Using Incremental Learning With Rehearsal to Enhance Global Collapse Prediction Machine Learning Models Across Diverse Steel Building Datasets

ABSTRACT 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.