This special issue of Deep Underground Science and Engineering(DUSE)showcases pioneering research on the transformative role of machine learning(ML)and Big Data in deep underground engineering.Edited by guest editors Prof.Asoke Nandi(Brunel University of London,UK),Prof.Ru Zhang(Sichuan University,China),Prof.Tao Zhao(Chinese Academy of Sciences,China),and Prof.Tao Lei(Shaanxi University of Science and Technology,China),this issue highlights the innovative applications of ML technique in reshaping structural safety,tunneling operations,and geotechnical investigations.
This paper presents a comprehensive review on recent development and research conducted in domestic and international underground laboratories. We first introduce the differences in three environments—surface, mountain tunnel cavities and underground coal mine tunnels—by examining cosmic ray background, ambient noises related to gravity and seismic measurement, and electromagnetic noises in magnetic and magnetotelluric measurements. We highlight potential misuse of the term Underground Lab or Deep Underground Lab when describing observations in different physical fields. We introduce unique features of underground coal mine tunnels in China, such as large spaces, ultra-quiet conditions, and ultra-clean environments. When comparing with mountain tunnel cavities and borehole observations, coal mine tunnel observations have superior long-term stability and high precision. Through observations and comparisons of multi-physic fields at surface and the deep underground, we find that the higher SNR seismic observations conducted in deep underground tunnels in coal mines are beneficial to improve velocity tomography of the solid earth. The gravity observation with a Superconducting Quantum Interference Device(SQUID) makes it possibly to capture slow earthquake, which has not been observed previously in the Chinese mainland. SQUID magnetic observations can detect fluctuations as weak as femto-Tesla(fT), enabling us to explore the attenuation of Schumann Resonance down to the solid Earth. This opens opportunities to investigate the connections between the Earth's magnetic field and the interactions within the human brain and heart. To improve the precision of quantum measurement,we should consider the possible effects of weak magnetic disturbances in deep underground environments. Finally, we discuss the importance of deep underground laboratories, observing facilities and techniques deployed in these laboratories, and their possible connection with respect to “deep space” and “deep ocean” exploration, emphasiz
Assessing the stability of pillars in underground mines(especially in deep underground mines)is a critical concern during both the design and the operational phases of a project.This study mainly focuses on developing two practical models to predict pillar stability status.For this purpose,two robust models were developed using a database including 236 case histories from seven underground hard rock mines,based on gene expression programming(GEP)and decision tree-support vector machine(DT-SVM)hybrid algorithms.The performance of the developed models was evaluated based on four common statistical criteria(sensitivity,specificity,Matthews correlation coefficient,and accuracy),receiver operating characteristic(ROC)curve,and testing data sets.The results showed that the GEP and DT-SVM models performed exceptionally well in assessing pillar stability,showing a high level of accuracy.The DT-SVM model,in particular,outperformed the GEP model(accuracy of 0.914,sensitivity of 0.842,specificity of 0.929,Matthews correlation coefficient of 0.767,and area under the ROC of 0.897 for the test data set).Furthermore,upon comparing the developed models with the previous ones,it was revealed that both models can effectively determine the condition of pillar stability with low uncertainty and acceptable accuracy.This suggests that these models could serve as dependable tools for project managers,aiding in the evaluation of pillar stability during the design and operational phases of mining projects,despite the inherent challenges in this domain.
Mohammad H.KadkhodaeiEbrahim GhasemiJian ZhouMelika Zahraei
目前,用于地下电缆射频识别(radio frequency identification,RFID)探测定位的传统紧密型线圈天线的性能不足严重制约了其探测定位距离的提高。提出了一种新型高场强分散型RFID线圈天线结构,在推导天线相关电气参数的基础上,以天线的磁场强度为目标函数,以其品质因数固定为约束条件,采用粒子群算法对天线的匝数和相邻两匝之间的匝间距进行了优化。最后,搭建了实验测试平台,测试结果表明,与传统紧密型RFID线圈天线相比,所设计的分散型RFID线圈天线将读取距离提高了33.3%,同时明显增强了相同距离下的标签返回信号强度(received signal strength indication,RSSI),有助于提高基于RSSI的地下RFID定位方法的精度,这为地下电缆RFID探测定位的应用提供重要参考。
