A rapid identification model of mine water inrush based on PSO-XGBoost

Mine water inrush is one of the main threats to mine safety production. Rapid analysis of the cause of water inrush and accurate identification of water inrush source are the key steps of mine water inrush disaster control. In order to effectively prevent and control mine water inrush disaster and identify mine water inrush source accurately and quickly, a mine water inrush source identification model (PSO-XGBoost) based on particle swarm optimization algorithm (PSO) and limit gradient lifting regression tree (XGBoost) was proposed. The efficiency and accuracy of water inrush source identification were further improved by the efficient parameter global search model, and the model was successfully applied to the Laohutai mine in Fushun coal field, Liaoning Province to verify the practicability of the model. Based on the spectral data of 40 groups of water samples from Laohutai mine, the original spectral data were preprocessed by multiple scattering correction, smoothing denoising, standardization and principal component analysis, and the training set and test set were divided according to the ratio of 7∶3 according to stratified random sampling. Secondly, the individual optimal value and the global optimal value of particles are initialized, and PSO is used to iteratively optimize seven parameters of XGBoost algorithm, such as learning_rate, n_estimatiors, max_depth, etc., to construct the classification and recognition model under the optimal parameter combination. To further investigate the superiority of the model, the average discrimination accuracy and log loss value were selected as evaluation indexes to compare the classification recognition results of PSO-XGBoost model with PSO-SVM and PSO-RF models, while the generalization ability of each model was evaluated by 100 repetitions of cross-validation. The comparison results showed that the average discrimination accuracies of XGBoost, PSO-SVM, PSO-RF and PSO-XGBoost models for the test set data were 87.76%, 87.56%, 91.67% and 91.67%, respectively. For repeated cross-validation, the average accuracy of XGBoost, PSO-SVM, PSO-RF, and PSO-XGBoost models were 87.76%, 87.56%, 90.63%, and 93.18%, respectively, with corresponding log-loss averages of 0.5453, 0.5460, 0.5623, and 0.4534, respectively. Comprehensive analysis of evaluation indexes shows that PSO-XGBoost model has higher discrimination accuracy and better generalization ability in mine water inrush source identification

Towards Long-Term Monitoring of the Structural Health of Deep Rock Tunnels with Remote Sensing Techniques

Due to the substantial need to continuously ensure safe excavations and sustainable operation of deep engineering structures, structural health monitoring based on remote sensing techniques has become a prominent research topic in this field. Indeed, throughout their lifetime, deep tunnels are usually exposed to many complex situations which inevitably affect their structural health. Therefore, appropriate and effective monitoring systems are required to provide real-time information that can be used as a true basis for efficient and timely decision-making. Since sensors are at the heart of any monitoring system, their selection and conception for deep rock tunnels necessitates special attention. This work identifies and describes relevant structural health problems of deep rock tunnels and the applicability of sensors employed in monitoring systems, based on in-depth searches performed on pertinent research. The outcomes and challenges of monitoring are discussed as well. Results show that over time, deep rock tunnels suffer several typical structural diseases namely degradation of the excavation damaged areas, corrosion of rock bolts and cable bolts, cracks, fractures and strains in secondary lining, groundwater leaks in secondary lining, convergence deformation and damage provoked by the triggering of fires. Various types of remote sensors are deployed to monitor such diseases. For deep rock tunnels, it is suggested to adopt comprehensive monitoring systems with adaptive and robust sensors for their reliable and long-lasting performance

Volume II: Mining Innovation

Contemporary exploitation of natural raw materials by borehole, opencast, underground, seabed, and anthropogenic deposits is closely related to, among others, geomechanics, automation, computer science, and numerical methods. More and more often, individual fields of science coexist and complement each other, contributing to lowering exploitation costs, increasing production, and reduction of the time needed to prepare and exploit the deposit. The continuous development of national economies is related to the increasing demand for energy, metal, rock, and chemical resources. Very often, exploitation is carried out in complex geological and mining conditions, which are accompanied by natural hazards such as rock bursts, methane, coal dust explosion, spontaneous combustion, water, gas, and temperature. In order to conduct a safe and economically justified operation, modern construction materials are being used more and more often in mining to support excavations, both under static and dynamic loads. The individual production stages are supported by specialized computer programs for cutting the deposit as well as for modeling the behavior of the rock mass after excavation in it. Currently, the automation and monitoring of the mining works play a very important role, which will significantly contribute to the improvement of safety conditions. In this Special Issue of Energies, we focus on innovative laboratory, numerical, and industrial research that has a positive impact on the development of safety and exploitation in mining

Numerical Modeling in Civil and Mining Geotechnical Engineering

This Special Issue (SI) collects fourteen articles published by leading scholars of numerical modeling in civil and mining geotechnical engineering. There is a good balance in the number of published articles, with seven in civil engineering and seven in mining engineering. The software used in the numerical modeling of these article varies from numerical codes based on continuum mechanics to those based on distinct element methods or mesh-free methods. The studied materials vary from rock, soil, and backfill to tailings. The investigations vary from mechanical behavior to hydraulic and thermal responses of infrastructures varying from pile foundations to tailings dams and underground openings. The SI thus collected a diversity of articles, reflecting the state-of-the-art of numerical modeling applied in civil and mining geotechnical engineering

English for Study and Work: Coursebook in 4 books. Book 2 Obtaining and Processing Information for Specific Purposes

Подано всі види діяльності студентів з вивчення англійської мови, спрямовані на розвиток мовної поведінки, необхідної для ефективного спілкування в академічному та професійному середовищах. Містить завдання і вправи, типові для різноманітних академічних та професійних сфер і ситуацій. Структура організації змісту – модульна, охоплює мовні знання і мовленнєві вміння залежно від мовної поведінки. Даний модуль має на меті розвиток у студентів стратегій, умінь, навичок читання, пошуку та вилучення професійно-орієнтованої інформації, необхідної для ефективної професійної діяльності і навчання. Містить завдання і вправи, типові для академічних та професійних сфер, пов’язаних з гірництвом і розробкою родовищ корисних копалин. Зразки текстів – автентичні, різножанрові, взяті з реального життя, містять цікаву й актуальну інформацію про особливості видобутку мінеральних ресурсів в провідних країнах світу, сучасний підхід до розробки родовищ тощо. Ресурси для самостійної роботи (Частина ІІ) містять завдання та вправи для розширення словникового запасу та розвитку знань найуживанішої термінології з гірництва, що спрямовано на організацію самостійної роботи з розвитку мовленнєвих умінь, знань про корисні копалини, методи їх видобутку. За допомогою засобів діагностики студенти можуть самостійно перевірити засвоєння навчального матеріалу й оцінити свої досягнення. Призначений для студентів вищих навчальних закладів, зокрема технічних університетів. Може використовуватися для самостійного вивчення англійської мови викладачами, фахівцями і науковцями різних галузей

Proceedings of the 2004 Coal Operators\u27 Conference

Proceedings of the 2004 Coal Operators\u27 Conference. All papers in these proceedings are peer reviewed in accordance with The AUSIMM publication standard

Mining Safety and Sustainability I

Safety and sustainability are becoming ever bigger challenges for the mining industry with the increasing depth of mining. It is of great significance to reduce the disaster risk of mining accidents, enhance the safety of mining operations, and improve the efficiency and sustainability of development of mineral resource. This book provides a platform to present new research and recent advances in the safety and sustainability of mining. More specifically, Mining Safety and Sustainability presents recent theoretical and experimental studies with a focus on safety mining, green mining, intelligent mining and mines, sustainable development, risk management of mines, ecological restoration of mines, mining methods and technologies, and damage monitoring and prediction. It will be further helpful to provide theoretical support and technical support for guiding the normative, green, safe, and sustainable development of the mining industry

Data Mining

The availability of big data due to computerization and automation has generated an urgent need for new techniques to analyze and convert big data into useful information and knowledge. Data mining is a promising and leading-edge technology for mining large volumes of data, looking for hidden information, and aiding knowledge discovery. It can be used for characterization, classification, discrimination, anomaly detection, association, clustering, trend or evolution prediction, and much more in fields such as science, medicine, economics, engineering, computers, and even business analytics. This book presents basic concepts, ideas, and research in data mining

Towards Long-Term Monitoring of the Structural Health of Deep Rock Tunnels with Remote Sensing Techniques

Due to the substantial need to continuously ensure safe excavations and sustainable operation of deep engineering structures, structural health monitoring based on remote sensing techniques has become a prominent research topic in this field. Indeed, throughout their lifetime, deep tunnels are usually exposed to many complex situations which inevitably affect their structural health. Therefore, appropriate and effective monitoring systems are required to provide real-time information that can be used as a true basis for efficient and timely decision-making. Since sensors are at the heart of any monitoring system, their selection and conception for deep rock tunnels necessitates special attention. This work identifies and describes relevant structural health problems of deep rock tunnels and the applicability of sensors employed in monitoring systems, based on in-depth searches performed on pertinent research. The outcomes and challenges of monitoring are discussed as well. Results show that over time, deep rock tunnels suffer several typical structural diseases namely degradation of the excavation damaged areas, corrosion of rock bolts and cable bolts, cracks, fractures and strains in secondary lining, groundwater leaks in secondary lining, convergence deformation and damage provoked by the triggering of fires. Various types of remote sensors are deployed to monitor such diseases. For deep rock tunnels, it is suggested to adopt comprehensive monitoring systems with adaptive and robust sensors for their reliable and long-lasting performance