CANFIS‐Based Semi‐active Vibration Control of Stochastically Excited High‐Rise Civil Engineering Structures with Nonlinearities and Uncertainties. Issue 1 (18th November 2019)

• Record Type: Journal Article
• Authors: Altay, Okyay; Klinkel, Sven
• Other Names: Eberhardsteiner J. guestEditor.; Schöberl M. guestEditor.
• Is Part Of: Proceedings in applied mathematics and mechanics. Volume 19:Issue 1(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-11-18
• Subjects: Applied mathematics -- Periodicals; Engineering mathematics -- Periodicals; Mathematical physics -- Periodicals; 519
• DOI: 10.1002/pamm.201900132
• Languages: English
• ISSNs: 1617-7061

Abstract

Abstract: This study introduces a co‐active neuro‐fuzzy inference system (CANFIS) based vibration control strategy for high‐rise civil engineering structures, such as buildings, wind turbines and towers. The proposed approach uses semi‐active dampers, which are distributed in the structure. Attached to the structural members, semi‐active dampers generate adaptive restoring forces to reduce the structural vibrations by autonomously tuning their parameters. The nonlinear behavior and the uncertainties involved in the mathematical models of both the semi‐active dampers and the structures require sophisticated control strategies. Previous studies have shown that fuzzy logic controllers (FLCs) can overcome these challenges and perform effectively with semi‐active dampers to mitigate the structural vibrations. Nevertheless, FLCs require a predefined parameter tuning of their fuzzy rules and membership functions, which must be performed based on the knowledge of a human expert. Combining FLCs with artificial neural networks (ANNs), hybrid systems are proposed, which can determine the parameters of FLCs by using learning based training and adaptation strategies of ANNs. However, there is only limited research investigating the performance of these adaptive systems in the field of vibration control of civil engineering structures. The present work is concerned with a numerical formulation of such an adaptive system, in particular the CANFIS, and its implementation on a non‐linear seismically excited three‐story steel benchmark building with semi‐active dampers. To determine the premise parameters of the membership functions and the consequent parameters of the fuzzy rules a hybrid training method is performed using the recursive least‐square algorithm and the steepest descent method. Investigations with historic earthquakes show a significant reduction of the roof displacements of the building. For instance, during the Hachinohe earthquake the peak value of the displacement is reduced by 87 %.