Application of Artificial Neural Network for Prediction of Concrete–High-Performance Concrete Interfacial Bond Strength After Exposure to Elevated Temperature

다국어 초록 (Multilingual Abstract)

High-performance concrete (HPC) is a new advanced building material for highway bridges, building construction, and repair/strengthen concrete structures with fire risk owing to its high fire resistance. The concrete composites should have interfacial bond strength (IBS) that is sufficient to transfer load between concrete components. When those composite structures are exposed to fire, horizontal cracks have been observed, and in some cases, the concrete layers have separated depending on the fire intensity. Therefore, the assessment of the IBS between the two concrete layers after exposure to fire is important for examining the entire fire behavior. Thus, the purpose of this work is to create an artificial neural network (ANN) model between statistically important factors and the IBS after exposure to elevated temperatures for using in the structural fire design of composite concrete layers. A total of 467 data points, including 252 data points from the slant shear test, 87 data points from the push-off test, and 128 data points from the tensile test, have been collected from literature reviews. Firstly, the independent parameters such as interfacial surface roughness, temperature exposure, part of the specimen exposed to temperature, type of concrete overlay, and fiber content introduced in the concrete overlay were carefully analyzed to identify the statistically important parameters and their impact on the IBS. Secondly, a designed ANN model has been developed to predict the IBS based on the type of test technique, interfacial surface roughness, temperature exposure, type of concrete overlay, and fiber content. Moreover, a mathematical model has been proposed to predict the IBS between concrete substrate and HPC after exposure to elevated temperature. Finally, the predicted IBS from the design ANN model and the mathematical IBS were compared with the available empirical models from literature. The outcome results demonstrated that the design ANN model was able to predict the IBS between two concrete layers after exposure to elevated temperatures with a coefficient of determination R2 of 0.97, while the mathematical IBS gave a good accuracy for predicting the IBS in the case of the interface under combined stress with R2 equal to 0.90. This study effectively bridges the gaps in both theoretical and experimental findings by integrating ANN models with advanced computational techniques and robust statistical analyses. This multifaceted approach not only enriches our understanding of the topic, but also provides more precise insights and predictive capabilities.