Stiffness Moduli Modelling and Prediction in Four-Point Bending of Asphalt Mixtures: A Machine Learning-Based Framework

Stiffness modulus represents one of the most important parameters for the mechanical characterization of asphalt mixtures (AMs). At the same time, it is a crucial input parameter in the process of designing flexible pavements. In the present study, two selected mixtures were thoroughly investigated in an experimental trial carried out by means of a four-point bending test (4PBT) apparatus. The mixtures were prepared using spilite aggregate, a conventional 50/70 penetration grade bitumen, and limestone filler. Their stiffness moduli (SM) were determined while samples were exposed to 11 loading frequencies (from 0.1 to 50 Hz) and 4 testing temperatures (from 0 to 30 °C). The SM values ranged from 1222 to 24,133 MPa. Observations were recorded and used to develop a machine learning (ML) model. The main scope was the prediction of the stiffness moduli based on the volumetric properties and testing conditions of the corresponding mixtures, which would provide the advantage of reducing the laboratory efforts required to determine them. Two of the main soft computing techniques were investigated to accomplish this task, namely decision trees with the Categorical Boosting algorithm and artificial neural networks. The outcomes suggest that both ML methodologies achieved very good results, with Categorical Boosting showing better performance (MAPE = 3.41% and R2 = 0.9968) and resulting in more accurate and reliable predictions in terms of the six goodness-of-fit metrics that were implemented

Electrophilic C–H Borylation of Aza[5]helicenes Leading to Bowl-Shaped Quasi-[7]Circulenes with Switchable Dynamics

The intrinsic relationship between helicenes and circulenes is of fundamental interest and importance in molecular engineering. Herein, electrophilic borylation of phenanthroline-derived aza[5]helicenes is presented, resulting in the incorporation of a boryl unit into two termini of helicenes to afford quasi-[7]circulenes. Their bowl-shaped structures were determined by X-ray diffraction. UV–vis absorption and fluorescence spectroscopy, as well as electrochemical measurements and DFT calculations, gave insight into their electronic properties. Variable-temperature NMR studies and DFT calculations revealed bowl-to-bowl inversion at room temperature and bowl-to-helix equilibria at elevated temperature, highlighting the important role of B ← N bond strength in tuning their dynamic properties