Automatic strength assessment of the virtually modelled concrete interfaces based on shadow-light images

Abstract

The interface between concrete layers cast at different ages is present both in new construction, e.g., precast beams with cast in place decks, and in rehabilitation, e.g., jacketing of existing beams and columns. The interface strength ensures the element’s monolithic behaviour. This strength is mainly determined by the interface surface roughness. Unlike other material parameters, the interface strength is accessed with deterministic parameters (cohesion and friction) making design dependent on their correct assessment. Within this framework, the research study herein described aims to find non-deterministic parameters of the interface strength and an automatic method for their evaluation. The proposed parameters are derived from best-fit geostatistical models for roughness. These parameters are neither deterministic nor used for roughness and strength evaluation but they describe natural phenomena well. The automatisation is obtained via the deep learning process of the interface texture and strength. Since this process requires huge and not available data, real sources of data (i.e. surfaces) are substituted with the virtual Gaussian models generated with the geostatistical parameters obtained from the real surfaces. A similar process can be applied to the strength data if available in sufficient numbers. For short, the paper presents the application of the geostatistical parameters to the evaluation of both surface texture and strength of concrete to concrete interface, which have not been used before. It is shown that these parameters can be used to ‘store’ surface data in a compact form and subsequently texture or strength virtual reproduction is possible making deep learning and automatisation possible to perform.