| [1] |
Wang Z, Guo N, Wang S, Xu Y. 2021. Prediction of highway asphalt pavement performance based on Markov chain and artificial neural network approach. |
| [2] |
Han Z, Sha A, Hu L, Jiang W. 2023. Calibration of inverted asphalt pavement rut prediction model, based on full-scale accelerated pavement testing. |
| [3] |
Bao S, Han K, Zhang L, Luo X, Chen S. 2021. Pavement maintenance decision making based on optimization models. |
| [4] |
Jourdain NOAS, Steinsland I, Birkhez-Shami M, Vedvik E, Olsen W, et al. 2024. A spatial-statistical model to analyse historical rutting data. |
| [5] |
Elhadidy AA, El-Badawy SM, Elbeltagi EE. 2021. A simplified pavement condition index regression model for pavement evaluation. |
| [6] |
Said SF, Hakim H. 2016. Asphalt concrete rutting predicted using the PEDRO model. |
| [7] |
Perl M, Uzan J, Sides A. 1983. Visco-elasto-plastic constitutive law for a bituminous mixture under repeated loading. Transportation Research Record 1983(911):118−127 |
| [8] |
Alae M, Zhao Y, Zarei S, Fu G, Cao D. 2020. Effects of layer interface conditions on top-down fatigue cracking of asphalt pavements. |
| [9] |
Shtayat A, Moridpour S, Best B, Abuhassan M. 2022. Using supervised machine learning algorithms in pavement degradation monitoring. |
| [10] |
Sandamal K, Shashiprabha S, Muttil N, Rathnayake U. 2023. Pavement roughness prediction using explainable and supervised machine learning technique for long-term performance. |
| [11] |
Marcelino P, de Lurdes Antunes M, Fortunato E, Gomes MC. 2021. Machine learning approach for pavement performance prediction. |
| [12] |
Sharma A, Sachdeva SN, Aggarwal P. 2023. Predicting IRI using machine learning techniques. |
| [13] |
Gong H, Sun Y, Shu X, Huang B. 2018. Use of random forests regression for predicting IRI of asphalt pavements. |
| [14] |
Li W, Ju H, Xiao L, Tighe S, Pei L. 2019. International roughness index prediction based on multigranularity fuzzy time series and particle swarm optimization. |
| [15] |
Justo-Silva R, Ferreira A, Flintsch G. 2021. Review on machine learning techniques for developing pavement performance prediction models. |
| [16] |
Zhou Q, Okte E, Al-Qadi IL. 2021. Predicting pavement roughness using deep learning algorithms. |
| [17] |
Ziari H, Sobhani J, Ayoubinejad J, Hartmann T. 2016. Prediction of IRI in short and long terms for flexible pavements: ANN and GMDH methods. |
| [18] |
Dong Y, Shao Y, Li X, Li S, Quan L, et al. 2019. Forecasting pavement performance with a feature fusion LSTM-BPNN model. Proceedings of the 28th ACM International Conference on Information and Knowledge Management. Beijing, China, 2019. New York, NY, USA: ACM. pp. 1953−1962 doi: 10.1145/3357384.3357867 |
| [19] |
Selsal Z, Karakas AS, Sayin B. 2022. Effect of pavement thickness on stress distribution in asphalt pavements under traffic loads. |
| [20] |
Alavi MZ, Ahmadi A, Movahed FV. 2025. How aggregate gradation and layer thickness influence asphalt microsurfacing texture and skid resistance. |
| [21] |
Yang R, Liu L, Sun L, Jin T, Cheng H, et al. 2025. Effective temperature model for rutting prediction considering temperature distribution inside the asphalt pavements. |