Enhancing Blast Design Efficiency for Rock Fragmentation with Gradient Descent and Artificial Neural Networks: An Optimization Study-Reference-Cited by-同舟云学术

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Enhancing Blast Design Efficiency for Rock Fragmentation with Gradient Descent and Artificial Neural Networks: An Optimization Study

Published:2023-12-13 Issue: Volume: Page:1-5
ISSN:
Container-title:2023 4th International Conference on Computers and Artificial Intelligence Technology (CAIT)
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Author:

Saubi Onalethata¹,Gaopale Kesalopa¹,Jamisola Rodrigo S.²,Suglo Raymond S.¹,Matsebe Oduetse²

Affiliation:

1. Botswana International University of Science and Technology,Dept. of Mining and Geological Engineering,Palapye,Botswana

2. Botswana International University of Science and Technology,Dept. of Mechanical, Energy and Industrial Engineering,Palapye,Botswana

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10468569/10468677/10469523.pdf?arnumber=10469523

Reference19 articles.

1. On certain integrals of Lipschitz-Hankel type involving products of bessel functions

2. A Treatise on Electricity and Magnetism

3. Using dimensional-regression analysis to predict the mean particle size of fragmentation by blasting at the Sungun copper mine

4. The Kuz-Ram model for prediction of fragmentation from blasting;Cunningham

5. The mean diameter of the fragments formed by blasting rock

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