Finite Element Simulation of Axial Crush of Thin-Walled Tubes with Different Cross-Sections: Vehicle/Pole Impact Applications

2011 ◽  
Vol 3 (3) ◽  
Author(s):  
Ahmed Elmarakbi ◽  
Niki Fielding ◽  
Homayoun Hadavinia
Keyword(s):  
Finite Element ◽  
Finite Element Simulation ◽  
Cross Sections ◽  
Axial Crush ◽  
Element Simulation ◽  
Thin Walled

Finite element simulation of the axial collapse of metallic thin-walled tubes with octagonal cross-section

2003 ◽  
Vol 41 (10) ◽  
pp. 891-900 ◽  
Author(s):  
A.G Mamalis ◽  
D.E Manolakos ◽  
M.B Ioannidis ◽  
P.K Kostazos ◽  
C Dimitriou
Keyword(s):  
Finite Element ◽  
Finite Element Simulation ◽  
Cross Section ◽  
Element Simulation ◽  
Thin Walled

Experimental and finite element simulation investigation of axial crushing of grooved thin-walled tubes

2014 ◽  
Vol 77 (9-12) ◽  
pp. 1627-1643 ◽  
Author(s):  
S. E. Mirmohammadsadeghi ◽  
Kh. Khalili ◽  
S. Y. Ahmadi ◽  
S. J. Hosseinipour
Keyword(s):  
Finite Element ◽  
Finite Element Simulation ◽  
Axial Crushing ◽  
Element Simulation ◽  
Thin Walled

scholarly journals Data-Driven Decision-Making in the Design Optimization of Thin-Walled Steel Perforated Sections: A Case Study

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Zhi-Jun Lyu ◽  
Qi Lu ◽  
YiMing Song ◽  
Qian Xiang ◽  
Guanghui Yang
Keyword(s):  
Decision Making ◽  
Finite Element ◽  
Finite Element Simulation ◽  
Optimization Design ◽  
Data Driven ◽  
Physical Test ◽  
Element Simulation ◽  
Industrial Big Data ◽  
Thin Walled

The rack columns have so distinctive characteristics in their design, which have regular perforations to facilitate installation of the rack system that it is more difficult to be analyzed with traditional cold-formed steel structures design theory or standards. The emergence of industrial “big-data” has created better innovative thinking for those working in various fields including science, engineering, and business. The main contribution of this paper lies in that, with engineering data from finite element simulation and physical test, a novel data-driven model (DDM) using artificial neural network technology is proposed for optimization design of thin-walled steel specific perforated members. The data-driven model based on machine learning is able to provide a more effective help for decision-making of innovative design in steel members. The results of the case study indicate that compared with the traditional finite element simulation and physical test, the DDM for the solving the hard problem of complicated steel perforated column design seems to be very promising.

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