A novel method for evaluating asphalt pavement structural performance based on vehicle vibration

References

• Barbosa, R.S., 2011. Vehicle dynamic response due to pavement roughness. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 33 (3), 302–307. doi:10.1590/S1678-58782011000300005
   Web of Science ®Google Scholar
• 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.
   Google Scholar
• Eriksson, J., et al., 2008. The pothole patrol: using a mobile sensor network for road surface monitoring. In: 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.
   Google Scholar
• Feng, K., Casero, M., and González, A., 2023. Characterization of the road profile and the rotational stiffness of supports in a bridge based on axle accelerations of a crossing vehicle. Computer-Aided Civil and Infrastructure Engineering, 38 (14), 1935–1954. doi:10.1111/mice.12974
   Web of Science ®Google Scholar
• Harikrishnan, P.M., and Gopi, V.P., 2017. Vehicle vibration signal processing for road surface monitoring. IEEE Sensors Journal, 17 (16), 5192–5197. doi:10.1109/JSEN.2017.2719865
   Web of Science ®Google Scholar
• Horak, E., 2008. Benchmarking the structural condition of flexible pavements with deflection bowl parameters. Journal of the South African Institution of Civil Engineering, 50 (2), 2–9.
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Liu, J., et al., 2023a. 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
   Web of Science ®Google Scholar
• Liu, Z., et al., 2023b. Automatic pixel-level detection of vertical cracks in asphalt pavement based on GPR investigation and improved mask R-CNN. Automation in Construction, 146, 104689. doi:10.1016/j.autcon.2022.104689
   Web of Science ®Google Scholar
• Lv, Y., et al., 2022. Vibration signal-based early fault prognosis: status quo and applications. Advanced Engineering Informatics, 52, 101609. doi:10.1016/j.aei.2022.101609
   Web of Science ®Google Scholar
• Mousavi, M., et al., 2021. Beam damage detection using synchronisation of peaks in instantaneous frequency and amplitude of vibration data. Measurement, 168, 108297. doi:10.1016/j.measurement.2020.108297
   Web of Science ®Google Scholar
• Ni, Y.Q., Wang, Y.W., and Zhang, C., 2020. A Bayesian approach for condition assessment and damage alarm of bridge expansion joints using long-term structural health monitoring data. Engineering Structures, 212, 110520. doi:10.1016/j.engstruct.2020.110520
   Web of Science ®Google Scholar
• Samie, M., et al., 2024. Potential applications of connected vehicles in pavement condition evaluation: a brief review. Road Materials and Pavement Design, 25 (5), 889–913. doi:10.1080/14680629.2023.2237597
   Web of Science ®Google Scholar
• Sattar, S., Li, S., and Chapman, M., 2018. Road surface monitoring using smartphone sensors: a review. Sensors, 18 (11), 3845. doi:10.3390/s18113845
   PubMed Web of Science ®Google Scholar
• Singh, G., et al., 2017. Smart patrolling: an efficient road surface monitoring using smartphone sensors and crowdsourcing. Pervasive and Mobile Computing, 40, 71–88. doi:10.1016/j.pmcj.2017.06.002
   Web of Science ®Google Scholar
• Vagnoli, M., Remenyte-Prescott, R., and Andrews, J., 2021. A Bayesian belief network method for bridge deterioration detection. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 235 (3), 338–355. doi:10.1177/1748006X20979225
   Web of Science ®Google Scholar
• Wang, W., Zhou, S., and Yang, Q., 2022. A characterization method for pavement structural condition assessment based on the distribution parameter of the vehicle vibration signal. Applied Sciences, 12 (2), 683. doi:10.3390/app12020683
   Google Scholar
• Yang, Q., et al., 2021a. Application of signal processing and support vector machine to transverse cracking detection in asphalt pavement. Journal of Central South University, 28 (8), 2451–2462. doi:10.1007/s11771-021-4779-6
   Web of Science ®Google Scholar
• Yang, Y.B., et al., 2021b. Scanning torsional-flexural frequencies of thin-walled box girders with rough surface from vehicles’ residual contact response: theoretical study. Thin-Walled Structures, 169, 108332. doi:10.1016/j.tws.2021.108332
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• 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
   Google Scholar
• 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
   Web of Science ®Google Scholar
• Yu, Q., Fang, Y., and Wix, R., 2022. Pavement roughness index estimation and anomaly detection using smartphones. Automation in Construction, 141, 104409. doi:10.1016/j.autcon.2022.104409
   Web of Science ®Google Scholar
• 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.
   Google Scholar
• Yuan, W., and Yang, Q., 2023a. A novel method for pavement transverse crack detection based on 2D reconstruction of vehicle vibration signal. KSCE Journal of Civil Engineering, 27 (7), 2868–2881. doi:10.1007/s12205-023-1972-1
   Web of Science ®Google Scholar
• Yuan, W., and Yang, Q., 2023b. Identification of asphalt pavement transverse cracking based on 2D reconstruction of vehicle vibration signal and edge detection algorithm. Construction and Building Materials, 408, 133788. doi:10.1016/j.conbuildmat.2023.133788
   Web of Science ®Google Scholar
• Yuan, W., Yang, Q., and Ye, W., 2024. A novel characterisation method for asphalt pavement structural performance assessment based on vehicle vibration data fusion. International Journal of Pavement Engineering, 25 (1), 2308178. doi:10.1080/10298436.2024.2308178
   Web of Science ®Google Scholar