Machine learning-based sensitivity of steel frames with highly imbalanced and high-dimensional data

Abstract

The machine learning-based feature selection approach is presented to estimate the effect of uncertainties and identify failure modes of structures that incorporate a low failure probability and high-dimensional uncertainties. As structures are designed to have few failures, a dataset classified based on the failure status becomes imbalanced, which poses a challenge for the predictive modeling of machine learning classifiers. Moreover, in order to improve the accuracy and efficiency of the model performance, it is necessary to determine the critical factors and redundant factors, especially for a large feature set. This study benchmarks the novel method for sensitivity analysis by using datasets that exacerbate the problems involved in class imbalance and large number of input features. This study investigates two planar steel frames with spatially uncorrelated properties between structural members. Geometric and material properties are considered as uncertainties, such as material yield stress, Young’s modulus, frame sway, and residual stress. Six feature importance techniques including ANOVA, mRMR, Spearman’s rank, impurity-based, permutation, and SHAP are employed to measure the feature importance and identify parameters germane to the prediction of structural failures. Logistic regression and decision tree models are trained on the important feature set, and the predictive performance is evaluated. The use of the feature importance approach for structures with a low probability of failure and a large number of uncertain parameters is validated by showing identical results with the reliability-based sensitivity study and appropriate predictive accuracy.