Experimental validation of the strut-and-tie design for reinforced concrete massive structures by deformation flow analysis

International audience; The design of reinforced concrete structures follows prescriptions of codes and standards (e.g., ACI and Eurocode). The strut-and-tie method is the only approach detailed in standards for the design of reinforcement of massive structures. Stut-and-tie methods are currently showing a strong interest and several techniques are based on automatic strategies and topological optimization. However, this method is based on a strong hypothesis associated with linear elastic analyses. In particular, the occurrence of pre-existing cracks is not considered when drawing the strut-and-tie model. This work aims to understand the role of an initially cracked state and, thus, the loading history in the design and choice of strut and tie directions. An experimental test is analyzed for a real size corbel loaded in two different inplane directions with forces not applied simultaneously. Hence, the specimen was cracked when the second force was applied. The corbel has minimal reinforcement to avoid brittle failure but should not affect the crack directions. The instrumentation used, combining digital image correlation and optical fiber sensing, allows for monitoring during the test and provides insight into the cracking history and the deformations in visible surfaces and in the bulk. Digital image correlation was used over the entire top surface of the corbel to measure displacement fields and thus strain fields, as well as information on cracking during the test. Optical fibers were installed within the specimen to measure strains in bulk. This measurement allows for the detection of cracks in the sample volume and thus complements digital image correlation measurements for fibers close to the surface. The results provide key data on the flow history of mechanical fields within the structure under different loads. This information helped clarify the domain of validity of strut-and-tie fundamental assumptions and may improve this design approach.