Abstract
Modeling response of structures under seismic loads is an important factor in Civil Engineering as it crucially affects the design and management of structures, especially for the high-risk areas. In this study, novel applications of advanced soft computing techniques are utilized for predicting the behavior of centrically braced frame (CBF) buildings with lead-rubber bearing (LRB) isolation system under ground motion effects. These techniques include least square support vector machine (LSSVM), wavelet neural networks (WNN), and adaptive neurofuzzy inference system (ANFIS) along with wavelet denoising. The simulation of a 2D frame model and eight ground motions are considered in this study to evaluate the prediction models. The comparison results indicate that the least square support vector machine is superior to other techniques in estimating the behavior of smart structures.
Highlights
Nowadays, smart structures are used widely to reduce and control the dynamic loads effects and structures response, respectively [1, 2]
The seismic response of a simulation of the centrically braced frame (CBF) steel building with lead-rubber bearing (LRB) isolation is predicted based on a development of advanced soft computing calculation methods, which are least square support vector machine (LSSVM), wavelet neural networks (WNN), and adaptive neurofuzzy inference system (ANFIS) with wavelet denoising
Two-delayed structure response is used with seismic effect to design the prediction model
Summary
Smart structures are used widely to reduce and control the dynamic loads effects and structures response, respectively [1, 2]. Structures built on lead-rubber bearing (LRB) isolations are ones of high effectiveness of structure control system [1,2,3], but the application of this system is still limited to use in real construction cases. The LRB is more effective in controlling steel and reinforced concrete buildings under seismic loads [1, 4]. Despite the responses of passive and active controller structures being modeled before [5, 6], this study presents the modeling prediction systems for a building with base isolation as a first application for this type of smart structures. The response prediction of structures with base isolation is the main aim of this study
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