Abstract
Introduction. This paper studies the capability of different types of artificial neural networks (ANN) to predict the modulus of elasticity of pavement layers for flexible asphalt pavement under operating conditions. The falling weight deflectometer (FWD) was selected to simulate the dynamic traffic loads and measure the flexural bowls on the road surface to obtain the database of ANN models.Materials and Methods. Artificial networks types (the feedforward backpropagation, layer-recurrent, cascade back- propagation, and Elman backpropagation) are developed to define the optimal ANN model using Matlab software. To appreciate the efficiency of every model, we used the constructed ANN models for predicting the elastic modulus values for 25 new pavement sections that were not used in the process of training, validation, or testing to ensure its suitability. The efficiency measures such as mean absolute error (MAE), the coefficient of multiple determinations R2, Root Mean Square Error (RMSE), Mean Absolute Percent Error (MAPE) values were obtained for all models results.Results. Based on the performance parameters, it was concluded that among these algorithms, the feed-forward model has a better performance compared to the other three ANN types. The results of the best four models were compared to each other and to the actual data obtained to determine the best method.Discussion and Conclusions. The differences between the results of the four best models for the four types of algorithms used were very small, as they showed the closeness between them and the actual values. The research results confirm the possibility of ANN-based models to evaluate the elastic modulus of pavement layers speedily and reliably for using it in the structural assessment of (NDT) flexible pavement data at the appropriate time.
Highlights
This paper studies the capability of different types of artificial neural networks (ANN) to predict the modulus of elasticity of pavement layers for flexible asphalt pavement under operating conditions
The results of the ANN-GA model showed reasonable accuracy with the data registered in the LTPP test database, where there were no big differences between the predicted values of the elastic modulus for the asphalt surface layer and the registered in the LTPP data [21]
The values of the following mean absolute error (MAE), R2, Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE) indices for all models were calculated as shown in Table 2 to assess the model performance
Summary
All pavement roads will deteriorate over time, regardless of how well designed or constructed [1]. The results of the ANN-GA model showed reasonable accuracy with the data registered in the LTPP test database, where there were no big differences between the predicted values of the elastic modulus for the asphalt surface layer and the registered in the LTPP data [21] In this investigation, several analyses were performed to define the best possible architecture along with learning rules and the type of the ANN model to increase the forecasting capabilities of ANNs. The used database includes wide ranges of deflection values obtained from impact load tests conducted on existing three-layer pavement systems on the roads network by the State Company Russian Highways from 2014 to 2018. It was utilized as an experimental basis for training artificial neural network models
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