51
Views
0
CrossRef citations to date
0
Altmetric
Research Article

A novel approach for asphalt pavement invisible distress classification prediction by machine learning algorithms

ORCID Icon, , &
Article: 2343087 | Received 06 Nov 2023, Accepted 08 Apr 2024, Published online: 25 Apr 2024

References

  • Abdulalim Alabdullah, A., et al., 2022. Prediction of rapid chloride penetration resistance of metakaolin based high strength concrete using light gbm and xgboost models by incorporating shap analysis. Construction and Building Materials, 345, 128296.
  • Aberasturi, D., 2023. Violin plot. New York: Wiley Online Library. 1–7.
  • Anastasopoulos, P., Haddock, J., and Peeta, S., 2014. Improving systemwide sustainability in pavement preservation programming. Journal of Transportation Engineering, 140 (3), 04013012.
  • Ben-Dov, M., and Feldman, R., 2010. Data mining and knowledge discovery handbook. Boston: Springer Science+Business Media, 809–835, Ch. 3..
  • Benmhahe, B., and Jihane, A.C., 2021. Automated pavement distress detection, classification and measurement: A review. International Journal of Advanced Computer Science and Applications, 12 (8), 708–718.
  • Borgohain, O., et al., 2021. Performance analysis of nearest neighbor, k-nearest neighbor and weighted k-nearest neighbor for the classification of Alzheimer disease. In: S. Borah, R. Pradhan, N. Dey, et al., eds. Soft Computing Techniques and Applications. Singapore: Springer Nature Singapore Pte Ltd., 295–304, Ch. 5.
  • Buscema, M., 1998. Back propagation neural networks. Substance Use & Misuse, 33 (2), 233–270.
  • Cano-Ortiz, S., Pascual-Muñoz, P., and Castro-Fresno, D., 2022. Machine learning algorithms for monitoring pavement performance. Automation in Construction, 139, 104309.
  • Chakraborty, D., et al., 2020. A novel construction cost prediction model using hybrid natural and light gradient boosting. Advanced Engineering Informatics, 46, 101201.
  • Dmitry, O., Batrakova, A., and Mykola, M., 2022. Computing technologies for assessing the quality of roads. In: G. Ranganathan, X. Fernando, and S. Piramuthu, eds. Soft computing for security applications. Singapore: Springer Nature Singapore. 639–653.
  • Dolmans, D., et al., 2016. Deep and surface learning in problem-based learning: a review of the literature. Advances in Health Sciences Education, 21 (5), 1087–1112.
  • Dong, Q., et al., 2021a. Classification of pavement climatic regions through unsupervised and supervised machine learnings. Journal of Infrastructure Preservation and Resilience, 2 (1), 5. https://doi.org/10.1186/s43065-021-00020-7.
  • Dong, S., et al., 2021b. A novel method for testing the fatigue performance of cement stabilized base field coring samples. Construction and Building Materials, 274, 122065. https://doi.org/10.1016/j.conbuildmat.2020.122065.
  • Donnell, E., and Mason, J., 2004. Predicting the severity of median-related crashes in Pennsylvania by using logistic regression. Transportation Research Record, 1897 (1), 55–63.
  • Elhadidy, A., El-Badawy, S., and Elbeltagi, E., 2019. A simplified pavement condition index regression model for pavement evaluation. International Journal of Pavement Engineering, 22, 1–10.
  • Elseicy, A., et al., 2022. Combined use of gpr and other ndts for road pavement assessment: An overview. Remote Sensing, 14 (17), 4336.
  • Gabr, A.R., et al., 2021. A novel approach for resilient modulus prediction using extreme learning machine-equilibrium optimiser techniques. International Journal of Pavement Engineering, 23 (10), 3346–3356. https://doi.org/10.1080/10298436.2021.1892109.
  • Gong, H., et al., 2018. Use of random forests regression for predicting iri of asphalt pavements. Construction and Building Materials, 189 (Nov.20), 890–897.
  • Gong, H., et al., 2019. Investigating impacts of asphalt mixture properties on pavement performance using ltpp data through random forests. Construction and Building Materials, 204, 203–212. https://doi.org/10.1016/j.conbuildmat.2019.01.198.
  • Guo, R., Fu, D., and Sollazzo, G., 2022. An ensemble learning model for asphalt pavement performance prediction based on gradient boosting decision tree. International Journal of Pavement Engineering, 23 (10), 3633–3646.
  • Han, C., et al., 2021. Application of a hybrid neural network structure for fwd backcalculation based on ltpp database. International Journal of Pavement Engineering, 23 (9), 3099–3112.
  • Hu, A., et al., 2022. A review on empirical methods of pavement performance modeling. Construction and Building Materials, 342, 127968.
  • Hubert, M., and Vandervieren, E., 2008. An adjusted boxplot for skewed distributions. Computational Statistics & Data Analysis, 52 (12), 5186–5201.
  • Kaloop, M., et al., 2023. International roughness index prediction for flexible pavements using novel machine learning techniques. Engineering Applications of Artificial Intelligence, 122, 106007.
  • Kheirati, A., and Golroo, A., 2022. Machine learning for developing a pavement condition index. Automation in Construction, 139, 104296.
  • Krzywinski, M., and Altman, N., 2014. Visualizing samples with box plots. Nat Methods, 11 (2), 119–120.
  • Liu, F., Ye, Z., and Wang, L., 2022. Deep transfer learning-based vehicle classification by asphalt pavement vibration. Construction and Building Materials, 342, 127997.
  • Long, J., et al., 2022. Road distress detection and maintenance evaluation based on ground penetrating radar. In: Advances in civil function structure and industrial architecture. Harbin, China: CRC Press, 474–481.
  • Mers, M., et al., 2022. Recurrent neural networks for pavement performance forecasting: Review and model performance comparison. Transportation Research Record: Journal of the Transportation Research Board, 2677, 036119812211005.
  • Naseri, H., et al., 2022. A newly developed hybrid method on pavement maintenance and rehabilitation optimization applying whale optimization algorithm and random forest regression. International Journal of Pavement Engineering, 24 (2), 1–13.
  • Paterson, W.D.O., 1989. Transferable causal model for predicting roughness progression in flexible pavements. Transportation Research Record Journal of the Transportation Research Board, 1 (1215), 70–84.
  • Patrick, G., and Soliman, H., 2019. Roughness prediction models using pavement surface distresses in different canadian climatic regions. Canadian Journal of Civil Engineering, 46 (10), 934–940.
  • Pedregosa, F., et al., 2012. Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12, 2825–2830.
  • Piryonesi, S.M., and El-Diraby, T., 2021. Using machine learning to examine impact of type of performance indicator on flexible pavement deterioration modeling. Journal of Infrastructure Systems, 27 (2), 04021005.
  • Pisner, D.A., and Schnyer, D.M., 2020. Chapter 6 – support vector machine. In: A. Mechelli and S. Vieira, eds. Machine learning. Boston, MA: Academic Press, Springer. 101–121.
  • Rashid, F., 2021. Analysis and determination of axle load spectra and traffic input for pavement design. International Journal for Research in Applied Science and Engineering Technology, 9 (8), 2536–2542.
  • Revin, I., et al., 2023. Automated machine learning approach for time series classification pipelines using evolutionary optimization. Knowledge-Based Systems, 268, 110483.
  • Sarwar, M.T., and Anastasopoulos, P.C., 2017. The effect of long term non-invasive pavement deterioration on accident injury-severity rates: A seemingly unrelated and multivariate equations approach. Analytic Methods in Accident Research, 13, 1–15. https://doi.org/10.1016/j.amar.2016.10.003.
  • Schonlau, M., 2023. Random forests. Cham: Springer. 183–204, Ch. 4.
  • Shi, J., et al., 2021. Validation of adhesive and temperature property characteristics of microsurfacing by performance-based mixture design approach. Materials, 14 (16), 4532.
  • Shi, J., et al., 2022. Permeability detection and distress evolution characteristics of semi-rigid base asphalt pavement by infrared thermal difference method. International Journal of Pavement Engineering, 24 (2), 1–11.
  • Shi, D., Jincheng, W., Ning, Z., and Dong, Q., 2023. Characterization of fatigue damage accumulation and prediction of modulus deterioration for cement stabilized base. International Journal of Pavement Engineering, 24 (1), 2209263.
  • Shi Dong, P.H., and Tighe, S.L., 2019. A diagnostic method for evaluating the condition index of cement-stabilised base using t-s fuzzy neural network. International Journal of Pavement Engineering, 20 (10), 1140–1153.
  • Shtayat, A., et al., 2023. Using supervised machine learning algorithms in pavement degradation monitoring. International Journal of Transportation Science and Technology, 12 (2), 628–639.
  • Sollazzo, G., Fwa, T., and Bosurgi, G., 2017. An ann model to correlate roughness and structural performance in asphalt pavements. Construction and Building Materials, 134, 684–693. https://doi.org/10.1016/j.conbuildmat.2016.12.186.
  • Tharwat, A., et al., 2017. Linear discriminant analysis: A detailed tutorial. Ai Communications, 30 (2), 169–190.
  • Todkar, S.S., et al., 2019. Performance assessment of svm-based classification techniques for the detection of artificial debondings within pavement structures from stepped-frequency a-scan radar data. NDT & E International, 107, 102128.
  • Wang, D., et al., 2023. Road structural defects detection and digitalization based on 3d ground penetrating radar technology: A state-of-the-art review. China Journal of Highway and Transport, 36 (3), 1–19.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.