| Abdelhak kharbouch, Mehdi Rasti, Amirhossein Ayoubi Khosroshahi | Demo: 6G‑enabled digital twins for multi-domain critical infrastructure monitoring | EUCNC26, Malaga, Spain | NA |
| Mehdi Rasti, Abdelhak kharbouch | T8- 6G-Enabled Digital Twins for Verticals: Architecture, Platforms, and Use-Cases | PIRMC 2025 Istanbul, Turkey | https://www.6gflagship.com/event/pimrc2025/ |
| Shiva Kazemi Taskou , Mehdi Rasti , and Ekram Hossain | End-to-End Resource Slicing for Coexistence of eMBB and URLLC Services in 5G-Advanced/6G Networks | IEEE Transactions on Mobile Computing | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10354514 |
| Daneshvar Arya, Golroo Amir, Rasti Mehdi | Incorporating physics-informed neural networks into yolo for pavement rutting detection | AM3P | https://doi.org/10.34726/10885 |
| Entezari Mohammad Saleh, Golroo Amir, Rasti Mehdi | A New Era in Transportation Infrastructure Contrition Evaluation with Connected Vehicles | AM3P | https://doi.org/10.34726/10578 |
| Gholipoor Narges, Kharbouch Abdelhak, Rasti Mehdi, Golroo Amir | Optimizing Storage for Real-Time Road Monitoring with Dual-Camera Systems | AM3P | https://doi.org/10.34726/10575 |
| Najafli Mohammad Amin, Fahmani Mohammad Sadegh, Golroo Amir, Rasti Mehdi | Enhancing pavement performance evaluation via crowdsourced Gamification | AM3P | https://doi.org/10.34726/10604 |
| Sedighian-Fard Mohammad, Golroo Amir, Nouri Salar, Rasti Mehdi | Autonomous synthetic data generation for asphalt pavement crack segmentation using generative models | AM3P | https://doi.org/10.34726/10781 |
| Talaghat, Mohammad Amin, Sedighian-Fard Mohammad, Golroo Amir, Rasti Mehdi | Leveraging Digital Twin for Data-Driven Pavement Maintenance | AM3P | https://doi.org/10.34726/10886 |
| Talaghat Mohammad Amin, Golroo Amir, Rasti Mehdi | Improving pavement distress segmentation with diffusion-based generative AI and digital twin insights | AM3P | https://doi.org/10.34726/10798 |
| Mohammad Amin Talaghat, Amir Golroo, Abdelhak Kharbouch, Mehdi Rasti, Rauno Heikkilä, Risto Jurva | Digital twin technology for road pavement | Automation in Construction | https://doi.org/10.1016/j.autcon.2024.105826 |
| Jianqi Zhang, Ling Ding, Wei Wang, Hainian Wang, Ioannis Brilakis, Diana Davletshina, Rauno Heikkilä, Xu Yang | Crack segmentation-guided measurement with lightweight distillation network on edge device | Computer-Aided Civil and Infrastructure Engineering | https://doi.org/10.1111/mice.13446 |
| Shiva Kazemi Taskou , Mehdi Rasti , and Ekram Hossain | Energy-Efficient Resource Allocation for FeMBB and eURLLC Coexistence in RSMA-Based Wireless Networks | IEEE Transactions on Cognitive Communications and Networking | https://doi.org/10.1109/TCCN.2025.3578509 |
| Narges Gholipoor; Mehdi Rasti; Fahimeh Aghaei; Farid Hamzeh Aghdam; Abdelhak Kharbouch; Valiollah Talaeizadeh | A Review on the Cross-Sector Resource Management Framework for Electric Vehicles Integration: Challenges, Solutions, Key-Enabling Technologies, and Future Directions | IEEE Open Journal of Intelligent Transportation Systems | https://doi.org/10.1109/OJITS.2025.3593437 |
| Mohammad Amin Talaghat,Amir Golroo,Vahid Shahhosseini & Mehdi Rasti | Enhancing pavement distress segmentation model with diffusion-based generative AI: a digital twin perspective | International Journal of Pavement Engineering | https://doi.org/10.1080/10298436.2025.2540076 |
| Mohammad Sedighian-Fard, Amir Golroo, Mahdi Javanmardi, Alexandre Alahi, Mehdi Rasti |
Data generation for asphalt pavement evaluation: Deep learning-based insights from generative models | Case Studies in Construction Materials | https://doi.org/10.1016/j.cscm.2025.e05116 |
| Mehdi Monemi, Maryam Chinipardaz, Mehdi Rasti, Mehdi Bennis, Matti Latva-Aho | Tutorial on Joint Embedding Predictive Architectures (JEPA): Foundations, Applications, and Future Directions | ACM Computing Surveys | https://openreview.net/pdf?id=Zr4PUe0ZNl |
| Mohammad Amin Talaghat, Amir Golroo, Mehdi Rasti and Vahid Shahhosseini | Road Pavement Digital Twin: A Blueprint with a Case Study | Journal of Computing in Civil Engineering | https://doi.org/10.1061/JCCEE5.CPENG-7187 |
| Soroush Amiri; Fereidoon Moghadas Nejad; Mehdi Rasti; Mehdi Monemi; Hannaneh Dehghan Tezerjani | A Novel Approach to Pavement Crack Classification using Joint-Embedding Predictive Architectures | Automation in Construction | NA |
| I. Niskanen, J. Ikonen, M. Immonen, R. Heikkilä | Infrastructure-Mounted 2D LiDAR System for Vehicle Detection and Speed Estimation on Motorways | IEEE Sensor | NA |