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.

The main objective of this study was to investigate the characteristic properties of lignin-modified bitumen with different lignin contents. The first step was the characterization of the physicochemical and thermal properties of the kraft lignin powder along with the determination of its microstructure. This was achieved by carrying out an elemental analysis, Gel Permeation Chromatography (GPC), Thermogravimetric Analysis (TGA)/Derivative Thermogravimetry (DTG), Fourier Transformation Infrared (FTIR) spectroscopy, Scanning Electron Microscopy (SEM), Dynamic Light Scattering (DLS) and Confocal Microscopy. After the latter tests, three (3) blends with different lignin contents (5%, 10% and 15% by weight of bitumen) were produced. Characteristic properties such as penetration, softening point, elastic recovery, force ductility, dynamic viscosity and storage stability were determined for the reference bitumen and the three lignin blends. The main conclusion was that kraft lignin powder hardens the conventional bitumen. Specifically, the addition of 15% lignin to the bitumen hardened the blend to such a degree that the bitumen changed category from 50/70 to 35/50 with respect to EN 12591.

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.

The growing need to preserve natural resources and minimize landfill waste has led to an increased consideration of incorporating waste materials in road construction and maintenance. This study focuses specifically on utilizing waste glass as part of the aggregates in hot asphalt, particularly in Asphalt Concrete (AC) for surface layers, known as “Glassphalt”. Glass, due to its poor adhesion to bitumen, presents challenges when used in asphalt mixtures. Two types of waste glass, monolithic and tempered, were incorporated at two distinct contents, 10% and 15%, into the AC. Several properties such as stiffness, resistance to permanent deformation (evaluated through cyclic compression tests), indirect tensile strength, and the indirect tensile strength ratio (ITSR) were assessed for all Glassphalt mixtures, as well as the conventional mixture. Additionally, the Solar Reflectance Index (SRI) was measured to evaluate the reflectivity of the resulting Glassphalts. The findings indicate that the incorporation of both types of waste glass resulted in reduced stiffness and resistance to permanent deformation. Regarding water sensitivity (ITSR), the Glassphalts containing 15% waste glass, regardless of the glass type, exhibited ITSR values below the accepted threshold of 80%. The addition of waste glass did not yield significant changes in SRI measurements.

The main aim of this study was to identify and evaluate the characteristic properties of bitumen modified with algae. Two types of algae, each with distinct gradation and origin, were employed for this investigation. For each type of algae (noted as chlorella and microchlorella), three blends were created with varying algae contents (5%, 10%, and 15% by weight of bitumen), utilizing a 70/100 reference bitumen as the virgin material and a basis for comparison. The properties of the blends were investigated using the Penetration, Softening Point, Elastic Recovery, Force Ductility, Dynamic Viscosity, and Storage Stability tests, both before and after short-term ageing (TFOT). The test results were then used to calculate the Activation Energy (E_a), Viscosity-Temperature Susceptibility (VTS) Index, and Mixing Temperature (T_mixing), along with their respective Pearson Correlation Coefficient (PCC) and R² and p-values. The main finding of the study was that the addition of a low algae content of 5% caused a change in the classification of the unaged bitumen from 70/100 to 50/70 according to EN 12591 and thus hardened the reference bitumen. Additionally, a strong linear statistical correlation was observed between E_a and the VTS index, suggesting that these values should be considered when characterizing the temperature susceptibility of algae-modified bitumen.