Recycled Aggregate Concrete (RAC) represents a significant innovation aimed at reducing the carbon footprint in the construction industry. Over the past few decades, numerous investigations and experiments have confirmed the viability of RAC as a construction material when the optimal combination of recycled and natural aggregates is used. This study seeks to further enhance the application of RAC by providing a robust framework for determining the optimal RAC mixture. To achieve this, machine learning is developed to predict the compressive strength of RAC by considering various mixture properties. To improve the accuracy of these predictions, the Symbiotic Organism Search (SOS) metaheuristic algorithm is employed, not only to fine-tune the machine learning hyperparameters but also to select the most suitable model. In this study, the SOS algorithm is tasked with choosing between Artificial Neural Networks (ANN), Support Vector Machines (SVM), or Random Forests (RF), based on predefined upper and lower bounds for their hyperparameters. The resulting machine learning model is then integrated with the novel Pareto Front Symbiotic Organism Search (PF-SOS) to generate a Pareto front of optimal mixtures, with compressive strength and production cost as the objectives. To validate the efficiency of the proposed method, the PF-SOS results are compared with those from other well-known multi-objective optimization algorithms. The findings demonstrate that PF-SOS offers faster convergence and a broader range of mixture options within the same function evaluation limit.

Asphalt pavement consists of aggregates resulting in a waste material at end of its life. The aggregates can be reused as basic material for asphalt or cementitious binding agents. In both scenarios, the recycled aggregates should provide a good bond with the binder to achieve strength. This study focuses on reusing recycled asphalt aggregates (RAA) in mortar. The major weakness of RAA is the thin oily film originating from the asphalt residue, weakening the bond with cement. The pyrolysis method is accessed in an attempt to overcome this weakness. Three scenarios were investigated; the use of virgin aggregates (VA), RAA, and pyrolysis recycled asphalt aggregate (PRAA) as constituent in mortar. All variables were set a constant except for the aggregate type, the VA mortar function as controlling element. This research is methodologically based on experimental data conducted in the laboratory, while aggregate samples were taken from the field. To analyze the influence of pyrolysis to the aggregate-to-cement bond behaviour, qualitative and quantitative data were collected. The quantitative data were the mechanical properties, the mortar tensile, and compression strength. The qualitative data were obtained from scanning electron microscope readings to visually observe the aggregate surface roughness and voids, including the aggregates cross-section and pre-existing micro-cracks in the aggregate-to-cement interface. Supporting data were the aggregates‘ abrasion rate and absorption. The RAA resulted in a significant mortar strength decrease. This conclusion was supported by the findings of pre-existing cracks in the interfacial transition zone. The pyrolysis method improved the compression strength but negligibly affected the tensile behavior. The compression and tensile strength increased as a function of time for both RAA and PRAA, and a strength convergence was reached at 28 days. The PRAA is considered an option for reuse in mortar, supporting nature conservation.