The mechanical performance of concrete elements is significantly influenced by specimen size, particularly in bending behaviour under load. In this study, the size effect of a specific concrete composition was examined through three-point bending tests on specimens of varying dimensions. The results revealed non-linear trends in load-bearing capacity and deformation, indicating that intermediate-sized specimens exhibited more favourable mechanical properties. Surface preparation methods were also investigated to enhance the accuracy of Digital Image Correlation (DIC) measurements using the ARAMIS system. Several factors – including paint absorption, drying time, ambient conditions, and mould materials – were evaluated to determine their impact on speckle pattern quality and system recognition. It was demonstrated that inadequate surface preparation leads to errors in DIC analysis, while optimised surface treatments significantly improve data reliability. The findings emphasise the importance of specimen scaling and standardised surface preparation protocols in experimental setups that utilise non-contact optical measurement techniques. By enhancing the precision and reliability of these experimental methods, this research facilitates the development of smarter, more adaptive infrastructure systems, thereby contributing to the broader goals of Cognitive Mobility. This approach leverages resilient materials and advanced monitoring techniques to enable safer, more efficient, and sustainable transportation solutions.