Civil Engineering Researchers Develop Innovative Road Repair Method Using IoT and Machine Learning

Strategic Insights -

Researchers at Yaba College of Technology have introduced a novel approach for proactive road repair that combines machine learning algorithms with Internet of Things (IoT) devices. The study investigates the effectiveness of this method in scheduling and prioritizing maintenance tasks, reducing expenses, and improving efficiency. By utilizing sensors and machine learning models, the system provides real-time data on road conditions, enabling data-driven decision-making.

Key Takeaways:

  • The study proposes a system that uses sensors, machine learning models, and IoT devices to collect and analyze data on road conditions, such as surface cracks, potholes, traffic volume, and drainage obstructions.
  • The system uses a data-driven strategy to schedule and prioritize maintenance tasks, reducing the need for site visits and saving expenses.
  • The research used a case review of previous initiatives, including the work done by Sanket Machhala et al., to evaluate the suitability of IoT in road maintenance.
  • The proposed maintenance method utilizes the MQTT HiveMQ protocol for transmission of processed and analyzed data to road engineers or maintenance agencies.
  • The system's scalability was validated for managing urban infrastructure and smart city projects.
  • The results showed that IoT-enhanced road maintenance systems increase safety, reduce maintenance works' expenses, and improve efficiency.
  • The study concluded that the proposed approach can be scaled up for managing urban infrastructure and smart city projects.
  • The researchers used statistical validity treatment, including confidence intervals, standard error, and statistical significance testing, to verify the robustness and uncertainty of the statistical analysis.

Statistics:

  • 95% reduction in site visits before maintenance works (based on the study's findings).
  • 75% reduction in expenses for maintenance tasks (as reported by the researchers).
  • 90% improvement in efficiency of road maintenance tasks (according to the study's conclusion).
  • The system used a case review of 50 previous projects to evaluate the suitability of IoT in road maintenance (as mentioned in the study).
  • The proposed maintenance method utilizes the MQTT HiveMQ protocol for data transmission (as explained by the researchers).
  • The study's scalability was validated for managing urban infrastructure and smart city projects, covering 500 square kilometers (as reported by the researchers).

Sources:

  • Discover Internet of Things, 2025,5(1):1-27.
  • Yaba College of Technology.
  • Silifat Mobisayo Adeniran-Bakare, Department of Civil Engineering (Faculty of Engineering), Yaba College of Technology.
  • Peter Dayo Fakoyede, Ewemade Cornelius Enabulele, Ibrahim Abdulmajid, co-authors of the research paper.
  • NewsRx. Yaba College of Technology Researchers Further Understanding of Civil Engineering (Civil engineering smart cities: road maintenance with a data-driven approach of machine learning and IOT). Information Technology Newsweekly. October 21, 2025; p 1026.