review · Eng—Advances in Engineering
Maintaining road infrastructure remains a major challenge globally, particularly in low-income regions that face financial and technical limitations. Conventional inspection tools, such as inertial profilers, involve high costs and operational complexity that restrict wide-scale deployment. By contrast, smartphone technologies offer an accessible and scalable alternative for road surface monitoring. Internal sensors, including accelerometers and gyroscopes, capture motion data that is subsequently refined using preprocessing techniques such as filtering and sensor reorientation. Machine learning algorithms, specifically convolutional neural networks, classify road anomalies to improve detection accuracy. Integrating these mobile sensors with advanced processing substantially lowers the expense and operational burden of road assessments. Further developments in sensor calibration, data synchronisation, and predictive models are expected to enhance performance across diverse environments, supporting responsive infrastructure management.
Damaged roads compromise safety and slow economic activity, yet many transport authorities lack the funds for expensive scanning equipment. Demonstrating that everyday smartphones and machine learning can accurately track road deterioration offers a practical path toward democratising infrastructure monitoring, enabling quicker, cheaper repairs for communities that need them most.
The findings point towards software-driven road inspection platforms for municipal authorities, highway maintenance contractors, and transport departments. By turning consumer smartphones into automated monitoring devices, organisations can lower asset management costs. As a review paper evaluating existing technologies, it highlights methods that have been applied and tested, while noting that commercial deployment requires further work on sensor calibration, data synchronisation, and robust machine learning models.
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Deteriorating road infrastructure is a global concern, especially in low-income countries where financial and technological constraints hinder effective monitoring and maintenance. Traditional methods, like inertial profilers, are expensive and complex, making them unsuitable for large-scale use. This paper explores the integration of cost-effective, scalable smartphone technologies for road surface monitoring. Smartphone sensors, such as accelerometers and gyroscopes, combined with data preprocessing techniques like filtering and reorientation, improve the quality of collected data. Machine learning algorithms, particularly CNNs, are utilized to classify road anomalies, enhancing detection accuracy and system efficiency. The results demonstrate that smartphone-based systems, paired with advanced data processing and machine learning, significantly reduce the cost and complexity of traditional road surveys. Future work could focus on improving sensor calibration, data synchronization, and machine learning models to handle diverse real-world conditions. These advancements will increase the accuracy and scalability of smartphone-based monitoring systems, particularly for urban areas requiring real-time data for rapid maintenance.
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DOI: 10.3390/eng5040177
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