Hybrid Multilayer Perceptron Network for Explosion Blast Prediction

Authors

  • Muhamad Hadzren Mat WBE Technologies Sdn. Bhd., 218 Jalan Ampang, 54500, Kuala Lumpur, Malaysia
  • Prakash Nagappan Royal Service Corp Directorates, Army Head Quarters, Ministry of Defense, Jalan Padang Tembak, 50634 Kuala Lumpur, Malaysia
  • Fakroul Ridzuan Hashim Faculty of Engineering, National Defence University of Malaysia, Sg. Besi Camp, 57000 Kuala Lumpur, Malaysia
  • Khairol Amali Ahmad Faculty of Engineering, National Defence University of Malaysia, Sg. Besi Camp, 57000 Kuala Lumpur, Malaysia
  • Mohd Sharil Saleh Centre for Research and Innovation Management, National Defence University of Malaysia, Sg. Besi Camp, 57000 Kuala Lumpur, Malaysia
  • Khalid Isa Faculty of Electrical & Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia
  • Khaleel Ahmad Department of Computer Science & Information Technology, Maulana Azad National Urdu University, India

DOI:

https://doi.org/10.37934/araset.30.3.265275

Keywords:

HMLP, explosion, blast prediction, PE-4, emulex

Abstract

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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Author Biographies

Muhamad Hadzren Mat, WBE Technologies Sdn. Bhd., 218 Jalan Ampang, 54500, Kuala Lumpur, Malaysia

muhamad.hadzren@yahoo.com

Prakash Nagappan , Royal Service Corp Directorates, Army Head Quarters, Ministry of Defense, Jalan Padang Tembak, 50634 Kuala Lumpur, Malaysia

xzrann@gmail.com

Fakroul Ridzuan Hashim , Faculty of Engineering, National Defence University of Malaysia, Sg. Besi Camp, 57000 Kuala Lumpur, Malaysia

fakroul@upnm.edu.my

Khairol Amali Ahmad , Faculty of Engineering, National Defence University of Malaysia, Sg. Besi Camp, 57000 Kuala Lumpur, Malaysia

khairol@upnm.edu.my

Mohd Sharil Saleh , Centre for Research and Innovation Management, National Defence University of Malaysia, Sg. Besi Camp, 57000 Kuala Lumpur, Malaysia

sharil@upnm.edu.my

Khalid Isa, Faculty of Electrical & Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia

halid@uthm.edu.my

Khaleel Ahmad , Department of Computer Science & Information Technology, Maulana Azad National Urdu University, India

khaleelahmad@manuu.edu.in

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Published

2023-05-23

How to Cite

Muhamad Hadzren Mat, Prakash Nagappan, Fakroul Ridzuan Hashim, Khairol Amali Ahmad, Mohd Sharil Saleh, Khalid Isa, & Khaleel Ahmad. (2023). Hybrid Multilayer Perceptron Network for Explosion Blast Prediction . Journal of Advanced Research in Applied Sciences and Engineering Technology, 30(3), 265–275. https://doi.org/10.37934/araset.30.3.265275

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Articles