Universiti Teknologi Malaysia Institutional Repository

Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm

Ebrahimi, Ebrahim and Monjezi, Masoud and Khalesi, Mohammad Reza and Armaghani, Danial Jahed (2016) Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm. Bulletin of Engineering Geology and the Environment, 75 (1). pp. 27-36. ISSN 1435-9529

Full text not available from this repository.

Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

In blasting works, the aim is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as back-break. Therefore, predicting fragmentation and back-break is a significant step in achieving a technically and economically successful outcome. In this paper, considering the robustness of artificial intelligence methods utilized in engineering problems, an artificial neural network (ANN) was applied to predict rock fragmentation and back-break; an artificial bee colony (ABC) algorithm was also utilized to optimize the blasting pattern parameters. In this regard, blasting parameters, including burden, spacing, stemming length, hole length and powder factor, as well as back-break and fragmentation were collected at the Anguran mine in Iran. Root mean square error (RMSE) values equal to 2.76 and 0.53 for rock fragmentation and back-break, respectively, reveal the high reliability of the ANN model. In addition, ABC algorithm results suggest values of 29 cm and 3.25 m for fragmentation and back-break, respectively. For comparison purposes, an empirical model (Kuz-Ram) was performed to predict the mean fragment size in the Anguran mine. A mean fragment size of 33.5 cm shows the ABC algorithm can optimize rock fragmentation with a high degree of accuracy.

Item Type:Article
Uncontrolled Keywords:Artificial intelligence, Blasting, Environmental impact, Forecasting, Mean square error, Neural networks, Optimization, Rock bursts, Rocks, Artificial bee colonies, Artificial bee colony algorithms (ABC), Artificial intelligence methods, Back-break, Bee colony algorithms, High degree of accuracy, Rock fragmentation, Root mean square errors, Algorithms, algorithm, artificial neural network, blasting, fragmentation, mine, optimization, prediction, rock mechanics, Angouran, Iran, Zanjan, Apoidea
Subjects:T Technology > TA Engineering (General). Civil engineering (General)
Divisions:Civil Engineering
ID Code:73901
Deposited By: Fahmi Moksen
Deposited On:21 Nov 2017 08:17
Last Modified:21 Nov 2017 08:17

Repository Staff Only: item control page