The accurate and efficient evaluation of structural performance of in-service cracked reinforced concrete (RC) structures depends on the evaluation-oriented finite element modelling and analysis of cracked RC structures. This study presents a digital twin-empowered performance evaluation of cracked RC beams. RC flexural beams are tested, monitored, and inspected until failure and taken as physical entities, whereas the 3D point cloud-assisted nonlinear finite element models of the RC flexural beams with simulated cracks are regarded as virtual entities. Based on sensitivity analysis and multi-objective particle swarm optimization algorithm, the static deflection, strain, and crack characteristic information collected from the physical entity are mingled with the virtual entity to achieve a digital twin of the test RC flexural beam. The digital twin of the RC flexural beam is finally used to evaluate and predict the structural performance of the flexural beam. The results obtained from this study demonstrate that the digital twin-empowered performance evaluation is feasible, and that the quality and capacity of the structural performance evaluation are enhanced.
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