An Optimized Deep Neural Network for Rockburst Damage Potential Modelling

Abstract

ABSTRACT Managing ground prone to rockburst is challenging, especially in seismically active underground mines. Over the past few decades, numerous studies have been conducted on predicting rockburst damage potential. However, in most cases, only fair model performance was achieved due to the complex nature of rockburst as a seismic event and the non-linearity of data. To overcome these limitations, this paper presents a more reliable model for predicting rockburst damage potential (RDP). An Artificial Neural Network was established, and its parameters were optimized using the Adam optimizer. Rockburst data consisting of 254 case histories were compiled and used to model the RDP scale. The dataset was divided into two parts: a training set, which accounted for 80% of the dataset, and a separate test set, which accounted for the remaining 20%. Cross-validation technique was applied to the training set to avoid overfitting. The input parameters for the model included the capacity of the ground support system, stress conditions, presence of geological structure, excavation span, and peak particle velocity. Several performance indices were used to evaluate the model, and the overall results indicate good performance. In conclusion, this study could help engineers adequately assess rockburst damage in seismically active mines. INTRODUCTION Rockburst refers to the damage that occurs on rock excavation surfaces as a result of a seismic event (Ortlepp and Stacey, 1994). It is characterized by a sudden release of strain energy stored within a specific volume of rock (Kaiser and Cai, 2012). Rockbursts are known for their unpredictable and violent nature, representing a significant threat to workers’ safety, mining productivity, and operational costs. Rockbursts and mine seismicity are responsible for a large number of accidents and casualties in many parts of the world every year, as reported in Europe (Ptáček, 2017; Heib, 2018), China (Cai, 2016; Jian-Po Liu, 2018; Linming Dou, 2018; Xia-Ting Feng, 2018), India (Panthi, 2018), South Africa (Aswegen, 2018), and Canada (Leveille et al., 2017; Simser, 2019). Furthermore, with the depletion of near-surface mineral resources caused by the rising demand for minerals, there is a shift towards exploiting deeper deposits, often in high-stress and unfavorable geo-engineering conditions. This results in an increased likelihood of rockburst incidents and makes mining-induced seismic activity a significant concern for the mining sector. It is, thus, evident that the ability to forecast and manage rockburst occurrences is becoming increasingly important.