References
- Bhoraskar, R., et al., 2012. Wolverine: Traffic and road condition estimation using smartphone sensors. In: 2012 Fourth International Conference on Communication Systems and Networks (COMSNETS 2012). Presented at the 2012 Fourth International Conference on Communication Systems and Networks (COMSNETS), Bangalore, India: IEEE, 1–6.
- Cano-Ortiz, S., Pascual-Muñoz, P., and Castro-Fresno, D., 2022. Machine learning algorithms for monitoring pavement performance. Automation in Construction, 139, 104309. doi: 10.1016/j.autcon.2022.104309
- Chen, C., Seo, H., and Zhao, Y., 2022. A novel pavement transverse cracks detection model using WT-CNN and STFT-CNN for smartphone data analysis. International Journal of Pavement Engineering, 23 (12), 4372–4384. doi: 10.1080/10298436.2021.1945056
- Chuang, T.-Y., Perng, N.-H., and Han, J.-Y., 2019. Pavement performance monitoring and anomaly recognition based on crowdsourcing spatiotemporal data. Automation in Construction, 106, 102882. doi: 10.1016/j.autcon.2019.102882
- Deng, Y. and Yang, Q., 2019. Rapid evaluation of a transverse crack on a semi-rigid pavement utilising deflection basin data. Road Materials and Pavement Design, 20 (4), 929–942. doi: 10.1080/14680629.2018.1424026
- Du, Y., et al., 2016. Application of vehicle mounted accelerometers to measure pavement roughness. International Journal of Distributed Sensor Networks, 12 (6), 8413146. doi: 10.1155/2016/8413146
- Eriksson, J., et al., 2008. 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. Presented at the Mobisys08: The 6th International Conference on Mobile Systems, Applications, and Services, Breckenridge CO USA: ACM, 29–39.
- Goel, A. and Das, A., 2008. Nondestructive testing of asphalt pavements for structural condition evaluation: a state of the art. Nondestructive Testing and Evaluation, 23 (2), 121–140. doi: 10.1080/10589750701848697
- Haralick, R.M., Shanmugam, K., and Dinstein, I., 1973. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics. SMC, 3 (6), 610–621. doi: 10.1109/TSMC.1973.4309314
- Ho, C.-H., Snyder, M., and Zhang, D., 2020. Application of vehicle-based sensing technology in monitoring vibration response of pavement conditions. Journal of Transportation Engineering, Part B: Pavements, 146 (3), 04020053.
- Kralovec, C. and Schagerl, M., 2020. Review of structural health monitoring methods regarding a multi-sensor approach for damage assessment of metal and composite structures. Sensors, 20 (3), 826. doi: 10.3390/s20030826
- Lin, C.W. and Yang, Y.B., 2005. Use of a passing vehicle to scan the fundamental bridge frequencies: an experimental verification. Engineering Structures, 27 (13), 1865–1878. doi: 10.1016/j.engstruct.2005.06.016
- Liu, C., et al., 2021. Large-scale pavement roughness measurements with vehicle crowdsourced data using semi-supervised learning. Transportation Research Part C: Emerging Technologies, 125, 103048. doi: 10.1016/j.trc.2021.103048
- Liu, J., et al., 2023. Pavement surface defect recognition method based on vehicle system vibration data and feedforward neural network. International Journal of Pavement Engineering, 24 (1), 2188594. doi: 10.1080/10298436.2023.2188594
- Mitchell, M.R., et al., 2010. Network level testing for pavement structural evaluation using a rolling wheel deflectometer. Journal of Testing and Evaluation, 38 (4), 102320. doi: 10.1520/JTE102320
- Mohan, P., Padmanabhan, V.N., and Ramjee, R., 2008. Nericell: rich monitoring of road and traffic conditions using mobile smartphones. In: Proceedings of the 6th ACM conference on Embedded network sensor systems. Presented at the SenSys08: The 6th ACM Conference on Embedded Networked Sensor Systems, Raleigh NC USA: ACM, 323–336.
- Nasimifar, M., et al., 2020. Temperature adjustment of surface curvature index from traffic speed deflectometer measurements. International Journal of Pavement Engineering, 21 (11), 1408–1418. doi: 10.1080/10298436.2018.1546858
- Pedret Rodés, J., Martínez Reguero, A., and Pérez-Gracia, V., 2020. GPR spectra for monitoring asphalt pavements. Remote Sensing, 12 (11), 1749. doi: 10.3390/rs12111749
- Sattar, S., Li, S., and Chapman, M., 2018. Road surface monitoring using smartphone sensors: a review. Sensors, 18 (11), 3845. doi: 10.3390/s18113845
- Shrestha, S., et al., 2022. Implementing traffic speed deflection measurements for network level pavement management in Virginia. Journal of Transportation Engineering, Part B: Pavements, 148 (2), 04022021.
- Wang, H.-W., et al., 2015. A real-time pothole detection approach for intelligent transportation system. Mathematical Problems in Engineering, 2015, 1–7.
- Wang, S., Zhao, S., and Al-Qadi, I.L., 2020. Real-Time density and thickness estimation of thin asphalt pavement overlay during compaction using ground penetrating radar data. Surveys in Geophysics, 41 (3), 431–445. doi: 10.1007/s10712-019-09556-6
- Yang, Q. and Deng, Y., 2019. Evaluation of cracking in asphalt pavement with stabilized base course based on statistical pattern recognition. International Journal of Pavement Engineering, 20 (4), 417–424. doi: 10.1080/10298436.2017.1299528
- Yang, Y.-B., Lin, C.W., and Yau, J.D., 2004. Extracting bridge frequencies from the dynamic response of a passing vehicle. Journal of Sound and Vibration, 272 (3–5), 471–493. doi: 10.1016/S0022-460X(03)00378-X
- Yang, Y.B. and Yang, J.P., 2018. State-of-the-art review on modal identification and damage detection of bridges by moving test vehicles. International Journal of Structural Stability and Dynamics, 18 (02), 1850025. doi: 10.1142/S0219455418500256
- Yang, Q. and Zhou, S., 2021. Identification of asphalt pavement transverse cracking based on vehicle vibration signal analysis. Road Materials and Pavement Design, 22 (8), 1780–1798. doi: 10.1080/14680629.2020.1714699
- Yu, B.X. and Yu, X., 2006. Vibration-Based System for Pavement Condition Evaluation. In: Applications of Advanced Technology in Transportation. Presented at the Ninth International Conference on Applications of Advanced Technology in Transportation (AATT), Chicago, Illinois, United States: American Society of Civil Engineers, 183–189.
- Zhang, Z., Gaspard, K., and Elseifi, M.A., 2016. Evaluating pavement management treatment selection utilising continuous deflection measurements in flexible pavements. International Journal of Pavement Engineering, 17 (5), 414–422. doi: 10.1080/10298436.2014.993198
- Zhao, W., et al., 2022a. Accuracy analysis of modulus results considering the whole process of modulus back-calculation—based on GPR and FWD. Construction and Building Materials, 348, 128671. doi: 10.1016/j.conbuildmat.2022.128671
- Zhao, W., et al., 2022b. Structural condition assessment and fatigue stress analysis of cement concrete pavement based on the GPR and FWD. Construction and Building Materials, 328, 127044. doi: 10.1016/j.conbuildmat.2022.127044