Stochastic-Based Nonlinear Numerical Modeling of Shear Critical RC Beam Repaired with Bonded CFRP Sheets - Yurdakul, Özgür; Tunaboyu, Onur; Řoutil, Ladislav; Avsar, Ozgur (American Society of Civil Engineers - ASCE, 2019)
The response of a shear critical substandard reinforced concrete beam and the structural repair of the predamaged beam by carbon-fiber-reinforced polymer sheets (CFRPs) were investigated by advanced numerical modeling method. First, the specimen was tested up to failure under monotonic loading in a four-point bending test. Owing to spatial variability of the concrete mechanical properties over a specific region, the damage was concentrated on one side of the beam. The severe shear cracks on the damaged side were then successfully repaired by externally bonded CFRP sheets. The former load-carrying capacity of the repaired beam was recovered, and the crack formation was transferred to the other side of the specimen, which had not been repaired. After repairing the unrepaired side of the beam using the same process, the failure mode was characterized by concrete crushing when the limiting strain of the concrete was reached under compression. The unsymmetrical crack pattern in the as-built specimen due to the uneven distribution of the concrete mechanical properties was reproduced by a random fields approach that combines the nonlinear finite-element method with stochastic sampling. In addition, the nonlinear response of the repair processes on the predamaged beam was adequately reproduced in the finite-element environment. The crack patterns and capacities obtained in the numerical solutions matched well the experimentally observed responses of the as-built and twice-repaired specimens.
Stochastic-Based Nonlinear Numerical Modeling of Shear Critical RC Beam Repaired with Bonded CFRP Sheets
Keywords: Repair; Carbon-fiber-reinforced polymer (CFRP); Shear failure; Finite-element method; Stochastic model; Random fields; Repair; Carbon-fiber-reinforced polymer (CFRP); Shear failure; Finite-element method; Stochastic model; Random fields