journal article·2019-03Open access

Twofold normalization of the cyclic creep curve of plain and steel-fiber reinforced concrete and its application to predict fatigue failure

S. Blasón, E. Poveda, G. Ruiz, H. Cifuentes, A. Fernández Canteli

Abstract

Abstract The cyclic creep curve or e-N curve recorded in the fatigue test of concrete contributes to more reliable prediction of the fatigue lifetime and its scatter. Such information is compatible with that provided from the lifetime tests (S-N field) thus reinforcing the potential information related to failure of structural concrete elements under fatigue. First, the normalization of the final number of cycles for each test provides an excellent fit thus allowing the e-N curves to be identified as cumulative distribution functions of the Weibull family. This fact points out the probabilistic character of the deformation accumulation process or that of the micro-fractures of the “primary elements” in the specimen or component concrete bulk. A second normalization, this time referred to the stress range, would facilitate the whole extension of the procedure regardless of the loading level to which the concrete is subjected. In this way, the conventional probabilistic assessment of the S-N field is enhanced by means of the fracture criterion based on the strain-normalizing model. The application of the procedure to concrete mixes with various percentages of steel-fiber reinforcement provides direct reference criteria of easier interpretation as being based on parameters exhibiting twofold meaning, physical and probabilistic. This allows a new limit state criterion to be established on a probabilistic basis and, consequently, higher reliability in the fatigue lifetime prediction of concrete with and without fiber reinforcement to be achieved.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Structural Behavior of Reinforced Concrete · Structural Load-Bearing Analysis · Geotechnical Engineering and Underground Structures

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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