a b s t r a c tThis study examines the performance of concrete under elevated temperatures at the meso-scale level of observation where aggregate particles and the embedding hydrated cement paste form interacting continua. Decomposing concrete into these two constituents leads to mismatch of the thermal and hydraulic transport properties and hence to self-equilibrating internal stresses introducing progressive damage of the mechanical response behavior of concrete. Thereby the internal stresses are disregarded by the macro-scale level approach when the heterogeneities are replaced by equivalent effective material properties using homogenization. In other terms, the macroscopic approach eliminates the contrast among the individual constituents and consequently negates the development of stresses causing pervasive microcracking in concrete.The current study resolves concrete into its main components, the aggregate particles and the cement paste, bonded by a weak interface transition zone that reduces to some extent the mismatch between the two constituents. The study illustrates the magnitude of the stress state in representative concrete specimens and the resulting damage evolution under high temperatures.
The bond behaviour between FRP and concrete elements is investigated, starting from available experimental evidences, through a calibrated and upgraded 3D mathematical-numerical model. The complex mechanism of debonding failure of FRP concrete reinforcement is studied within the context of damage mechanics to appropriately describe transversal effects and developing a realistic study of the delamination process.
The numerical simulation of structural mechanics applications via finite elements usually requires the solution of large-size and ill-conditioned linear systems, especially when accurate results are sought for derived variables interpolated with lower order functions, like stress or deformation fields. Such task represents the most time-consuming kernel in commercial simulators; thus, it is of significant interest the development of robust and efficient linear solvers for such applications. In this context, direct solvers, which are based on LU factorization techniques, are often used due to their robustness and easy setup; however, they can reach only superlinear complexity, in the best case, thus, have limited applicability depending on the problem size. On the other hand, iterative solvers based on algebraic multigrid (AMG) preconditioners can reach up to linear complexity for sufficiently regular problems but do not always converge and require more knowledge from the user for an efficient setup. In this work, we present an adaptive AMG method specifically designed to improve its usability and efficiency in the solution of structural problems. We show numerical results for several practical applications with millions of unknowns and compare our method with two state-of-the-art linear solvers proving its efficiency and robustness.
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