Deep Learning Model Ensemble Applied to Modulus Back-Calculation of Old Cement Concrete Rubblized Overlay Asphalt Pavement

Accurately determining the modulus of each structural layer remains a key challenge in asphalt pavement design, construction quality control, and bearing capacity assessment. This study introduces an ensemble model combining a genetic algorithm-optimized backpropagation neural network (GA-BP) and a convolutional neural network (CNN) to back-calculate the dynamic modulus of asphalt pavement layers over rubblized old cement concrete structures. Using a dynamic deflection basin database created by our research team, we built a dataset of 1,552,000 pavement structure samples with Falling Weight Deflectometer (FWD) data. Based on this dataset, we developed regression models, including a backpropagation (BP) neural network, GA-BP, and CNN, to perform the back-calculation of dynamic modulus values. Performance testing revealed that the CNN model outperformed both the GA-BP and BP models in terms of accuracy and stability, as indicated by evaluation metrics (R2, MAE, RMSE, MAPE), with the following ranking: CNN > GA-BP > BP. Nonetheless, the maximum relative error across all three models remained notable. To address this, an ensemble model combining GA-BP and CNN was created, significantly enhancing the accuracy and stability of the back-calculation results. The proposed ensemble model was tested on-site with FWD data to estimate the dynamic modulus of asphalt pavement layers. The results demonstrated strong agreement with actual pavement performance and high consistency with numerical outcomes from three-dimensional (3D) dynamic finite element method simulations. These findings suggest that the GA-BP and CNN ensemble approach offers a reliable method for back-calculating the dynamic modulus of asphalt pavement layers over rubblized old cement concrete structures.