Estimating Peak Floor Acceleration Using Artificial Neural Networks
Publication: Lifelines 2022
ABSTRACT
Peak floor acceleration (PFA) can be useful immediately after an earthquake to potentially optimize recovery resources, especially in cases of invisible damage that can be detrimental for essential lifelines. While code equations estimate PFA based on the design spectrum, simplified methods, available in the literature, have proven to be cumbersome for real-life structures. To mitigate this, a machine learning (ML) approach using Artificial Neural Networks (ANN) was investigated to quickly predict PFA using ground motion parameters and structural properties without the need for time-history analysis (THA) or providing ground motion time series. While previous studies fed ground motion time-series data to the ML model, this study investigates the use of ground motion parameters instead. Data from linear elastic THA were used to train the model. Results illustrated that a shallow ANN predicted PFA with coefficient of determination (R2) reaching 0.8; however, additional studies are underway to enhance the model performance.
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Lifelines 2022
Pages: 648 - 659
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Published online: Nov 16, 2022
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