Geomechanics and Engineering

  • 제35권2호
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  • Pages.121-133
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  • 2023
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  • 2005-307X(pISSN)
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  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Predicting blast-induced ground vibrations at limestone quarry from artificial neural network optimized by randomized and grid search cross-validation, and comparative analyses with blast vibration predictor models

  • Salman Ihsan (Department of Mining Engineering, University of Engineering and Technology) ;
  • Shahab Saqib (Department of Mining Engineering, University of Engineering and Technology) ;
  • Hafiz Muhammad Awais Rashid (Department of Geological Engineering, University of Engineering and Technology) ;
  • Fawad S. Niazi (Department of Civil and Mechanical Engineering, Purdue University) ;
  • Mohsin Usman Qureshi (Faculty of Engineering, Sohar University)
  • 투고 : 2023.02.14
  • 심사 : 2023.08.12
  • 발행 : 2023.10.25
⟨ 이전 논문 다음 논문 ⟩

초록

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.

키워드

과제정보

This study was supported by Higher Education Commission (HEC) Pakistan vide National Research Program for Universities (NRPU) Project No.9494. The authors would also like to acknowledge the quarry managers of different cement quarries at Chakwal (DG cement limited, Bestway cement limited, Chakwal & Bestway cement, Kallarkahar), who have facilitated this research work by providing access to blast sites for monitoring of blast-induced ground vibrations.

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