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Asphalt concrete's (AC) durability and maintenance demands are strongly influenced by its fatigue life. Traditional methods for determining this characteristic are both resource-intensive and time-consuming. This study employs artificial neural networks (ANNs) to predict AC fatigue life, focusing on the impact of strain level, binder content, and air-void content. Leveraging a substantial dataset, we tailored our models to effectively handle the wide range of fatigue life data, typically represented on a logarithmic scale. The mean square logarithmic error was utilized as the loss function to enhance prediction accuracy across all levels of fatigue life. Through comparative analysis of various hyperparameters, we developed a machine-learning model that captures the complex relationships within the data. Our findings demonstrate that higher binder content significantly enhances fatigue life, while the influence of air-void content is more variable, depending on binder levels. Most importantly, this study provides insights into the intricacies of using ANNs for modeling, showcasing their potential utility with larger datasets. The codes developed and the data used in this study are provided as open source on a GitHub repository, with a link included in the paper for full access.

Architectural conservation and repair are becoming increasingly important issues in many countries due to numerous prior improper interventions, including the use of inappropriate repair materials over time. As a result, the composition of repair masonry mortars is now being more frequently addressed in mortar research. Just recently, for example, it has become apparent that Portland cement mortars, extensively exploited as repair mortars over the past few decades, are not suitable for repair because of their chemical, physical, mechanical, and aesthetic incompatibilities with original materials. This paper focuses on the performance of various lime-based alternative materials intended for application in repairing historic structures when subjected to mechanical loading. Results of basic material tests indicate that the use of metakaolin as a pozzolanic additive produces mortars with superior strength and sufficiently low shrinkage. Moreover, mortar strength can be further enhanced by the addition of crushed brick fragments, which explains the longevity of Roman concretes rich in pozzolans and aggregates from crushed clay products such as tiles, pottery, or bricks. An integrated experimental-numerical approach was used to identify key mortar parameters influencing the load-bearing capacity of masonry piers subjected to a combination of compression and bending. The simulations indicate increased load-bearing capacities for masonry piers containing metakaolin-rich mortars with crushed brick fragments, as a result of their superior compressive strength., Comment: 22 pages, 19 figures, 2 tables

The purpose of this paper is to propose a continuum micromechanics model for the simulation of uniaxial compressive and tensile tests on lime-based mortars, in order to predict their stiffness, compressive and tensile strengths, and tensile fracture energy. In tension, we adopt an incremental strain-controlled form of the Mori-Tanaka scheme with a damageable matrix phase, while a simple $J_2$ yield criterion is employed in compression. To reproduce the behavior of lime-based mortars correctly, the scheme must take into account shrinkage cracking among aggregates. This phenomenon is introduced into the model via penny-shaped cracks, whose density is estimated on the basis of a particle size distribution combined with the results of finite element analyses of a single crack formation between two spherical inclusions. Our predictions show a good agreement with experimental data and explain the advantages of compliant crushed brick fragments, often encountered in ancient mortars, over stiff sand particles. The validated model provides a reliable tool for optimizing the composition of modern lime-based mortars with applications in conservation and restoration of architectural heritage., Comment: 21 pages, 12 figures, 1 table

The purpose of this paper is to propose an inexpensive micromechanics-based scheme for stiffness homogenization and strength estimation of mortars containing crushed bricks, known as cocciopesto. The model utilizes the Mori-Tanaka method to determine the effective stiffness, combined with estimates of quadratic invariants of the deviatoric stresses inside phases to predict the compressive strength. Special attention is paid to the representation of C-S-H gel layer around bricks and interfacial transition zone around sand aggregates, which renders the predictions sensitive to particle sizes. Several parametric studies are performed to demonstrate that the method correctly reproduces data and trends reported in available literature. Moreover, the model is based exclusively on parameters with clear physical or geometrical meaning and as such it provides a convenient framework for its further experimental validation., Comment: 12 pages, 6 figures, 1 table, v2: moderate revisions after the first round of review