Data Mining Model Based on Neural Network and Its Application on Anti-Climber Device

CHE Quanwei; LEI Cheng; LI Yuru; ZHU Tao; TANG Zhao; YAO Shuguang

doi:10.3969/j.issn.0258-2724.20200266

Volume 56 Issue 5

Oct. 2021

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Journal of Southwest Jiaotong University > 2021 > 56(5): 995-1001.

CHE Quanwei, LEI Cheng, LI Yuru, ZHU Tao, TANG Zhao, YAO Shuguang. Data Mining Model Based on Neural Network and Its Application on Anti-Climber Device[J]. Journal of Southwest Jiaotong University, 2021, 56(5): 995-1001. doi: 10.3969/j.issn.0258-2724.20200266

Citation:

CHE Quanwei, LEI Cheng, LI Yuru, ZHU Tao, TANG Zhao, YAO Shuguang. Data Mining Model Based on Neural Network and Its Application on Anti-Climber Device[J]. Journal of Southwest Jiaotong University, 2021, 56(5): 995-1001. doi: 10.3969/j.issn.0258-2724.20200266

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Data Mining Model Based on Neural Network and Its Application on Anti-Climber Device

doi: 10.3969/j.issn.0258-2724.20200266

CHE Quanwei^{1,2
,},
LEI Cheng³,
LI Yuru⁴,
ZHU Tao^{4
,
,},
TANG Zhao⁴,
YAO Shuguang¹

Received Date: 06 May 2020
Rev Recd Date: 21 Jun 2020

Available Online: 25 Aug 2020

Publish Date: 15 Oct 2021

Abstract

Abstract

Given the low efficiency of traditional finite element analysis method in calculating the crashworthiness of locomotive and vehicle structure, machine learning method is introduced to analyze and predict the crashworthiness and crash safety of vehicle structure on the basis of the existing simulation analysis data. Firstly, the neural network data mining model is established, and according to this model the prediction method for thecollision response of vehicle key structure. Secondly, tests are conducted to validate the finite element model of the anti-climber device, and the collision response data of the finite element model are obtained under different wall thicknesses. Then, the wall thickness of the anti-climber device is used as the model input, and the corresponding displacement, velocity, interfacial force and internal energy are used as the model output. The simulation data are used to train the model, the goodness fit of which is above 0.922. Finally, in order to test and verify the model, the predicted results of the energy absorption device are compared with the finite element simulation results, showing that the average relative errors of velocity, displacement, interfacial force, and internal energy are 7.10%, 4.51%, 6.20%, and 2.50%, respectively. The results indicate that the data mining model based on neural network can well reflect the collision characteristics of the anti-climber device with the precision; meanwhile, its computation time is greatly reduced and thecomputational efficiency is significantly improved.
- machine learning,
- neural network,
- collision response,
- data mining model,
- anti-climber device,
- computational efficiency

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Data Mining Model Based on Neural Network and Its Application on Anti-Climber Device

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