Application of pool-based active learning in reducing the number of required response history analyses

A step by step method is presented for reducing the need for a large number of response history analyses (RHAs) in developing surrogates to predict the structural responses. These surrogates, which map ground motions features and characteristics of the structural systems into structural responses, are used in deriving fragility curves; and mostly are developed using machine learning algorithms. A machine learning algorithm, depending on the complexity of the model, requires a sufficient amount of training data to predict the outputs accurately. For complicated structural models, generating training data can be computationally demanding. Therefore, there is a need to generate the least amount of training data while preserving the accuracy of the prediction models. Towards this goal, a pool-based query-by-committee active learning (AL) algorithm is applied to choose probably the most informative data samples for surrogate training. A pool of unlabeled GM data samples is generated and a committee of artificial neural networks (ANNs) is defined to choose the smallest subset of the data that would be the most informative training data. The selected data samples meet informativeness, representativeness, and diversity criteria. The results of the applied case study show that implementing AL, can considerably reduce the size of required training data and consequently the amount of RHAs while improving the performance of the prediction models. Specifically, the findings demonstrate that AL improves the average F-scores of ANNs by about 10% and significantly reduces their variations, which indicates its stable behavior. (C) 2020 Elsevier Ltd. All rights reserved.