Unified machine-learning-assisted design of stainless steel bolted connections.

For the design of stainless steel bolted connections, current design codes firstly calculate the design resistance of each potential failure mode and then take the minimum of the design resistances calculated from all the potential failure modes as the final design failure load. However, this design framework has been found to be tedious and also leads to inaccurate failure load predictions. In this paper, a more accurate and unified predictive framework for stainless steel bolted connections made of different materials with various configurations and bolt hole patterns at both room and elevated temperatures is proposed based on machine learning. This paper starts with a collection of 301 experimental data of stainless steel bolted connections to establish a database. Then, regression models for failure load predictions were developed and trained by six machine learning algorithms, including Decision Tree, Random Forest, Support Vector Machine, Adaptive Boosting, Extreme Gradient Boosting and Cat Boosting, with the key hyperparameters for each machine learning algorithm tuned. This is followed by the evaluation of the model performance by means of a series of statistical indices, with the results indicating that the regression model trained by Support Vector Machine has the best model performance. On the basis of the collected experimental data, the regression model trained by Support Vector Machine and current design codes were assessed and compared, showing that the current design codes are rather inaccurate, while the machine-learning-assisted design method is a unified method and can provide accurate and consistent failure load predictions for stainless steel bolted connections made of different materials with various configurations and bolt hole patterns at both ambient and elevated temperatures. • A database of stainless steel bolted plate-to-plate connections was established. • Regression models were developed and trained by six machine learning algorithms. • The regression model with the best performance was compared with existing design codes. • The machine-learning-assisted design method resulted in much improved design accuracy and consistency. [ABSTRACT FROM AUTHOR]