Application of Support Vector Machine in Springback Forecasting for Steel Structure

Jian Guo Chen; Zhao Guang Li

doi:10.4028/www.scientific.net/AMM.166-169.1366

Paper Titles

Analysis of Principal Component - Application of SVM Model in Prediction of Ultimate Bearing Capacity of Static Pressure Pipe Pile
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Analysis of Kinematic Behavior on Landslide Groundwater at K144 Section along Da-Yu Highway
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Vibration Control Device and its Performance under Wind Load in High-Rise Buildings
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Dynamic Responses and Failure Modes of T-Shaped Concrete-Filled Steel Tube Core Columns under the Blast Loads
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Application of Support Vector Machine in Springback Forecasting for Steel Structure
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Finite Element Modeling for Health Monitoring and Condition Assessment of Large-Span Steel Roof Unloading Process
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Dynamic Stability Analysis of Big Span Steel Tubular Truss
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Experimental Investigation on Temperature Distribution of Reinforced Concrete Beam
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Experimental Study on Seismic Behaviors of Improved Composite Steel Staggered Truss Structures
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 166-169Application of Support Vector Machine in...

Application of Support Vector Machine in Springback Forecasting for Steel Structure

Abstract:

Support vector machine is applied to springback forecasting for steel structure in the paper. In the steel structure, pressure-pad-force, friction coefficient and die filleted corner have a certain influence on springback amount.We employ BP neural network to compare with support vector machine to show the superiority of support vector machine in this study. Finally,we give the comparison of the prediction error of springback for steel structure between support vector machine and BP neural network. Evidently,the springback prediction for steel structure of support vector machine is better than that of BP neural network.

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Periodical:

Applied Mechanics and Materials (Volumes 166-169)

Pages:

1366-1369

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.166-169.1366

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Online since:

May 2012

Authors:

Jian Guo Chen, Zhao Guang Li

Keywords:

Forecasting Model, Springback, Steel Structure, Support Vector Machine (SVM)

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