## 1 Introduction
[35] discussed the accuracy and robustness of ANN-based models for estimating the dynamic modulus of hot mixes: such models exhibit significantly higher prediction accuracy (also at the input domain boundaries), less prediction bias and better understanding of the influences of temperature and mixture composition than their regression-based counterparts. Recently, [PERSON] et al. [36] developed an advanced hybrid model, as it is based both on ANNs and optimization technique, to accurately predict the dynamic modulus of Stone Mastic Asphalt (R\({}^{2}\) = 0.985); also, they use the proposed model to evaluate and discuss the effects of temperature and frequency on the mechanical parameter. Similarly, [PERSON] et al. [37] used a simple ANN approach for modeling experimental test results and examining the impact of different features on the properties of construction and demolition waste, such as the reclaimed asphalt pavement. Although the documented research has attempted to introduce new approaches to an empirical-mechanical mix design, the Marshall approach is still widely adopted in many laboratories [38; 39; 40; 41; 42; 43; 44]. [PERSON] et al. [45] have verified the possibility of applying ANNs for the prediction of Marshall test results of BMs. The proposed NN model uses the physical properties of standard Marshall specimens to predict the Marshall stability (MS), flow (MF) and Marshall Quotient values obtained at the end of mechanical tests. [PERSON] [46] has studied the effects of varying temperatures and exposure times on the stability of BMs and modeled the test data by using a multilayer ANN. Conversely, [PERSON] et al. [23] have validated the efficiency of the multilayer perceptron ANNs for the assessment of the rutting potential of dense BMs: the flow number of Marshall specimens has been correlated to the aggregate and bitumen contents, percentage of voids in mineral aggregate, MS and MF. The mechanical characteristics of the bituminous mixtures depend on the volumetric properties as well as the bitumen content. Such parameters have to meet the limits, set by the current local specifications, for the pavement layer interested from the intervention. Nevertheless, voids in mineral aggregate, voids filled with bitumen and air voids (AV) are determined with a specific test (EN 12697-8), which requires additional time and costs. [PERSON] et al. [47] have proposed a neural network model that uses the quantities of bitumen and aggregate in Marshall specimens to predict the MS, MF value and AV obtained from the tests. Likewise, [PERSON] et al. [48] have used ANNs to estimate air void content in several types of BMs produced according to EN 13108-1. Anyway, the literature presented has in common the need to provide the road engineer with an algorithm that can provide accurate predictions of empirical parameters related to BMs, without the need for sophisticated, time-consuming and expensive laboratory testing. Despite the fact that ANNs have successfully provided predictive equations to quicken the empirical Marshall mix design, such computational models were usually based on a neural network structure set a priori by the research engineer and trained on a random subset of the available data sample. In case of a relatively small data set, such a practice may involve the risk of leaving out some relevant trends from the training set and leading to a variable prediction error, measured on the validation set, due to data sample variability and selected ANN architecture [49]. These issues can be avoided if an efficient model selection and appropriate data partition are performed. In particular, the search for the optimal network architecture, one of the most difficult tasks in ANN studies, consists of tuning the model settings, called hyperparameters, that yield the best performance score on the validation set.