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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semarak ilmu
2023
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| 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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| author | Muhamad Hadzren Mat, Muhamad Hadzren Mat Prakash Nagappan, Prakash Nagappan Fakroul Ridzuan Hashim, Fakroul Ridzuan Hashim Khairol Amali Ahmad, Khairol Amali Ahmad Mohd Sharil Saleh, Mohd Sharil Saleh Khalid Isa, Khalid Isa Khaleel Ahmad, Khaleel Ahmad |
| author_facet | Muhamad Hadzren Mat, Muhamad Hadzren Mat Prakash Nagappan, Prakash Nagappan Fakroul Ridzuan Hashim, Fakroul Ridzuan Hashim Khairol Amali Ahmad, Khairol Amali Ahmad Mohd Sharil Saleh, Mohd Sharil Saleh Khalid Isa, Khalid Isa Khaleel Ahmad, Khaleel Ahmad |
| author_sort | Muhamad Hadzren Mat, Muhamad Hadzren Mat |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. |
| format | Article |
| id | my.uthm.eprints-10633 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2023 |
| publisher | semarak ilmu |
| record_format | eprints |
| spelling | my.uthm.eprints-106332024-01-15T07:31:23Z http://eprints.uthm.edu.my/10633/ Hybrid Multilayer Perceptron Network for Explosion Blast Prediction Muhamad Hadzren Mat, Muhamad Hadzren Mat Prakash Nagappan, Prakash Nagappan Fakroul Ridzuan Hashim, Fakroul Ridzuan Hashim Khairol Amali Ahmad, Khairol Amali Ahmad Mohd Sharil Saleh, Mohd Sharil Saleh Khalid Isa, Khalid Isa Khaleel Ahmad, Khaleel Ahmad T Technology (General) 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. semarak ilmu 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/10633/1/J16654_d82c00fc7bca79478ca87a25b8913789.pdf Muhamad Hadzren Mat, Muhamad Hadzren Mat and Prakash Nagappan, Prakash Nagappan and Fakroul Ridzuan Hashim, Fakroul Ridzuan Hashim and Khairol Amali Ahmad, Khairol Amali Ahmad and Mohd Sharil Saleh, Mohd Sharil Saleh and Khalid Isa, Khalid Isa and Khaleel Ahmad, Khaleel Ahmad (2023) Hybrid Multilayer Perceptron Network for Explosion Blast Prediction. Journal of Advanced Research in Applied Sciences and Engineering Technology, 30 (3). pp. 265-275. ISSN 2462-1943 https://doi.org/10.37934/araset.30.3.265275 |
| spellingShingle | T Technology (General) Muhamad Hadzren Mat, Muhamad Hadzren Mat Prakash Nagappan, Prakash Nagappan Fakroul Ridzuan Hashim, Fakroul Ridzuan Hashim Khairol Amali Ahmad, Khairol Amali Ahmad Mohd Sharil Saleh, Mohd Sharil Saleh Khalid Isa, Khalid Isa Khaleel Ahmad, Khaleel Ahmad Hybrid Multilayer Perceptron Network for Explosion Blast Prediction |
| title | Hybrid Multilayer Perceptron Network for Explosion Blast Prediction |
| title_full | Hybrid Multilayer Perceptron Network for Explosion Blast Prediction |
| title_fullStr | Hybrid Multilayer Perceptron Network for Explosion Blast Prediction |
| title_full_unstemmed | Hybrid Multilayer Perceptron Network for Explosion Blast Prediction |
| title_short | Hybrid Multilayer Perceptron Network for Explosion Blast Prediction |
| title_sort | hybrid multilayer perceptron network for explosion blast prediction |
| topic | T Technology (General) |
| url | 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 |
| url_provider | http://eprints.uthm.edu.my/ |