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Rutting development in semi-rigid asphalt pavements can be predicted using models such as mechanistic-empirical (M-E) and data-driven methods. However, the prediction accuracies of the M-E methods in the laboratory may not be generalised due to the boundary condition differences, while inappropriate calibration in the data-driven methods may reduce the reliability owing to the lack of theoretical support. This study has proposed a combined framework of an M-E method and an artificial neural network (ANN) for rutting development prediction. This framework, namely the mechanistic-empirical and artificial neural network method, first calibrated the existing M-E model by adding a term of the time-varying hardening characteristics of asphalt mixture and optimised the parameters in the term using a genetic algorithm. The new M-E model was used to predict the possible range of rutting depth. An ANN then predicted the rutting depth that was normalised by the predicted range of the new M-E model. Thus, the proposed approach combined the reliability of the M-E method and the prediction accuracy of the ANN method. Field test results showed that the proposed framework with a 97.5% accuracy outperformed the ANN-based method. [ABSTRACT FROM AUTHOR]

Rutting development in semi-rigid asphalt pavements can be predicted using models such as mechanistic-empirical (M-E) and data-driven methods. However, the prediction accuracies of the M-E methods in the laboratory may not be generalised due to the boundary condition differences, while inappropriate calibration in the data-driven methods may reduce the reliability owing to the lack of theoretical support. This study has proposed a combined framework of an M-E method and an artificial neural network (ANN) for rutting development prediction. This framework, namely the mechanistic-empirical and artificial neural network method, first calibrated the existing M-E model by adding a term of the time-varying hardening characteristics of asphalt mixture and optimised the parameters in the term using a genetic algorithm. The new M-E model was used to predict the possible range of rutting depth. An ANN then predicted the rutting depth that was normalised by the predicted range of the new M-E model. Thus, the proposed approach combined the reliability of the M-E method and the prediction accuracy of the ANN method. Field test results showed that the proposed framework with a 97.5% accuracy outperformed the ANN-based method. [ABSTRACT FROM AUTHOR]

The Rutting prediction model is an essential element of efficient pavement management systems. Accuracy of commonly used predictive model necessitates knowledge of the input parameters that was incorporated and local calibration of the model coefficients. In this paper, a novel rutting prediction model based on multivariate transfer entropy and graph neural networks is proposed for incorporating a limited number of observable inputs, which can accommodate with sufficient prediction performance and generalization to a variety of complex pavement design structure data. The multivariate transfer entropy based graph representation is able to find the significant causality between variables and rutting. The influence factor analysis results confirm the high influence of temperature and vehicle axle load. Several experiments are set up on the Research Institute of Highway Ministry of Transport track (RIOHTrack) dataset for the comparison between the proposed model and the state-of-art prediction models. The result demonstrates that the proposed model is more accurate and robust compared to existing methods on the rutting prediction task. [ABSTRACT FROM AUTHOR]

This study investigates the development of pavement performance models, focusing on the predictive analysis of pavement distress using ensemble machine learning methods. We employ the Research Institute of Highway Ministry of Transport track dataset to construct predictive models for pavement distress, comparing various methodologies including Random Forest (RF) regression, Extreme Gradient Boosting (XGB) machines, Snake Optimizer-Random Forest (SO-RF), and Snake Optimizer-Extreme Gradient Boosting (SO-XGB). The approach is conducted by multiple experiments and a comprehensive comparative analysis leveraging the repeated K -Fold cross-validation method to ensure the robust assessment for these models. Our findings show that the SO-XGB method is particularly effective in predicting rutting depth. The strength of the SO-XGB model lies in its excellent performance in several performance metrics, such as coefficient of determination (R 2), the mean absolute error (MAE), root mean square error (RMSE), the mean absolute percentage error (MAPE), performance index (PI), index of agreement (IA), variance accounted for (VAF) and objective function (OBJ). These results not only illustrate the model's accuracy but also the practical applicability of SO-XGB model in automatically forecasting pavement damage. Thus, the SO-XGB method stands out as a direct, powerful, and efficient tool in the field of pavement performance prediction. [Display omitted] • Developing ML algorithms for rutting depth prediction in asphalt pavements. • Integrating snake optimization to enhance prediction accuracy of RF and XGB. • Evaluating ML models using eight performance metrics. • Utilizing repeated K-Fold cross-validation for precise model assessment. [ABSTRACT FROM AUTHOR]

• Deep learning time series models were proposed to predict the rutting progression. • The impacts of different architectures and sequence lengths on deep learning prediction models were analyzed. • The prediction accuracy of ARIMAX, Gaussian process and M−E models were compared. • The proposed model can capture linear and seasonal components of a rutting progression curve. Considering that traditional statistical regression and machine learning rutting prediction models hardly capture time series characteristics of rutting, this study employed deep learning (DL) techniques to develop multivariate time series models to predict the rutting progression curve for asphalt pavements commonly constructed in China from the data relating to the rutting depth of 19 asphalt pavements from RIOHTrack. 32 features related to climate, traffic, pavement structure, and materials, were selected from initial 50 features using variance threshold and feature importance analysis. k -means clustering was used to classify these pavement sections according to their rutting progression pattern. Three DL models (RNN, LSTM, and GRU) with different architectures and sequence lengths (SLs) (24 models produced) were trained from historical rutting depths and the selected exogenous variables, and their performances were then compared with ARIMAX, Gaussian process, and mechanistic-empirical (M−E) models. The results show that the architecture 2 based GRU model with an SL of 11 is found to have the best prediction performance (R2 = 0.90). The DL model can capture the pattern of the observed rutting progression curve which contains linear and seasonal trends. Moreover, the optimal DL model is more accurate than the statistical time series models and the M−E model. Permutation feature importance analysis shows the impacting features mainly include historical rutting depth, temperature, age, as well as binder penetration and air void of the second asphalt concrete layer. [ABSTRACT FROM AUTHOR]

• Rutting development is predicted using laboratory-based and multi-scale tests. • Triaxial tests predict the rheological properties of asphalt at high temperatures. • Modified Burgers FE model considers the damage effect of the asphalt mixture. • Permanent deformation and rutting depth of the asphalt pavement are monitored. • The computationally efficient method can aid long-term maintenance of pavements. Rutting development and its prediction are critically important for the long-term preservation of semi-rigid pavements. A multi-scale test was developed to analyze the rutting development and its prediction model, involving triaxial, scaled loading, and full-scale loading tests. Laboratory-based triaxial tests predicted the rheological properties of asphalt mixtures at high temperatures. The flow number F N was only related to the cohesion and internal friction angle, confining pressure, and circulating pressure of the mixture. Rutting prediction models were established using a modified Burgers finite-element model and verified through scaled and full-scale loading tests. The relationship between F N of the dynamic creep test and loading times of scaled and full-scale loading test were determined through comparative analysis. Finally, the cross-scale relationship between the multi-scale tests was established by introducing correction coefficients K 1 and K 2. The proposed prediction model of rutting development is suggested to be computationally reasonable, which could provide a scientific reference for the treatment and maintenance of rutting on asphalt pavements. [ABSTRACT FROM AUTHOR]

Abstract: Semi-rigid asphalt pavement is one of the most popularly used pavement types. The most serious distress of a semi-rigid asphalt pavement is rutting due to the fact that the pavement materials are inadequate to resist the shear stress from load repetitions. Therefore, it is very important to keep adequate shear strength of mixes for preventing rutting and to determine a rational criterion of shear strength. This paper presents a rutting predictive model through analyzing accelerated pavement rutting tests with the Accelerated Load Facility (ALF). The model was proposed to utilize a power equation with six factors, namely shear strength of asphalt mixture, shear stress of pavement structure, number of load repetitions, pavement temperature, vehicle speed and pavement depth. A procedure was proposed to determine the shear strength criterion of asphalt mixture based on the previously established rutting prediction model. The shear strength criterion of asphalt mixture for different layers, regions, expected service life and vehicle speeds can be developed with the proposed procedure. As an example application of this research, the shear strength criterions of asphalt mixtures in different layers of a typical asphalt pavement structure were recommended in five different regions in Gansu Province of China. Those criterions were related to temperature, number of loading repetitions, pavement structure, and vehicle speed. [Copyright &y& Elsevier]