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Numerical and Neural Network Investigation of a New Dual Self‐centering Energy Dissipation Device
Journal article   Peer reviewed

Numerical and Neural Network Investigation of a New Dual Self‐centering Energy Dissipation Device

Vasileios C. Kamperidis, Dan V. Bompa, Georgios Chliveros, Themelina S. Paraskeva, Konstantinos N. Kalfas and John Bellos
ce/papers, Vol.9(2-3), pp.824-829
09/2026

Abstract

Artificial Neural Network (ANN) Energy Dissipation Seismic Design Seismic Resilience Self‐centering Structural Connection
Proposed self‐centering (SC) seismic systems can reduce residual drift, but their SC and energy‐dissipating (ED) solutions often face limitations, including reliance on yielding components, full‐bay tendon layouts, and detailing complexity in beam‐column connections. This paper investigates a compact SC‐ED sub‐assembly comprising a high‐strength post‐tensioned multi‐wire steel tendon routed in a 180° path around a roller bearing, with both tendon ends aligned. A three‐dimensional nonlinear finite element (FE) workflow, building on earlier numerical work and selectively refined here, is used to evaluate response under a displacement‐controlled cyclic protocol representative of seismic demands. Results show stable flag‐shaped hysteresis without residual displacement and elastic behaviour of the primary components. ED is interpreted as arising from tendon‐bearing interface friction and curvature‐activated internal mechanisms in the multi‐wire tendon. An artificial neural network (ANN) surrogate model trained on force‐displacement data reproduces the nonlinear response on unseen data, enabling rapid prediction. The study demonstrates the feasibility of a tribology‐driven curved tendon‐bearing SC‐ED sub‐assembly for seismic‐resilient structural connections.
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https://doi.org/10.1002/cepa.70795View
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