• Geomechanics and Engineering
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• 제35권2호
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• pp.121-133
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• 2023

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.

• 문화기술의 융합
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• 제10권3호
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• pp.833-838
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• 2024

도시철도 침목플로팅궤도(STEDEF)는 침목상자와 침목방진패드를 이용하여 침목과 콘크리트 도상을 구조적으로 분리하여 콘크리트 도상에 전달되는 진동을 감소시키는 구조이다. 최근에는 20년 이상 공용중인 침목플로팅궤도의 침목방진패드 열화가 발생하고 있다. 이에 침목방진패드에 대한 성능을 평가하기 위해 침목방진패드 발췌 후 정적 스프링강성 시험을 수행하고 있다. 해당 평가기법은 사용 중인 침목방진패드를 반드시 교체한 후 평가를 수행한다. 그러나 궤도 고유진동수는 침목방진패드 스프링강성 및 콘크리트 도상의 융기와 침하에 따라 변화할 수 있다. 본 연구에서는 궤도 고유진동수를 평가하기 위해 모달시험기법을 사용하였다. 이를 위해 실험실 규모에서 침목상자 소재, 침목방진패드 스프링강성 및 콘크리트 도상 융기 및 침하에 따른 궤도 고유진동수를 모달시험을 이용하여 측정하였다. 침목플로팅궤도 고유진동수는 침목방진패드 스프링강성 변화에 따라 직접적인 영향을 받는 것으로 분석되었다. 또한 콘크리트 도상의 융기 및 침하에 따른 고유진동수 변화도 큰 것으로 나타났다. 따라서 본 연구에서 제시한 모달시험기법을 이용하여 침목방진패드의 열화 및 뜬 침목 평가 등에 활용할 수 있을 것으로 판단된다.