2023
DOI: 10.1080/17445302.2022.2162754
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On the crashworthiness optimisation of a new multi-corner tube under axial loading

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Cited by 7 publications
(3 citation statements)
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“…95 All these parameters could be studied in a future work. Najibi et al 96,97 proved that Multi-objective optimization with Pareto fronts is effective for crashworthiness analysis. Therefore, this method will be adapted in future studies to analyze the crushing of GFRP perforated tubes.…”
Section: Future Workmentioning
confidence: 99%
“…95 All these parameters could be studied in a future work. Najibi et al 96,97 proved that Multi-objective optimization with Pareto fronts is effective for crashworthiness analysis. Therefore, this method will be adapted in future studies to analyze the crushing of GFRP perforated tubes.…”
Section: Future Workmentioning
confidence: 99%
“…Also, the proposed hybrid network was compared with the well-known traditional machine learning methods, such as k-NN [66], RF [33], and GBM [44]. Additionally, one of the earliest deep learning methods, namely, the group method of data handling (GMDH) [67][68][69], was used for comparison. Each experiment was repeated ten times to alleviate the impact of randomization, and the mean values were reported.…”
Section: Prediction Performance Comparison Of Different Neural Networ...mentioning
confidence: 99%
“…In this way, the input vector can be mapped to the hidden space without weight, while the mapping from the hidden layer to the output space is linear; that is, the output of RBF is a linear weighted sum of unit outputs. Different from traditional data processing methods such as group method of data handling (GMDH), GMDH increases the complexity of the research due to the interrelationships between research data [30,31]. RBF has the advantages of simple structure, simple training, fast learning convergence, and can approximate any nonlinear function.…”
Section: 𝜑(â€–đ‘„ − đ‘„mentioning
confidence: 99%