Casting concrete at different ages for new construction and repairing or retrofitting concrete structures requires a sufficient bond between concrete casts. The bond strength between different casts is attributed to surface roughness. Surface roughness can be achieved in many ways, such as water‐jetting or sandblasting. To evaluate the degree of surface roughness, qualitative and quantitative methods are introduced by many researchers; however, several drawbacks are associated with most of these methods, including cost, availability, human errors, and inability to assess old structures from prior inspection records. Two novel industrial implementation methods are introduced in this paper to estimate, quantitatively, the concrete surface roughness from images with sufficient resolution. In the first application method, a digital image processing method is proposed to distinguish the coarse aggregate from cement paste, and a new index is presented as a function of aggregate proportional area to the surface area. In the second application method, data augmentation and transfer learning techniques in computer vision and machine learning are utilized to classify new images based on predefined images during the learning process. Both application methods were related to a well‐established method of 3D laser scanning from sandblasted concrete surfaces. Finally, a brand new set of images of sandblasted surfaces was used to test and validate both methods. The results show that both methods successfully estimate the concrete surface roughness with an accuracy of more than 93%.

Ultra-High Performance Concrete (UHPC) has been a material of interest for retrofitting reinforced concrete elements because of its pioneer mechanical and material properties. Numerous experimental studies for retrofitting concrete structures have shown an improvement in durability performance and structural behaviour. However, conservative and sometimes erroneous estimates for bond strength are used for numerically calculating the strength of the composite members. In addition, different roughening methods have been used to improve the bond mechanism; however, there is a lack of numerical simulation for the force transfer mechanism between the concrete substrate and UHPC as a repair material. This paper presents an experimental and numerical programme designed to characterize the interfacial properties of concrete substrate and its effect on the bond strength between the two materials. The experimental programme evaluates the bond strength between the concrete substrates and UHPC with two different surface preparations while using bi-surface test and additional material tests, including cylinder and cube tests for compression property, direct tension test, and flexural test to complement UHPC tensile properties. Non-linear finite element analysis was conducted, which uses a numerical zero thickness volume model to define the interface bond instead of a traditional fixed contact model. The numerical results from the zero thickness volume model show good agreement with the experimental results with a reduction in error by 181% and 24% for smooth and rough interface surfaces if compared to the results from the model with a fixed contact.