A groundbreaking application of deep learning in the mining industry is revolutionizing the prediction of rock bursts, a significant safety hazard in underground mines. Rock bursts, characterized by violent ejections of rock caused by high-stress levels, pose a great threat to personnel and property in the coal mining sector. Although forecasting these events has been challenging due to their complexity and unpredictability, a novel approach called BiLSTM with Differential Evolution Algorithm is overcoming these obstacles.
The BiLSTM, a type of recurrent neural network, has proven to be adept at capturing temporal patterns, making it an ideal tool for predicting events like rock bursts. By incorporating a differential evolution algorithm and an attention mechanism in its training, this approach maximizes the accuracy in determining dangerous periods leading up to a rock burst. The differential evolution algorithm optimizes the network’s performance, while the attention mechanism allows the model to focus on critical information, further enhancing the prediction accuracy.
In a comparative analysis, deep learning techniques outperformed existing prediction models, solidifying its supremacy in the domain. For instance, a model based on machine learning was used to predict the peak shear strength of rock joints and was found to be superior to other methods. This highlights the potential of deep learning in reliably predicting rock-related events.
While deep learning is transforming rock burst prediction, the issue of land subsidence resulting from mining activity remains a concern. Accurate estimation of subsidence rates is vital for land protection and reclamation efforts. A study proposes the use of the overburden’s average Generalized Strength Index (GSI) value as an indicator for estimating subsidence rates. By establishing a relationship between GSI values and subsidence rates through a calibrated numerical model, accurate estimations can be made.
As the mining industry continues to venture deeper into the Earth’s crust, the risk of rock bursts intensifies. Therefore, advanced prediction techniques are essential for maintaining safety. Deep learning, with its ability to analyze vast amounts of data and identify patterns, presents a promising solution. By harnessing the power of deep learning, the mining industry can proactively anticipate and prevent rock bursts, ensuring the well-being of underground personnel and the continuity of mining operations.
The source of the article is from the blog portaldoriograndense.com