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.
Highlights
Faculty of Civil Engineering, Czech Technical University, Thákurova 7, 166 29 Prague, Czech Republic; Department of Civil Engineering, University Campus, Aristotle University of Thessaloniki, 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)
The intention was to collect a wide range of possible mix designs which differ in used aggregate type, bitumen type, and gradation or partial substitution of virgin aggregates by reclaimed asphalt
The indirect tensile strain test on cylindrical specimens (IT-CY) Stiffness Modulus of 115 Marshall test specimens of high-modulus asphalt mixtures prepared in the laboratory with reclaimed asphalt pavement or polymermodified bitumen has been investigated, according to EN 12697-26 Annex C, as part of real case-mix design processes
Summary
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. HMAC or interchangeable term EME (Enrobé a Module Élevé) is a special type of asphalt mixture (asphalt concrete) with a strong aggregate structure, slightly higher amount of bituminous binder and high stiffness. This type of mixture is used in both heavy-duty and structural rehabilitation projects where it is desirable to minimize the impact of grade change, yet still, ensure pavement longevity
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