Stiffness Data of High-Modulus Asphalt Concretes for Road Pavements: Predictive Modeling by Machine-Learning

Abstract

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.

Experimental and Machine Learning Approach to Investigate the Mechanical Performance of Asphalt Mixtures with Silica Fume Filler

“…The specimens for the subsequent physical-mechanical tests were prepared using the Marshall method, which is based on the impulsive compaction principle. Although other laboratory techniques, such as the gyratory compaction, are widely used in the USA within the SUPERPAVE method (NAPA, 1995) [38], the Marshall compaction is still commonly used in many road laboratories due to its simplicity, low cost, and extensive database available in the literature, despite the fact that it leads to volumetric properties of the compacted specimens quite different from those observed in road pavement as a result of rollers compaction [8,37,[39][40][41][42][43]. Marshall testing was carried out in accordance with ASTM D6927 [44] and the results were reported as Marshall stability, flow, and quotient.…”

Road Pavement Asphalt Concretes for Thin Wearing Layers: A Machine Learning Approach towards Stiffness Modulus and Volumetric Properties Prediction

“…To escape a local minimum, the improvement proposed by Bull [45] allows the EI acquisition function to modify its behaviour when it estimates the over-exploitation of an area of surface f(•). Thanks to this enhancement, such acquisition function is called Expected-Improvement-Plus (EIP) [46]. Given the seven hyperparameters N, act, μ, μ inc , μ dec , μ max , E and their bounded domain (Table 3), f(•) is a function that constructs a SNN with N neurons in the hidden layer, act as activation function and runs an 8-Fold CV experiment in which the network is trained on eight disjointed data sets for E iterations with an adaptive learning step size μ. α and β are updated iteratively by an independent procedure [41] to force the resulting interpolation to be smooth.…”

Prediction of Airport Pavement Moduli by Machine Learning Methodology Using Non-destructive Field Testing Data Augmentation

“…Finally, a single neuron belongs to the output layer to represent the asphalt layer stiffness modulus (E AC ). The best activation function to be assigned to the hidden layer was searched within 4 of the most commonly used in literature [14]: ELU, ReLU, TanH, LogS (Fig. 6).…”