Improving Structural Safety with Machine Learning: Shear Strength Prediction in Interior Beam-Column Joints

Determining the shear capacity of joints between columns and beams within a structure is crucial to guarantee its safety and stability. It directly impacts buildings' structural integrity, cost-effectiveness, and resilience, making it a critical aspect of structural engineering and construction. Estimating shear properties in beam-column joints is done via machine learning due to its ability to capture complex relationships, adapt to diverse data, and automatically identify relevant features, potentially offering improved accuracy and insights compared to traditional methods. This paper includes creating a machine-learning regression model for predicting joint shear strength in interior beam-column joints. It involves the analysis of a comprehensive dataset comprising 445 data points with 17 variables sourced from 100 research papers. The primary objective is to craft a machine-learning regression model capable of accurately forecasting joint shear strength. To achieve this goal, a multitude of methodologies have been explored, including the application of 2 machine learning regression techniques and two codes of practice (Step-wise Linear Regression, Medium neural networks, and EN 1998-1:2004, NZS 3101:1-2006). Of the two methods, step-wise linear regression gave the best results in predicting the shear capacity of interior column-beam connections.