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Neural Computing and Applications
Published in: Neural Computing and Applications 18/2020

09-03-2020 | Original Article

Predicting ground vibration induced by rock blasting using a novel hybrid of neural network and itemset mining

Authors: Maryam Amiri, Mahdi Hasanipanah, Hassan Bakhshandeh Amnieh

Published in: Neural Computing and Applications | Issue 18/2020

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Abstract

Blasting operation is considered as one of the cheapest methods to break the rock into small pieces in surface and underground mines. Ground vibration is a side effect of blasting and can result in damage to, or failure of, nearby structures. Therefore, it is imperative to predict ground vibration in the blasting sites. The primary objective of this paper is to propose a new model to predict ground vibration based on itemset mining (IM) and neural networks (NN), called IM–NN. It is worth mentioning that no research has tested the efficiency of IM–NN to predict ground vibration yet. IM–NN is composed of three steps; firstly, frequent and confident patterns (itemsets) were extracted by using IM. Secondly, for each test instance, the most appropriate instances were selected based on the extracted patterns. Thirdly, NN was only trained by the selected instances. To achieve the objective of this research, a dataset including 92 instances was collected from blasting events of two surface mines in Iran, Kerman province. To demonstrate the acceptability of IM–NN, the classical NN as well as several empirical equations were also developed in this study. The results indicated that IM–NN with the correlation squared (R2) of 0.944 has better performance than NN with R2 of 0.898 and may be a promising alternative to the NN for predicting ground vibration. Thus, the use of IM was a good idea to optimize and improve the NN performance.
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Metadata
Title
Predicting ground vibration induced by rock blasting using a novel hybrid of neural network and itemset mining
Authors
Maryam Amiri
Mahdi Hasanipanah
Hassan Bakhshandeh Amnieh
Publication date
09-03-2020
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 18/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-04822-w

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