Accurate prediction of concrete properties, such as compressive strength, is essential for ensuring structural performance. Particle size distribution (PSD) and nature of aggregates are key components of concrete mix tures, significantly influencing their final compressive strength. This paper presents a novel approach that leverages embedding vectors extracted from images of aggregates using the DinoV2 model to efficiently pre dict compressive strength. DinoV2 is a state-of-the-art vision transformer that excels at generating high-quality embeddings for various visual tasks. In this study, the effectiveness of these embeddings is evaluated by using them to classify and estimate the PSD of aggregates on public datasets. Small neural models trained on these vectors achieved comparable accuracy to the best found fine-tuned ViT-16 model, demonstrating the poten tial of using embedding vectors for accurate PSD prediction. Building on these results, a new approach for predicting concrete compressive strength by combining embedding vectors with data on concrete mix com ponents is explored. A small dataset of concrete mixtures was created. To mitigate the challenges of limited data, augmentation techniques were proposed to generate additional, realistic mix designs. An ablation study was performed, indicating promising results and highlighting the potential of this new approach for predicting other concrete properties.