Optimized Neural Network Prediction Model of Shape Memory Alloy and Its Application for Structural Vibration Control

The traditional mathematical model of shape memory alloy (SMA) is complicated and difficult to program in numerical analysis. The artificial neural network is a nonlinear modeling method which does not depend on the mathematical model and avoids the inevitable error in the traditional modeling metho...

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Autores principales:	Meng Zhan, Junsheng Liu, Deli Wang, Xiuyun Chen, Lizhen Zhang, Sheliang Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	SMA prediction model BP neural network genetic algorithm seismic response Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
Acceso en línea:	https://doaj.org/article/37e17f2000e5464daf322287420ae3d5
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spelling	oai:doaj.org-article:37e17f2000e5464daf322287420ae3d52021-11-11T18:08:04ZOptimized Neural Network Prediction Model of Shape Memory Alloy and Its Application for Structural Vibration Control10.3390/ma142165931996-1944https://doaj.org/article/37e17f2000e5464daf322287420ae3d52021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1944/14/21/6593https://doaj.org/toc/1996-1944The traditional mathematical model of shape memory alloy (SMA) is complicated and difficult to program in numerical analysis. The artificial neural network is a nonlinear modeling method which does not depend on the mathematical model and avoids the inevitable error in the traditional modeling method. In this paper, an optimized neural network prediction model of shape memory alloy and its application for structural vibration control are discussed. The superelastic properties of austenitic SMA wires were tested by experiments. The material property test data were taken as the training samples of the BP neural network, and a prediction model optimized by the genetic algorithm was established. By using the improved genetic algorithm, the position and quantity of the SMA wires were optimized in a three-storey spatial structure, and the dynamic response analysis of the optimal arrangement was carried out. The results show that, compared with the unoptimized neural network prediction model of SMA, the optimized prediction model is in better agreement with the test curve and has higher stability, it can well reflect the effect of loading rate on the superelastic properties of SMA, and is a high precision rate-dependent dynamic prediction model. Moreover, the BP network constitutive model is simple to use and convenient for dynamic simulation analysis of an SMA passive control structure. The controlled structure with optimized SMA wires can inhibit the structural seismic responses more effectively. However, it is not the case that the more SMA wires, the better the shock absorption effect. When SMA wires exceed a certain number, the vibration reduction effect gradually decreases. Therefore, the seismic effect can be reduced economically and effectively only when the number and location of SMA wires are properly configured. When four SMA wires are arranged, the acceptable shock absorption effect is obtained, and the sum of the structural storey drift can be reduced by 44.51%.Meng ZhanJunsheng LiuDeli WangXiuyun ChenLizhen ZhangSheliang WangMDPI AGarticleSMAprediction modelBP neural networkgenetic algorithmseismic responseTechnologyTElectrical engineering. Electronics. Nuclear engineeringTK1-9971Engineering (General). Civil engineering (General)TA1-2040MicroscopyQH201-278.5Descriptive and experimental mechanicsQC120-168.85ENMaterials, Vol 14, Iss 6593, p 6593 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	SMA prediction model BP neural network genetic algorithm seismic response Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
spellingShingle	SMA prediction model BP neural network genetic algorithm seismic response Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85 Meng Zhan Junsheng Liu Deli Wang Xiuyun Chen Lizhen Zhang Sheliang Wang Optimized Neural Network Prediction Model of Shape Memory Alloy and Its Application for Structural Vibration Control
description	The traditional mathematical model of shape memory alloy (SMA) is complicated and difficult to program in numerical analysis. The artificial neural network is a nonlinear modeling method which does not depend on the mathematical model and avoids the inevitable error in the traditional modeling method. In this paper, an optimized neural network prediction model of shape memory alloy and its application for structural vibration control are discussed. The superelastic properties of austenitic SMA wires were tested by experiments. The material property test data were taken as the training samples of the BP neural network, and a prediction model optimized by the genetic algorithm was established. By using the improved genetic algorithm, the position and quantity of the SMA wires were optimized in a three-storey spatial structure, and the dynamic response analysis of the optimal arrangement was carried out. The results show that, compared with the unoptimized neural network prediction model of SMA, the optimized prediction model is in better agreement with the test curve and has higher stability, it can well reflect the effect of loading rate on the superelastic properties of SMA, and is a high precision rate-dependent dynamic prediction model. Moreover, the BP network constitutive model is simple to use and convenient for dynamic simulation analysis of an SMA passive control structure. The controlled structure with optimized SMA wires can inhibit the structural seismic responses more effectively. However, it is not the case that the more SMA wires, the better the shock absorption effect. When SMA wires exceed a certain number, the vibration reduction effect gradually decreases. Therefore, the seismic effect can be reduced economically and effectively only when the number and location of SMA wires are properly configured. When four SMA wires are arranged, the acceptable shock absorption effect is obtained, and the sum of the structural storey drift can be reduced by 44.51%.
format	article
author	Meng Zhan Junsheng Liu Deli Wang Xiuyun Chen Lizhen Zhang Sheliang Wang
author_facet	Meng Zhan Junsheng Liu Deli Wang Xiuyun Chen Lizhen Zhang Sheliang Wang
author_sort	Meng Zhan
title	Optimized Neural Network Prediction Model of Shape Memory Alloy and Its Application for Structural Vibration Control
title_short	Optimized Neural Network Prediction Model of Shape Memory Alloy and Its Application for Structural Vibration Control
title_full	Optimized Neural Network Prediction Model of Shape Memory Alloy and Its Application for Structural Vibration Control
title_fullStr	Optimized Neural Network Prediction Model of Shape Memory Alloy and Its Application for Structural Vibration Control
title_full_unstemmed	Optimized Neural Network Prediction Model of Shape Memory Alloy and Its Application for Structural Vibration Control
title_sort	optimized neural network prediction model of shape memory alloy and its application for structural vibration control
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/37e17f2000e5464daf322287420ae3d5
work_keys_str_mv	AT mengzhan optimizedneuralnetworkpredictionmodelofshapememoryalloyanditsapplicationforstructuralvibrationcontrol AT junshengliu optimizedneuralnetworkpredictionmodelofshapememoryalloyanditsapplicationforstructuralvibrationcontrol AT deliwang optimizedneuralnetworkpredictionmodelofshapememoryalloyanditsapplicationforstructuralvibrationcontrol AT xiuyunchen optimizedneuralnetworkpredictionmodelofshapememoryalloyanditsapplicationforstructuralvibrationcontrol AT lizhenzhang optimizedneuralnetworkpredictionmodelofshapememoryalloyanditsapplicationforstructuralvibrationcontrol AT sheliangwang optimizedneuralnetworkpredictionmodelofshapememoryalloyanditsapplicationforstructuralvibrationcontrol
_version_	1718431936148406272

Optimized Neural Network Prediction Model of Shape Memory Alloy and Its Application for Structural Vibration Control

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