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This research proposes a surrogate model to predict the seismic response of individual structural elements in structures whose inherent vertical and horizontal irregularities result in components with different seismic vulnerabilities. A frequency-based data-driven model was developed which predominantly uses the frequency spectrum of earthquakes as input data. The seismic responses of several structural components can be simultaneously generated as output using the proposed model. A comparison of structure fragility assessments obtained with a conventional approach, and the proposed Deep Learning-based approach, was conducted to verify the accuracy of the proposed method's prediction capability. [ABSTRACT FROM AUTHOR]

Non-ductile reinforced concrete frames have seismic vulnerabilities due to inadequate reinforcing details. Adequately characterizing the performance of these details is critical to the development of analytical models. This study presents a methodology to simulate the response of such frames with and without fiber-reinforced polymer column jackets. The bond-slip effects between reinforcing bars and surrounding concrete, observed in column lap-splice and beam–column joints, are modeled with one-dimensional slide line models in LS-DYNA. The model is defined from failure modes and bonding conditions observed in full-scale dynamic tests and can predict story displacements, inter-story drifts, and damage mechanisms. [ABSTRACT FROM AUTHOR]