Machine learning applications for assessment of dynamic progressive collapse of steel moment frames

Abstract

A highly efficient implementation of machine learning (ML) framework is developed for assessing the dynamic increase factor (DIF) used in nonlinear static analysis (pushdown). Analysis datasets with a total of 3992 samples consisting of training (70%) and testing (30%) are generated by the Monte Carlo simulation that carried out by Sap2000 program application programming interface (API) link with Python in PyCharm software. The generated datasets are evaluated by the correlation matrix and relative feature importance of features that consist of damping ratio (Zeta) range, period of vertical vibration (Tv), ratio of duration of a column removal to Tv (Tau), ratio of factored moment to yielding moment (MMR) and ratio of factored rotation to yielding rotation (TTR). The current study incorporates implementation of K-nearest neighbors algorithm (KNN), extreme gradient boosting (XGBoost), back-propagation neural network (BPNN), and one-dimensional convolutional neural network (1DCNN). The results confirm the ability of the developed framework to efficiently implement regression analysis for the DIF.