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Generalization of machine learning (ML) surrogate models across distinct databases is underexplored, despite being crucial as retraining the entire model every time new data become available is inefficient. This study proposes an incremental learning methodology to improve ML models' prediction of seismic collapse of steel moment‐resisting frames (SMRFs) across distinct datasets. Three boosting algorithms, XGBoost, LightGBM, and CatBoost, were trained on a source dataset to generate surrogate ML models that can predict the SMRF's seismic response. Thereafter, the ML models were used to predict the response on a new (target) dataset of SMRFs that differ in geometric dimensions and design approaches. Initially, boosting models trained on one dataset performed poorly on another dataset, even if the datasets displayed similar characteristics and consistent feature importance rankings. Incorporation of incremental learning improved the prediction on the target dataset, but introduced catastrophic forgetting that reduced the effectiveness of the ML model on the source dataset, a problem mitigated with a rehearsal strategy. Incremental learning with rehearsal yields results comparable to those obtained by fully retraining with both source and target datasets, resulting in an effective method for ML transferability, without having to retrain entire databases and without reducing the effectiveness of ML models on the source database. 

This study implements an ensemble neural network (ENN) to obtain representative and stable feature importance contributions to collapse prediction of steel moment resisting frames (SMRFs). The feature importance assessment includes global sensitivity analyses (GSAs) and feature extraction techniques. To construct the ENN, hundreds of neural network (NN) architectures are generated and an elite set of 50 NNs is initially obtained using a multi-criteria decision analysis (MCDA). A final elite set of 50 NNs is generated after applying a genetic algorithm to the initial elite set, which undergoes several iterations of crossover and mutation. To generate the dataset of SMRF collapse status, thousands of nonlinear time history analyses are carried out on frame systems ranging from 2 to 20 stories. The frames are based on five SMRF baseline systems with variability in input parameters that are randomly selected. 

Machine learning (ML) techniques were generated for different information levels to identify the minimum set of system parameters required for predicting collapse and maximum interstory drift (SDR_max) of steel moment resisting frame (SMRF) buildings. Five baseline modern SMRFs were evaluated under seismic loading with varying system and ground motion (GM) parameters to generate a database. Classification and regression-based ML models were tested at three system information levels to predict collapse and SDR_max, respectively. The ML predictions were mainly controlled by GM parameters and were relatively insensitive to system parameters defining nonlinear behavior, such as spring backbone curve features. 

This study applies machine learning (ML) models to predict the collapse limit state of steel moment resisting frame (SMRF) buildings, considering uncertainties in system parameters and input ground motion characteristics. Structural global collapse is affected by a large number of linear and nonlinear system parameters. One of the main goals of the study is to find the effectiveness of ML methods to predict collapse, as the number of system’s features is reduced. Because of the lack of sufficient experimental data, an ML approach is followed in which three code-compliant SMRF buildings of varying heights (2, 4 and 8 stories), are evaluated up to the collapse limit state, using nonlinear time history analyses. Variability in system parameters and ground motions, as well as potential correlation among some of the parameters, is considered to generate a database of more than 19,000 realizations of collapsed and non-collapsed systems. The ML models are trained and tested with this database, and the efficiency of the models is categorized using different metrics, such as accuracy, F1-score, precision, and recall. Six different ML classification-based techniques are employed to predict collapse, finding that boosting algorithms (eg, AdaBoost and XGBoost) are the best methods for collapse status classification of the evaluated structural systems. Permutation feature importance is applied to identify the main contributors to collapse. The ML models are then retrained using less features, considering first removal of nonlinear deteriorating parameters, and then removal of the hardening nonlinear parameters.