Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81647
Title: A deep learning based method for the non-destructive measuring of rock strength through hammering sound
Authors: Han, S
Li, H 
Li, MC
Rose, T
Keywords: Transfer learning
Spectrogram analysis
Selecting samples
non-destructive testing
Hammering sound
Regression algorithm
Issue Date: 2019
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Applied sciences, 1 Sept. 2019, v. 9, no. 17, 3484, p. 1-14 How to cite?
Journal: Applied sciences 
Abstract: Hammering rocks of different strengths can make different sounds. Geological engineers often use this method to approximate the strengths of rocks in geology surveys. This method is quick and convenient but subjective. Inspired by this problem, we present a new, non-destructive method for measuring the surface strengths of rocks based on deep neural network (DNN) and spectrogram analysis. All the hammering sounds are transformed into spectrograms firstly, and a clustering algorithm is presented to filter out the outliers of the spectrograms automatically. One of the most advanced image classification DNN, the Inception-ResNet-v2, is then re-trained with the spectrograms. The results show that the training accurate is up to 94.5%. Following this, three regression algorithms, including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) are adopted to fit the relationship between the outputs of the DNN and the strength values. The tests show that KNN has the highest fitting accuracy, and SVM has the strongest generalization ability. The strengths (represented by rebound values) of almost all the samples can be predicted within an error of [ 5, 5]. Overall, the proposed method has great potential in supporting the implementation of efficient rock strength measurement methods in the field.
URI: http://hdl.handle.net/10397/81647
ISSN: 2076-3417
DOI: 10.3390/app9173484
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The following publication Han, S.; Li, H.; Li, M.; Rose, T. A Deep Learning Based Method for the Non-Destructive Measuring of Rock Strength through Hammering Sound. Appl. Sci. 2019, 9, 3484, 1-14 is available at https://dx.doi.org/10.3390/app9173484
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