The present paper discusses the analysis and modeling of laboratory data regarding the mechanical characterization of hot mix asphalt (HMA) mixtures for road pavements, by means of artificial neural networks (ANNs). The HMAs investigated were produced using aggregate and bitumen of different types. Stiffness modulus (ITSM) and Marshall stability (MS) and quotient (MQ) were assumed as mechanical parameters to analyze and predict. The ANN modeling approach was characterized by multiple layers, the k-fold cross validation (CV) method, and the positive linear transfer function. The effectiveness of such an approach was verified in terms of the coefficients of correlation ( R ) and mean square errors; in particular, R values were within the range 0.965 – 0.919 in the training phase and 0.881 – 0.834 in the CV testing phase, depending on the predicted parameters.

In this study, a cohort of 78 university students performed a driving experience in a virtual urban scenario, by means of a car driving simulator, to examine effects of a planned hands-free mobile phone conversation on young drivers’ braking behaviors. To this aim, a control group was left free to drive without any imposed cognitive task. An experimental group faced the same scenario while engaged in a phone call. The conversation via earphones was arranged to diminish the amount of cognitive resources allocated to the driving task. For both groups, the analyses focused on the moment at which a child entered a pedestrian crossing from a sidewalk. The results of a mixed two-way ANOVA showed the presence of a significant difference for distracted and non-distracted drivers with the absence of gender-related differences across the two groups. Distracted participants assumed lower initial speeds, took the first action to stop at shorter distances from the zebra crossing, and had more difficulty in keeping speed variations under control. These findings suggest that the distraction induced by the use of earphones may induce risk compensation behaviors and delay pedestrian perception. Moreover, the effects on the participants' braking behavior suggest that the procedure adopted to increase cognitive load, based on a story retelling, is an effective method to analyze the impact of hands-free cellphone use on driving skills in a car simulation experiment.

An integrated approach based on machine learning and data augmentation techniques has been developed in order to predict the stiffness modulus of the asphalt concrete layer of an airport runway, from data acquired with a heavy weight deflectometer (HWD). The predictive model relies on a shallow neural network (SNN) trained with the results of a backcalculation, by means of a data augmentation method and can produce estimations of the stiffness modulus even at runway points not yet sampled. The Bayesian regularization algorithm was used for training of the feedforward backpropagation SNN, and a k-fold cross-validation procedure was implemented for a fair performance evaluation. The testing phase result concerning the stiffness modulus prediction was characterized by a coefficient of correlation equal to 0.9864 demonstrating that the proposed neural approach is fully reliable for performance evaluation of airfield pavements or any other paved area. Such a performance prediction model can play a crucial role in airport pavement management systems (APMS), allowing the maintenance budget to be optimized.

This study introduces a machine learning approach based on Artificial Neural Networks (ANNs) for the prediction of Marshall test results, stiffness modulus and air voids data of different bituminous mixtures for road pavements. A novel approach for an objective and semi-automatic identification of the optimal ANN’s structure, defined by the so-called hyperparameters, has been introduced and discussed. Mechanical and volumetric data were obtained by conducting laboratory tests on 320 Marshall specimens, and the results were used to train the neural network. The k-fold Cross Validation method has been used for partitioning the available data set, to obtain an unbiased evaluation of the model predictive error. The ANN’s hyperparameters have been optimized using the Bayesian optimization, that overcame efficiently the more costly trial-and-error procedure and automated the hyperparameters tuning. The proposed ANN model is characterized by a Pearson coefficient value of 0.868.

This paper presents a study about a Machine Learning approach for modeling the stiffness of different high-modulus asphalt concretes (HMAC) prepared in the laboratory with harder paving grades or polymer-modified bitumen which were designed with or without reclaimed asphalt (RA) content. Notably, the mixtures considered in this study are not part of purposeful experimentation in support of modeling, but practical solutions developed in actual mix design processes. Since Machine Learning models require a careful definition of the network hyperparameters, a Bayesian optimization process was used to identify the neural topology, as well as the transfer function, optimal for the type of modeling needed. By employing different performance metrics, it was possible to compare the optimal models obtained by diversifying the type of inputs. Using variables related to the mix composition, namely bitumen content, air voids, maximum and average bulk density, along with a categorical variable that distinguishes the bitumen type and RAP percentages, successful predictions of the Stiffness have been obtained, with a determination coefficient (R2) value equal to 0.9909. Nevertheless, the use of additional input, namely the Marshall stability or quotient, allows the Stiffness prediction to be further improved, with R2 values equal to 0.9938 or 0.9922, respectively. However, the cost and time involved in the Marshall test may not justify such a slight prediction improvement.