A Comprehensive Review of Smartphone and Other Device-Based Techniques for Road Surface Monitoring

A Comprehensive Review of Smartphone and Other Device-Based Techniques for Road Surface Monitoring
by Saif Alqaydi 1ORCID,Waleed Zeiada 2,3,*ORCID,Ahmed El Wakil 4,Ali Juma Alnaqbi 2ORCID andAbdelhalim Azam 3,5ORCID
1
Department of Industrial Engineering Management, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
2
Department of Civil and Environmental Engineering, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
3
Department of Public Works Engineering, Mansoura University, Mansoura 35516, Egypt
4
Department of Electrical Engineering, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
5
Department of Civil Engineering, Jouf University, Sakaka 72388, Saudi Arabia
*
Author to whom correspondence should be addressed.
Eng 2024, 5(4), 3397-3426; https://doi.org/10.3390/eng5040177
Submission received: 8 November 2024 / Revised: 5 December 2024 / Accepted: 10 December 2024 / Published: 16 December 2024
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Abstract
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.