Influence of the response modification factor on the bidirectional seismic performance of reinforced-concrete buildings
Abstract
Abstract Although the response modification factor (R) has been extensively studied, most prior work relied on simplified unidirectional systems, limiting applicability to real reinforced-concrete (RC) buildings subjected to bidirectional seismic excitation. This study examines how R influences the seismic performance and collapse capacity of RC buildings using the FEMA P695 methodology and Incremental Dynamic Analysis. Three-dimensional (3D) models of 1-, 2-, 4-, and 8-story RC buildings were analyzed under unidirectional and bidirectional ground motions using nonlinear time-history response analyses (NLTHA) to obtain equivalent systems. In parallel, equivalent two-degree-of-freedom (2DOF) systems with an elliptical bidirectional yield surface and IMK-based hysteretic deterioration were developed as efficient alternatives for collapse assessment. Results show that bidirectional excitation reduced the Collapse Margin Ratio (CMR) by 6.6%–30.1% relative to unidirectional analyses, and compliance with FEMA P695 acceptance criteria required lowering R from 8 to 4–5, depending on the structural system. The proposed 2DOF framework reproduced the 3D median collapse capacity with errors generally below 6%, offering an efficient tool for bidirectional collapse assessment and R calibration.
Read the paper
Related studies
- Current state and future directions for deep learning based automatic seismic fault interpretation: A systematic review
- Physics of Partially Saturated Porous Media: Residual Saturation and Seismic-Wave Propagation
- Modern Imaging Using Seismic Reflection Data
- Effects of marine seismic surveys on free-ranging fauna: a systematic literature review
- A review of the recent seismic activity in the southern part of egypt (upper egypt)
- A review of research on mechanism of seismic attenuation and energy compensation
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.