High-cycle fatigue life prediction of reinforced concrete deep beams

Concrete elements deteriorate as a result of continuous application of compressive fatigue loads. Irreversible deformation accumulates; hence, the effect on embedded steel reinforcing bars capacity and concrete resistance should be accounted for in the fatigue analysis of concrete structures. Experimental investigations were conducted to study the fatigue behaviour of eight small-scale reinforced concrete deep beams with a shear span to effective depth ratio of 1.25. Percentages of the diagonal cracking load from monotonic tests were used as fatigue loads. The deformation evolution within the shear spans of the deep beams were obtained by estimating the average principal and shear strain evolutions from the strain transformation analysis of LVDT (Linear Variable Displacement Transformer) data. Mid span deflections and reinforcement strain evolutions with proximity to a major concrete crack location were obtained. In all beams, failure occurred with fracture of the longitudinal reinforcement at the intersection with the major concrete crack. Maximum strain evolutions for shear reinforcement measured at regions around the bends were observed to be lower than the strain evolutions observed in the longitudinal reinforcement. This was attributed to the governing arch mechanism common with deep beams. The strut and tie method was modified to predict the fatigue life of the deep beams tested by modifying the constitutive models and effectiveness factor of concrete with fatigue damage models. To achieve this, the irreversible compressive fatigue strain in concrete is considered as a pseudo-load. The crack initiation life and the progressive crack growth of steel reinforcement are accounted for using strain-life models and linear elastic fracture mechanics, respectively. Within the developed algorithm, failure will occur when one of the evolving forces in either the concrete strut or steel reinforcement approaches the corresponding residual resistance capacity. (C) 2017 Elsevier Ltd. All rights reserved.