Civil engineering smart cities: road maintenance with a data-driven approach of machine learning and IOT

Abstract

The study investigates an innovative approach for proactive road repair that combines machine learning algorithms with Internet of Things (IoT) devices. To gather real-time data on variables such surface cracks, potholes, traffic volume, and drainage obstructions, the system uses sensors installed in road corridors. Using machine learning models for analysis and prediction and Internet of Things devices for data collection, a data-driven strategy was put into practice. The system produces useful information for scheduling and setting priorities for maintenance tasks. To evaluate the suitability of IoT in road maintenance, the study used a case review of previous initiatives, such as the work done by Sanket Machhala et al. Statistical validity treatment such as confidence intervals, standard error, statistical significance testing was verified to check for the robustness and uncertainty of the statistical analysis. The proposed maintenance method will utilize the use MQTT HiveMQ protocol (publisher), for transmission of processed and analyzed data gotten from embedded sensors to road engineers or maintenance agency (subscriber), preprocessing for noise reduction, feature extraction, and prediction analysis with classification algorithms like k-NN and SVM were all part of the implementation process ensuring seamless communication between IoT devices and the central analysis system. The findings show that by facilitating preventive action against road deterioration, IoT-enhanced road maintenance systems increase safety, reduce site visit before decision made for maintenance works, save expenses, and improve efficiency. The results validate the approach’s scalability for managing urban infrastructure and smart city projects.