نشریه فناوری آزمون‌های غیرمخرب

نشریه فناوری آزمون‌های غیرمخرب

ارزیابی روش‌های غیرمخرب پایش سلامت روسازی جاده‌ها مبتنی بر سیستم‌های هوشمند

نوع مقاله : مقاله فنی

نویسندگان
1 دانشکده فنی و مهندسی، دانشگاه ارومیه، ارومیه، ایران
2 دانشکده مهندسی عمران، دانشگاه خواجه نصیرالدین طوسی، تهران، ایران
چکیده
پایش وضعیت جاده‌ها به‌عنوان یکی از ارکان اساسی مدیریت زیرساخت‌های حمل‌ونقل، نقش حیاتی در ارتقای ایمنی، کاهش هزینه‌های نگهداری و بهبود کارایی شبکه جاده‌ای ایفا می‌کند. در سال‌های اخیر، روش‌های متعددی برای ارزیابی سلامت جاده‌ها توسعه یافته‌اند که از جمله آن‌ها می‌توان به بازدیدهای میدانی سنتی، حسگرهای تعبیه‌شده در جاده، سامانه‌های مبتنی بر وسایل نقلیه مجهز به حسگر، روش‌های مبتنی بر داده‌برداری جمعی شده از طریق گوشی‌های هوشمند و فناوری‌های تصویربرداری هوایی اشاره کرد. هر یک از این روش‌ها دارای مزایا و معایبی در زمینه دقت، هزینه، پوشش مکانی، اتوماسیون و مقیاس‌پذیری هستند. بررسی این روش‌ها نشان می‌دهد که روش‌های سنتی اگرچه از دقت بالایی برخوردارند، اما به دلیل هزینه‌های اجرایی زیاد و پوشش محدود، برای شبکه‌های گسترده جاده‌ای چندان مناسب نیستند. در مقابل، فناوری‌های نوین مانند حسگرهای کم‌هزینه نصب‌شده بر روی خودروها یا استفاده از داده‌های جمع‌آوری‌شده از طریق تلفن‌های همراه، با وجود چالش‌هایی در زمینه دقت و پردازش داده‌ها، به دلیل مقرون‌به‌صرفه بودن و قابلیت گسترش در مقیاس وسیع، گزینه‌های امیدوارکننده‌ای برای پایش مستمر جاده‌ها محسوب می‌شوند. همچنین، روش‌های مبتنی بر تصویربرداری هوایی و پردازش تصویر، به‌ویژه در مناطق صعب‌العبور، می‌توانند به‌عنوان مکمل سایر روش‌ها مورد استفاده قرار گیرند. در نهایت، ترکیب هوشمندانه روش‌های مختلف با تأکید بر فناوری‌های کم‌هزینه و مقیاس‌پذیر، می‌تواند به ایجاد سامانه‌های پایش یکپارچه و کارآمد منجر شود. توسعه چنین سیستم‌هایی با بهره‌گیری از تحلیل‌های پیش‌بینانه و داده‌های بلادرنگ، زمینه را برای تحول در مدیریت نگهداری جاده‌ها و تصمیم‌گیری‌های مبتنی بر شواهد فراهم می‌کند. این امر به‌ویژه در کشورهای در حال توسعه با منابع مالی محدود، از اهمیت بسزایی برخوردار است.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Evaluation of Non-Destructive Pavement Health Monitoring Methods Based on Intelligent Systems

نویسندگان English

Farogh Zabihi 1
Saeid Mirbeygi 2
Hasan Sadeghi 2
Mohadeseh Khedmati 2
1 Faculty of Engineering, Urmia University, Urmia, Iran
2 Faculty of Civil Engineering, K.N.Toosi, Tehran, Iran.
چکیده English

Pavement condition monitoring represents a critical aspect of transportation infrastructure management, directly influencing road safety, lifecycle maintenance planning, and the overall efficiency of roadway networks. With the increasing demand for resilient and sustainable transportation systems, the development and implementation of effective pavement health assessment methods have become a top priority for both researchers and infrastructure authorities. In recent years, a variety of approaches have been proposed to evaluate road surface conditions. These include conventional manual surveys, sensors embedded within pavements, mobile sensing platforms installed on vehicles, crowdsourced data collection through smartphone applications, and remote sensing techniques such as aerial or satellite imagery. Each method offers distinct advantages and limitations when evaluated based on criteria such as cost, spatial and temporal resolution, data accuracy, operational complexity, automation potential, and scalability. Traditional approaches, although accurate and widely validated, are often constrained by high labor requirements, limited coverage, and elevated operational costs. In contrast, recent innovations involving low-cost inertial sensors mounted on passenger vehicles or smartphones provide a more affordable and scalable alternative for continuous, real-time pavement monitoring. However, these methods may face challenges regarding signal noise, environmental sensitivity, and the need for robust preprocessing algorithms to ensure reliable interpretation. Furthermore, the integration of aerial imaging and computer vision techniques has enabled the assessment of road surfaces in remote or difficult-to-access regions, thereby expanding the coverage of monitoring systems. The fusion of different sensing modalities (ground-based, vehicular, airborne, and crowdsourced) within a unified analytical framework holds the potential to create a comprehensive and intelligent monitoring system capable of predictive maintenance and timely decision-making. Such a hybrid and intelligent approach is particularly valuable for developing countries, where limited financial and technical resources necessitate cost-effective, scalable, and low-maintenance solutions. By leveraging real-time data streams and predictive analytics, transportation agencies can significantly enhance the reliability and efficiency of pavement management systems and move toward evidence-based infrastructure planning.

کلیدواژه‌ها English

Pavement Health Monitoring
Intelligent Sensors
Image Processing
Transportation Infrastructure Management
 [1]P. Harikrishnan and V. P. Gopi, "Vehicle vibration signal processing for road surface monitoring," IEEE Sensors Journal, vol. 17, no. 16, pp. 5192-5197, 2017.
 [2]K. De Zoysa, C. Keppitiyagama, G. P. Seneviratne, and W. Shihan, "A public transport system based sensor network for road surface condition monitoring," in Proceedings of the 2007 workshop on Networked systems for developing regions, 2007, pp. 1-6.
 [3]V. Bassoo, Y. Beeharry, R. Ramjug-Ballgobin, M. Deenmahomed, and K. Soin, "A low-cost real-time road surface condition monitoring and alert system," in 2022 4th International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM), 2022: IEEE, pp. 1-5.
 [4]G. Alessandroni et al., "Smartroadsense: Collaborative Road surface condition monitoring," Proceedings of the UBICOMM, pp. 210-215, 2014.
 [5]E. Golov, S. Evtyukov, M. Protsuto, S. Evtyukov, and E. Sorokina, "Influence of the road surface roughness (according to the International Roughness Index) on road safety," Transportation research procedia, vol. 63, pp. 999-1006, 2022.
 [6]X. Nguyen, T. Nguyen, and P. H. Tran, "The effect of road surface roughness to recommended speed of vehicles," in IOP Conference Series: Materials Science and Engineering, 2020, vol. 886, no. 1: IOP Publishing, p. 012014.
 [7]A. Babu, D. Gerber, S. V. Baumgartner, and G. Krieger, "Road Surface Roughness Estimation Using Spaceborne Synthetic Aperture Radar," IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1-5, 2023.
 [8]A. Louhghalam, M. Akbarian, and F.-J. Ulm, "Roughness-induced pavement–vehicle interactions: key parameters and impact on vehicle fuel consumption," Transportation Research Record, vol. 2525, no. 1, pp. 62-70, 2015.
 [9]A. Basavaraju, J. Du, F. Zhou, and J. Ji, "A machine learning approach to road surface anomaly assessment using smartphone sensors," IEEE Sensors Journal, vol. 20, no. 5, pp. 2635-2647, 2019.
 [10]S. Sattar, S. Li, and M. Chapman, "Road surface monitoring using smartphone sensors: A review," Sensors, vol. 18, no. 11, p. 3845, 2018.
 [11]A. Khandakar et al., "Harnessing Smartphone Sensors for Enhanced Road Safety: A Comprehensive Dataset and Review," Scientific Data, vol. 12, no. 1, p. 418, 2025.
 [12]C. Wu et al., "An automated machine-learning approach for road pothole detection using smartphone sensor data," Sensors, vol. 20, no. 19, p. 5564, 2020.
 [13]P. K. Shukla, A. Vijayvargiya, and R. Kumar, "Human activity recognition using accelerometer and gyroscope data from smartphones," in 2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3), 2020: IEEE, pp. 1-6.
 [14]P. Kamranfar, D. Lattanzi, A. Shehu, and S. Stoffels, "Pavement distress recognition via wavelet-based clustering of smartphone accelerometer data," Journal of Computing in Civil Engineering, vol. 36, no. 4, p. 04022007, 2022.
 [15]A. F. Olsen and J. Torresen, "Smartphone accelerometer data used for detecting human emotions," in 2016 3rd International Conference on Systems and Informatics (ICSAI), 2016: IEEE, pp. 410-415.
 [16]M. Monteiro, C. Cabeza, and A. C. Marti, "Acceleration measurements using smartphone sensors: Dealing with the equivalence principle," Revista Brasileira de Ensino de Física, vol. 37, p. 1303, 2015.
 [17]J. Wahlström, I. Skog, P. Händel, B. Bradley, S. Madden, and H. Balakrishnan, "Smartphone placement within vehicles," IEEE transactions on intelligent transportation systems, vol. 21, no. 2, pp. 669-679, 2019.
 [18]J. Jun, R. Guensler, and J. Ogle, "Smoothing methods designed to minimize the impact of GPS random error on travel distance, speed, and acceleration profile estimates," Transportation Research Record, 2005.
 [19]Á. Catalá-Prat, F. Köster, and R. Reulke, "Image and laser scanner processing as confident cues for object detection in driving situations," in Proc. of the ISPRS Com. V Symposium:” Image Engineering and Vision Metrology, 2010.
 [20]V. Usenko, J. Engel, J. Stückler, and D. Cremers, "Reconstructing street-scenes in real-time from a driving car," in 2015 International Conference on 3D Vision, 2015: IEEE, pp. 607-614.
 [21]H. Qin and H. Qin, "Image-based dedicated methods of night traffic visibility estimation," Applied Sciences, vol. 10, no. 2, p. 440, 2020.
 [22]L. D'Orazio, F. Visintainer, and M. Darin, "Sensor networks on the car: State of the art and future challenges," 2011 Design, Automation & Test in Europe, pp. 1-6, 2011.
 [23]R. Roberts, G. Giancontieri, L. Inzerillo, and G. Di Mino, "Towards low-cost pavement condition health monitoring and analysis using deep learning," Applied Sciences, vol. 10, no. 1, p. 319, 2020.
 [24]S. H. Khahro et al., "Low-cost pavement management system for developing countries," Sustainability, vol. 13, no. 11, p. 5941, 2021.
 [25]Y. Wang et al., "An Android Sensors-Based Portable Road Health Monitoring System Utilizing Measurement Uncertainty Analysis," ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, vol. 10, no. 4, p. 044501, 2024.
 [26]S. H. Khahro, Y. Javed, and Z. A. Memon, "Low Cost Road Health Monitoring System: A Case of Flexible Pavements," Sustainability, vol. 13, no. 18, p. 10272, 2021.
 [27]M. A. Khan, M. H. Tariq, A. Ali, Y. A. Bakhtiar, and T. Kamal, "Design and Implementation of Smart Inertial Profilometer System for Road Quality Assessment," in IECON 2023-49th Annual Conference of the IEEE Industrial Electronics Society, 2023: IEEE, pp. 1-6.
 [28]D. M. Dilip, "Thermal Digital Twins of Asphalt Pavements using Physics-informed Neural Networks."
 [29]Z. Dong, H. Song, X. Ma, Y. Li, and D. Wang, "Intelligent Weigh-in-Motion System Using Embedded MEMS Sensors for Pavement Monitoring," IEEE Transactions on Instrumentation and Measurement, 2024.
 [30]M. Barriera, S. Pouget, B. Lebental, and J. Van Rompu, "In situ pavement monitoring: A review," Infrastructures, vol. 5, no. 2, p. 18, 2020.
 [31]N. A. Prabatama, M. L. Nguyen, P. Hornych, S. Mariani, and J. M. Laheurte, "Pavement Monitoring with a Wireless Sensor Network of MEMS Accelerometers," in 2024 IEEE International Symposium on Measurements & Networking (M&N), 2024: IEEE, pp. 1-6.
 [32]J. Braunfelds et al., "Road pavement structural health monitoring by embedded fiber-bragg-grating-based optical sensors," Sensors, vol. 22, no. 12, p. 4581, 2022.
 [33]M. Abdellatif, H. Peel, A. G. Cohn, and R. Fuentes, "Hyperspectral imaging for autonomous inspection of road pavement defects," in Proceedings of the 36th International Symposium on Automation and Robotics in Construction (ISARC), 2019: International Association for Automation and Robotics in Construction, pp. 384-392.
 [34]S. Jana, S. Thangam, A. Kishore, V. Sai Kumar, and S. Vandana, "Transfer learning based deep convolutional neural network model for pavement crack detection from images," International Journal of Nonlinear Analysis and Applications, vol. 13, no. 1, pp. 1209-1223, 2022.
 [35]D. Grabowski, M. Szczodrak, and A. Czyżewski, "Economical methods for measuring road surface roughness," Metrology and Measurement Systems, pp. 533-549-533-549, 2018.
 [36]S. Cafiso, A. Di Graziano, and S. Battiato, "Evaluation of pavement surface distress using digital image collection and analysis," in Seventh International congress on advances in civil Engineering, 2006, pp. 1-10.
 [37]H. A. Gabbar et al., "HAIS: Highways Automated-Inspection System," Technologies, vol. 11, no. 2, p. 51, 2023.
 [38]L. Manoni, S. Orcioni, and M. Conti, "Recent Advancements in Deep Learning Techniques for Road Condition Monitoring: A Comprehensive Review," IEEE Access, 2024.
 [39]M. Adamiak et al., "Deep Learning Enhanced Road Traffic Analysis: Scalable Vehicle Detection and Velocity Estimation Using PlanetScope Imagery," arXiv preprint arXiv:2410.14698, 2024.
 [40]V. Radojčić, A. S. Cvetković, M. Dobrojević, P. Spalević, and J. M. E. Guider, "Advancements in Computer Vision Applications for Traffic Surveillance Systems," 2024.
 [41]N. V. Hung, N. H. Dung, T. M. Hoang, and N. T. Dzung, "A traffic monitoring system for a mixed traffic flow via road estimation and analysis," in 2016 IEEE Sixth International Conference on Communications and Electronics (ICCE), 2016: IEEE, pp. 375-378.
 [42]R. Du, G. Qiu, K. Gao, L. Hu, and L. Liu, "Abnormal Road surface recognition based on smartphone acceleration sensor," Sensors, vol. 20, no. 2, p. 451, 2020.
 [43]T. Dange, D. Pawar, R. Potdar, S. Kaul, and P. Pawar, "Evaluation of Road Condition Using Android Sensors and Cloud Computing," International Research Journal of Engineering and Technology (IRJET), 2019.
 [44]N. Van Khang and É. Renault, "Cooperative sensing and analysis for a smart pothole detection," in 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), 2019: IEEE, pp. 1785-1790.
 [45]M. A. Bidgoli, A. Golroo, H. S. Nadjar, A. G. Rashidabad, and M. R. Ganji, "Road roughness measurement using a cost-effective sensor-based monitoring system," Automation in Construction, vol. 104, pp. 140-152, 2019.
 [46]M. R. Ganji, A. Golroo, H. Sheikhzadeh, A. Ghelmani, and M. A. Bidgoli, "Dense-graded asphalt pavement macrotexture measurement using tire/road noise monitoring," Automation in construction, vol. 106, p. 102887, 2019.
 [47]E. Edwan, N. Sarsour, and M. Alatrash, "Mobile application for bumps detection and warning utilizing smartphone sensors," in 2019 International conference on promising electronic technologies (ICPET), 2019: IEEE, pp. 50-54.
 [48]N. Silva, V. Shah, J. Soares, and H. Rodrigues, "Road anomalies detection system evaluation," Sensors, vol. 18, no. 7, p. 1984, 2018.
 [49]O. Rajmane, V. Rane, and A. Bhosale, "Road condition detection using arduino based sensing module and android smartphone," Int. J. Adv. Eng. Res. Dev, vol. 4, pp. 52-57, 2017.
 [50]F. Seraj, B. J. Van Der Zwaag, A. Dilo, T. Luarasi, and P. Havinga, "RoADS: A road pavement monitoring system for anomaly detection using smart phones," in International Workshop on Modeling Social Media, 2014: Springer, pp. 128-146.
 [51]F. Menant, J.-M. Martin, D. Meignen, D. Bétaille, and M. Ortiz, "Using probe vehicles for pavement monitoring: experimental results from tests performed on a road network," Transportation Research Procedia, vol. 14, pp. 3013-3020, 2016.
 [52]J. Eriksson, L. Girod, B. Hull, R. Newton, S. Madden, and H. Balakrishnan, "The pothole patrol: using a mobile sensor network for road surface monitoring," in Proceedings of the 6th international conference on Mobile systems, applications, and services, 2008, pp. 29-39.

  • تاریخ دریافت 14 اردیبهشت 1404
  • تاریخ بازنگری 23 تیر 1404
  • تاریخ پذیرش 30 بهمن 1404