Hybrid Multilayer Perceptron Network for Explosion Blast Prediction
For decades, scientists have studied the blast wave profile produced by an explosive detonation. Based on a significant amount of experimental data, the blast wave propagation profile has been predicted under given parameters. However, most studies have only looked at the central point of initiation...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
semarak ilmu
2023
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/10633/1/J16654_d82c00fc7bca79478ca87a25b8913789.pdf http://eprints.uthm.edu.my/10633/ https://doi.org/10.37934/araset.30.3.265275 |
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| Summary: | For decades, scientists have studied the blast wave profile produced by an explosive detonation. Based on a significant amount of experimental data, the blast wave propagation profile has been predicted under given parameters. However, most studies have only looked at the central point of initiation for spherical form explosives. The purpose of this research is to compare the prediction performance of blast peak
overpressure based on type of explosive, shape of explosive and point of detonation. The blast profiles of Emulex and PE-4, as well as to develop a prediction model using a
Hybrid Multilayer Perceptron (HMLP) network. This experiment, which began at a distance of 1.2 m from the ground, employed a total of 500 grams of military explosive
and Emulex. At distances of 0.5 m, 1.0 m, 1.5 m, 2.0 m, 2.5 m, 3.0 m, 3.5 m and 4.0 m, the bomb was exploded. The Bayesian Regularization (BR) training algorithm is the best training algorithm for modelling Explosive Blast Prediction. |
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