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Filtering by: Creator Wang, B. Conference Section/Track MS 40: Artificial Intelligence & Machine Learning in Design and Assessment of Structures

Structural Damage Prediction Under Seismic Sequence Using Neural Networks

Creator:

Lazaridis, Petros C., Kavvadias, Ioannis E., Demertzis, Konstantinos, Iliadis, Lazaros, Papaleonidas, Antonios, Vasiliadis, Lazaros K., and Elenas, Anaxagoras

Abstract:

Advanced machine learning algorithms, such as neural networks, have the potential to be successfully applied to many areas of system modelling. Several studies have been already conducted on forecasting structural damage due to individual earthquakes, ignoring the influence of seismic sequences, using neural networks. In the present study, an ensemble...

Resource Type:

Conference Proceeding

Full Text:

), the stress at tension cracking σt = 0.0022GPa, and the slope of the post-peak falling branch (E f b