Analytical and Experimental Study for Development of Composite Coil Springs

Oh, Sung Ha; Choi, Bok Lok

doi:10.3795/ksme-a.2014.38.1.031

Cited by 8 publications

(3 citation statements)

References 2 publications

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“…vibration, assess the structural reliability, and predict concrete compressive strength [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42].…”

Section: Mechanical Behavior Of Coil Springmentioning

confidence: 99%

“…In a study on the coil spring material, Sawanobori [22] analyzed the dynamic characteristics of coil springs according to the material properties of the steel, using a finite element method. Research using shape memory alloy was conducted by Kwon [23] and Lee [24], and Oh [25] investigated the design factors involved in using carbon-fiber-reinforced polymer (CFRP) for a light weight coil spring.…”

Section: Introductionmentioning

confidence: 99%

“…Since then, ANN have become a new alternative approach for pattern recognition in many fields of application, including biology [27], economics [28], environment [29], manufacturing [30], medicine [31], and the military [32]. In civil engineering, neural networks have been applied to assess the damage to a structure, analyze the behavior of the material, design the optimization of the structure, monitor the groundwater level, control the structural vibration, assess the structural reliability, and predict concrete compressive strength [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

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An Intelligent Process to Estimate the Nonlinear Behaviors of an Elasto-Plastic Steel Coil Damper Using Artificial Neural Networks

Chang

Cho²

2021

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This study developed a nonlinear behavior prediction model for elasto-plastic steel coil dampers (SCDs) using artificial neural networks (ANN). To train the ANN, first, the input and output data of the behavior of the elasto-plastic SCD was prepared. This study utilized the design parameters and load–displacement curves of the SCD to train the ANN. The elasto-plastic load–displacement curve of the SCD was obtained from simulation results using an ANSYS workbench. The design parameters (wire diameter, internal diameter, number of active windings, yield strength) of the SCD were defined as the input patterns, while the yield deformation, first stiffness, and second stiffness were output patterns. During learning of the neural network model, 60 datasets of the SCD were used as the learning pattern, and the remaining 21 were used to verify the model. Although this study used a small number of learning patterns, the ANN predicted accurate results for yield displacement, first stiffness, and second stiffness. In this study, the ANN was found to perform very well, predicting the nonlinear response of the SCD, compared with the values obtained from a finite element analysis program.

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“…vibration, assess the structural reliability, and predict concrete compressive strength [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42].…”

Section: Mechanical Behavior Of Coil Springmentioning

confidence: 99%