Department of Mining Engineering, Institute Technology of Bandung, Bandung, Indonesia.
School of Resources and Safety Engineering, Central South University, Changsha, China.
Front Public Health. 2022 Oct 20;10:1023890. doi: 10.3389/fpubh.2022.1023890. eCollection 2022.
The rockburst phenomenon is the major source of the high number of casualties and fatalities during the construction of deep underground projects. Rockburst poses a severe hazard to the safety of employees and equipment in subsurface mining operations. It is a hot topic in recent years to examine and overcome rockburst risks for the safe installation of deep urban engineering designs. Therefore, for a cost-effective and safe underground environment, it is crucial to determine and predict rockburst intensity prior to its occurrence. A novel model is presented in this study that combines unsupervised and supervised machine learning approaches in order to predict rockburst risk. The database for this study was built using authentic microseismic monitoring occurrences from the Jinping-II hydropower project in China, which consists of 93 short-term rockburst occurrences with six influential features. The prediction process was succeeded in three steps. Firstly, the original rockburst database's magnification was reduced using a state-of-the-art method called isometric mapping (ISOMAP) algorithm. Secondly, the dataset acquired from ISOMAP was categorized using the fuzzy c-means algorithm (FCM) to reduce the minor spectral heterogeneity impact in homogenous areas. Thirdly, K-Nearest neighbor (KNN) was employed to anticipate different levels of short-term rockburst datasets. The KNN's classification performance was examined using several performance metrics. The proposed model correctly classified about 96% of the rockbursts events in the testing datasets. Hence, the suggested model is a realistic and effective tool for evaluating rockburst intensity. Therefore, the proposed model can be employed to forecast the rockburst risk in the early stages of underground projects that will help to minimize casualties from rockburst.
岩爆现象是导致深部地下工程施工中高伤亡人数的主要原因。岩爆对地下采矿作业中员工和设备的安全构成严重威胁。近年来，研究和克服岩爆风险以安全安装深部城市工程设计已成为热点。因此，为了实现具有成本效益和安全的地下环境，在岩爆发生之前确定和预测岩爆强度至关重要。本研究提出了一种新模型，该模型结合了无监督和监督机器学习方法来预测岩爆风险。本研究的数据库是使用中国锦屏二级水电站的真实微震监测事件构建的，其中包含 93 起短期岩爆事件和 6 个有影响的特征。预测过程分三个步骤完成。首先，使用等距映射（ISOMAP）算法等先进方法降低原始岩爆数据库的放大倍数。其次，使用模糊 C 均值算法（FCM）对从 ISOMAP 获得的数据集进行分类，以减少同质性区域中小谱异质性的影响。然后，使用 K-最近邻（KNN）来预测短期岩爆数据集的不同级别。使用多个性能指标检查 KNN 的分类性能。该模型正确分类了测试数据集中约 96%的岩爆事件。因此，该模型是评估岩爆强度的现实而有效的工具。因此，该模型可以用于预测地下工程早期的岩爆风险，从而有助于减少岩爆造成的人员伤亡。
