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Article citations


Saadat, M., Khandelwal, M. and Monjezi, M. (2014) An ANN-Based Approach to Predict Blast-Induced Ground Vibration of Gol-E-Gohar Iron Ore Mine, Iran. Journal of Rock Mechanics and Geotechnical Engineering, 6, 67-76.

has been cited by the following article:

  • TITLE: Web-GIS Based Visualization System of Predicted Ground Vibration Induced by Blasting in Urban Quarry Sites

    AUTHORS: Youhei Kawamura, Yoshio Moriyama, Hyongdoo Jang

    KEYWORDS: ANN, Prediction Model, PPV, Web-GIS, Ground Vibration Visualization

    JOURNAL NAME: Journal of Geographic Information System, Vol.11 No.1, February 1, 2019

    ABSTRACT: Blasting is routinely carried out at various resource extraction sites, even in urban areas. As a consequence of this, residents around urban quarry sites are affected by ground vibration induced by blasting on a regular basis. In this study, a prediction and visualization system for ground vibrations is developed for the purpose of reducing the adverse psychological effects of blasting. The system consists of predicting ground vibration using an Artificial Neural Network (ANN) and visualizing it on an online map using Web-GIS. A prediction model using ANN that learned the optimum weight by taking 50 sets of data indicated a regression value of 0.859 and a Mean Square Error (MSE) of 0.0228. Compared with previous researches, these values are not bad results. Peak Particle Velocity (PPV) was used as a metric to measure ground vibration intensity. A color contour is generated using GIS tools based on the PPV value of each prediction point. The system is completed by overlaying the contour onto a basic map in a website. The basic map shows the surrounding area through the use of Google Maps data. This system can be used by anyone with access to the internet and a browser, requiring no special software or hardware. In addition, mining operations can utilize the data to modify blasting design and planning to minimize ground vibration. In conclusion, this system has the potential to alleviate the worries of surrounding residents caused by ground vibrations from blasting due to the fact that they can personally check the predicted vibration around their locale. Furthermore, since this data will be publicly available on the internet, it is also possible that this system can contribute to research in other fields.