New Paper |Machine Learning-Based Estimation of Energy Dissipation Capacity of RC Shear walls

by Mehmet Oruç | Dec 22, 2022

Our Faculty Members Assoc. Prof. Zeynep Tuna Deger, Assoc. Prof. Gulsen Taskin Kaya, Asst. Prof. Fatih Sutcu and our graduate student Berkay Topaloglu have developed a machine-learning based model for the estimation of the energy dissipation capacity of reinforced concrete shear walls. Their study named “Machine learning-based estimation of energy dissipation capacity of RC shear walls” has been published in Structures journal.

The outcomes of this study are expected to contribute to the wall design process by (i) identifying the most influential wall properties on the seismic energy dissipation capacity of shear walls and (ii) providing predictive models that allow for comparison of different wall design configurations to achieve higher energy dissipation capacity.

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DOI: 10.1016/j.istruc.2022.08.114