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March 14, 2026Engineering Structures0 citationsOpen Access

Modeling Inelastic Response Ratios in Buildings with Damping Under Near-Fault Motions

Investigation of inelastic response ratios for buildings with damping subjected to near-fault ground motions using numerical simulations and transformer-based models

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Authors

LKLakshitha KonaraTDThilini DeshikaRGRajeswaran Gobirahavan

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Overview

Numerical simulations assessed inelastic responses in SDOF systems with viscous damping, indicating that neural networks can enhance prediction accuracy.

Key Points

  • This research focuses on the inelastic response ratios for structures under near-fault ground motions, emphasizing the role of damping.
  • Simulated inelastic responses of SDOF systems under near-fault motions using numerical simulations.
  • Evaluated the effects of ductility and viscous damping on inelastic responses.
  • Proposed empirical equations for inelastic displacement and velocity ratios.
  • Developed neural network models trained on a large dataset to enhance predictions.
  • Neural networks achieved a high accuracy (R2 = 0.944 for inelastic displacement ratio) on unseen data.
  • Empirical equations identify general trends, while neural networks capture minor variations in responses.
  • Demonstrated the effectiveness of data-driven methods in predicting inelastic responses.

Cite This Study

Konara et al. (2026) studied this question.

synapsesocial.com/papers/69b4b9fb18185d8a3980250dhttps://doi.org/10.1016/j.engstruct.2026.122554
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