Integration of Finite Element Analysis and Machine Learning for Assessing the Spatial-Temporal Conditions of Reinforced Concrete

Composite reinforcements are attracting attention in the reinforced concrete (RC) field for their high corrosion resistance, low thermal conductivity, and low electromagnetic interference behavior. However, compared to metallic reinforcements, composites are less ductile and may lead to brittle failure. Three-point flexural tests provide information on the mechanical behavior of metal- and composite-reinforced concrete beams with distinct crack patterns. The structural conditions and failure mechanisms can be defined based on stress change and crack propagation. This study employs finite element analysis (FEA) to simulate the mechanical responses of composite- and metal-reinforced concrete beans under three-point flexural tests and predict the crack propagation in the beams. Machine learning-based algorithms are trained using FEA data to assess the spatial–temporal conditions of the RC beams. The findings indicate that composite rebars provide better reinforcement than metallic rebars in terms of stress fields (30.27% less stress in composite rebars) and crack propagation (fewer cracks in composite RC beams), with the initiation of shear cracks and maximum von Mises stress in rebars being correlated. The findings highlight the effectiveness of the Random Forest Regression (RFR) algorithm (R2=0.96) in assessing RC beam conditions under flexural loads, offering insights for efficient industry applications.

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