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Optimizing blasting’s air overpressure prediction model using swarm intelligence

Alel, Mohd Nur Asmawisham and Upom, Mark Ruben and Abdullah, Rini Asnida and Zainal Abidin, Mohd Hazreek (2017) Optimizing blasting’s air overpressure prediction model using swarm intelligence. In: International Seminar on Mathematics and Physics in Sciences and Technology 2017 (ISMAP 2017), 28–29 October 2017, Hotel Katerina, Malaysia.

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Air overpressure (AOp) resulting from blasting can cause damage and nuisance to nearby civilians. Thus, it is important to be able to predict AOp accurately. In this study, 8 different Artificial Neural Network (ANN) were developed for the purpose of prediction of AOp. The ANN models were trained using different variants of Particle Swarm Optimization (PSO) algorithm. AOp predictions were also made using an empirical equation, as suggested by United States Bureau of Mines (USBM), to serve as a benchmark. In order to develop the models, 76 blasting operations in Hulu Langat were investigated. All the ANN models were found to outperform the USBM equation in three performance metrics; root mean square error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination (R2). Using a performance ranking method, MSO-Rand-Mut was determined to be the best prediction model for AOp with a performance metric of RMSE=2.18, MAPE=1.73% and R2=0.97. The result shows that ANN models trained using PSO are capable of predicting AOp with great accuracy.

Item Type: Conference or Workshop Item (Paper)
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA703-711 Engineering geology. Rock mechanics. Soil mechanics.
Divisions: Faculty of Civil and Environmental Engineering > Department of Infrastructure and Geomatic Engineering
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 31 Jul 2019 01:01
Last Modified: 31 Jul 2019 01:01
URI: http://eprints.uthm.edu.my/id/eprint/11441
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