Data-Driven Prediction Model for High-Strength Bolts in Composite Beams

Abstract

In recent years, the application of artificial intelligence-based methods to engineering problems has received consistent praise for their high predictive accuracy. This paper utilizes a BP neural network to predict the strength of steel–concrete composite beam shear connectors with high-strength friction-grip bolts (HSFGBs). These connectors are widely used in bridge and building construction due to their superior strength and stiffness compared to traditional beams. A validated finite element model was used to predict the strength of HSFGB shear connectors. A reliable database was created by analyzing 208 models with different characteristics for machine learning modeling. Previous studies have identified issues with result variation and overestimation or underestimation of shear connection strength. Among the machine learning methods evaluated, the backpropagation neural network model performed the best. It achieved a goodness of fit of over 93% in both the training and testing sets, with a low coefficient of variation of 6.50%. Concrete strength, bolt diameter, and bolt tensile strength were found to be important variables influencing the strength of shear connectors. Other variables showed a proportional or inverse relationship with compressive strength, except for concrete strength and bolt pretension. This study presents an accurate machine learning approach for predicting the strength of HSFGB shear connectors in steel–concrete composite beams. The study offers valuable insights into the effects of various variables on the performance of shear connection strength, providing support for structural design and analysis.

Keywords

• composite beams
• bolted shear connectors
• data-driven approach
• finite element modeling
• sustainability