Enhancing Seismic Performance of Ductile Linked Rocking Steel Frames: A Parametric Investigation
Date
Authors
Supervisor
Item type
Journal Article
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier BV
Abstract
In a Ductile Linked Rocking Frame (DLRF) system, the steel braced frames remain elastic, incorporating Rocking Column Bases (RCBs) with coupon-type fuses at the horizontal rocking interface to facilitate uplift and rocking. This system is further enhanced by Corrugated Steel Panel (CSP) links, which are positioned along the vertical rocking interface. These CSP links act as additional structural fuses, dissipating energy and accommodating relative vertical movements between adjacent steel braced frames. This paper presents a numerical study to examine the seismic response of a typical DLRF and assess the influence of two key design parameters, the Self-Centering ratio (SC) and the Coupling Ratio (CR), on seismic-induced forces and drifts. A total of 40 prototype buildings, including two heights (4-storey and 8-storey) and various configurations of RCBs and CSP links, are analyzed using nonlinear time-history analysis in OpenSees with 22 far-field ground motions. The results indicate that an optimized DLRF configuration, especially with SC values in the range of 0.6 to 0.9 and CR values in the range of 0.5 to 0.6, achieves a uniform inter-storey drift distribution and negligible residual drift, even under Maximum Considered Earthquake (MCE) shaking. Finally, an improved capacity design procedure for DLRFs is proposed, which incorporates both SC and CR to guide engineering design.Description
Source
Journal of Constructional Steel Research, ISSN: 0143-974X (Print), Elsevier BV, 233, 109665-109665. doi: 10.1016/j.jcsr.2025.109665
Rights statement
This is the Author's Accepted Manuscript version of an article published in the Journal of Constructional Steel Research. The Version of Record can be found at https://doi.org/10.1016/j.jcsr.2025.109665 © 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.