References
- Cafiso, S., et al., 2017. From manual to automatic pavement distress detection and classification. 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017 - Proceedings, 433–438.
- Capuruço, R. A. C., et al., 1968. Performance evaluation of sensor-and image-based technologies for automated pavement condition surveys. Transportation Research Record: Journal of the Transportation Research Board, 1, 47–52.
- Chacra, D. B. A., and Zelek, J. S, 2017. Fully automated road defect detection using street view images. Proceedings - 2017 14th Conference on Computer and Robot Vision, CRV 2017, 2018-January, 353–360. doi:10.1109/CRV.2017.50.
- Chan, S., Sc, M. A., and Eng, P., 2016. Transition from manual to automated pavement distress data collection and performance modelling in the pavement management system. In TAC 2016: Efficient Transportation-Managing the Demand-2016 Conference and Exhibition of the Transportation Association of Canada.
- Clemen, R. T, 1989. Combining forecasts: A review and annotated bibliography. International Journal of Forecasting, 5 (4), 559–583.
- Dietterich, T. G, 1997. Machine-Learning research. AI Magazine, 18 (4), 97–97.
- Dietterich, T. G, 2000. Experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Machine Learning, 40 (2), 139–157.
- Gopalakrishnan, K., et al., 2017. Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection. Construction and Building Materials, 157, 322–330.
- Haas, R., Hudson, W. R., and Zaniewski, J. P, 1994. Modern pavement management. Krieger Publishing Company, 102 (7), 583.
- Hu, A., et al., 2022. A review on empirical methods of pavement performance modeling. Construction and Building Materials, 342, 127968.
- Khadgi, P., et al., 2017. Mapping automated pavement data to windshield visual survey data: statistical approach. Technical Report.
- Kotsiantis, S. B, 2013. Decision trees: a recent overview. Artificial Intelligence Review, 39, 261–283.
- KYTC, 2020. PAVEMENT MANAGEMENT FIELD HANDBOOK KYTC PAVEMENT DISTRESS IDENTIFICATION MANUAL & GUIDELINE FOR PREVENTIVE MAINTENANCE TREATMENTS 2009. https://transportation.ky.gov/Maintenance/Documents/PavementOperations/PM%20Field%20Manual09.pdf [Accessed 15 October 2022].
- McQueen, Jason Michael, 2004. A study of manual vs automated pavement condition surveys. Alabama: Auburn University.
- Mokhtari, S., Wu, L., and Yun, H.-B, 2016. Comparison of supervised classification techniques for vision-based pavement crack detection. Transportation Research Record: Journal of the Transportation Research, 2595 (1), 119–127.
- Mraz, A., et al., 2006. Experimental evaluation of a pavement imaging system Florida department of transportation’s multipurpose survey vehicle. Transportation Research Record: Journal of the Transportation Research Board, 1974(1), 97–106.
- Naftaly, U., Intrator, N., and Horn, D, 1997. Optimal ensemble averaging of neural networks. Network: Computation in Neural Systems.
- North Florida TPO, 2020. PAVEMENT MANAGEMENT PILOT. https://northfloridatpo.com/uploads/Studies/Final-Pavement-Management-Report-v2.pdf [Accessed 15 October 2022].
- Pérez-Acebo, H., et al., 2018. Research trends in pavement management during the first years of the 21st century: A bibliometric analysis during the 2000–2013 period. Applied Sciences, 8 (7), 1041.
- Ping Ong, G., Noureldin, S., and Sinha, K. C., 2009. Methodology to evaluate quality of pavement surface distress data collected by automated techniques. Transportation Research Record, 2093 (1), 3–11.
- Priore, P., et al., 2018. Learning-based scheduling of flexible manufacturing systems using ensemble methods. Computers & Industrial Engineering, 126, 282–291.
- Stone, M, 1974. Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36 (2), 111–133.
- Tewari, S., and Dwivedi, U. D, 2019. Ensemble-based big data analytics of lithofacies for automatic development of petroleum reservoirs. Computers & Industrial Engineering, 128, 937–947.
- Tighe, S. L., et al., 2008. Evaluation of semiautomated and automated pavement distress collection for network-level pavement management The Ministry of Transportation Ontario (MTO), like many provincial and state departments of transportation (DOTs), was interested. Transportation Research Record, 2084 (1), 11–17.
- Underwood, B. S., et al., 2010. Assessment of use of automated distress survey methods for network-level pavement management. Journal of Performance of Constructed Facilities, 25 (3), 250–258.
- Wang, K. C. P., and Gong, W., 2002. Automated pavement distress survey: a review and a new direction ICC MDR408X view project pavement texture view project automated pavement distress survey: a review and a new direction. In Pavement Evaluation Conference, 21–25.
- Ying, L., and Salari, E, 2010. Beamlet transform-based technique for pavement crack detection and classification. Computer-Aided Civil and Infrastructure Engineering, 25 (8), 572–580.
- Zhang, A., et al., 2017. Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network. Computer-Aided Civil and Infrastructure Engineering, 32 (10), 805–819.
- Zhang, J., et al., 2018. Models for mapping from an Automatic Pavement Condition Survey to a Legacy Manual Survey. In IISE Annual Conference. Proceedings.
- Zhang, S., Lippitt, C. D., and Bogus, S. M, 2017. Pavement surface condition estimation based on geospatial modelling. Annals of GIS, 23 (3), 167–181.
- Zhi-Hua, Z. H., 2012. Ensemble methods: foundations and algorithms. CRC Press.