Modeling Hot-Mix asphalt dynamic modulus using deep residual neural Networks: Parametric and sensitivity analysis study

The dynamic modulus (E*) of hot-mix asphalt mixtures is one of the most tedious and time-consuming laboratory testing material properties. It requires costly, advanced equipment and skills that are not easily accessible in the majority of laboratories yet. Thus, many studies have been dedicated to developing E* predictive models. Unfortunately, it is a complex task due to the many input variables and their non-linear effect on the E*. This study applies a deep residual neural networks (DRNNs) technique for the first time to the problem to enhance the E* prediction capabilities. The proposed DRNNs architecture utilizes residual connections (i.e., shortcuts) that bypass some layers in the deep network structure in order to alleviate the problem of training with high accuracy. An intensive laboratory database is employed in the DRNNs model development considering all influential input parameters such as; mixture gradation, volumetric properties, binder characteristics, and testing conditions parameters. Moreover, a brute force enumeration is integrated in the model to reduce the number of needed input variables and identify the best combinations of them. Then, the proposed DRNNs performance, with the best combination of inputs, is evaluated using representative performance indicators and compared with the well-known E* predictive models, namely; Witczak 1-37A, Witczak 1-40D, and Hirsch models. Finally, a variance-based global sensitivity (VB-GS) analysis is conducted with the Monte Carlo simulation aid to highlight each input variable effect on the E* magnitude in real practice while removing the potential distortion of results due to the input variables correlations. Performance evaluation indicators reveal that the DRNNs model outperforms other E* prediction ones. Furthermore, VB-GS analysis shows that, among all feasible inputs, binder stiffness characteristics and testing temperature are the most significant ones. (c) 2021 Elsevier Ltd. All rights reserved.