چکیده مقاله
Underground mining and tunneling operations are inherently characterized by high levels of geological uncertainty and operational risk, ranging from catastrophic ground failures to hazardous atmospheric conditions Traditional risk management approaches, often reliant on static empirical methods and reactive protocols, frequently struggle to address the dynamic and non linear nature of subsurface hazards This paper presents a comprehensive review of the transformative role of Artificial Intelligence AI and Machine Learning ML in shifting the industry paradigm from reactive crisis management to proactive risk mitigation Specifically, the study explores the application of advanced deep learning architectures such as Long Short Term Memory LSTM networks and Convolutional Neural Networks CNNs in predicting geotechnical instabilities e g , rockbursts and convergence , optimizing ventilation on demand VOD systems, and enabling predictive maintenance of critical machinery Beyond successful applications, this review critically examines the significant barriers to widespread adoption, including data scarcity and class imbalance, the Black Box interpretability challenge, and hardware constraints in harsh environments Finally, the paper highlights the emergence of Physics Informed Machine Learning PIML as a vital frontier, advocating for hybrid models that integrate data driven insights with fundamental laws of rock mechanics to ensure physical consistency and generalizability The review concludes that the synergistic integration of AI with human engineering expertise is essential for developing the next generation of intelligent, safe, and resilient underground spaces
کلیدواژهها
نویسندگان
Abolfazl Faghihi Habibabadi
M Sc Student of Rock Mechanics, Faculty of Mining Engineering, Amirkabir University of Technology, Tehran, Iran
رزومه علمی کاربران
درباره سیویلیکاتبلیغات در سیویلیکاآمار علمی ایرانتقویم علمی ایرانداده کاوی مقالاتطرح های پژوهشی
شیوه ارجاع
در صورتی که می خواهید در اثر پژوهشی خود به این مقاله ارجاع دهید، به سادگی می توانید از عبارت زیر در بخش منابع و مراجع استفاده نمایید: Faghihi Habibabadi, Abolfazl,1404,Leverging artificial intelligence for enhanced risk management in underground mining and tunnelingi: A review,The First National Conference on Underground Management,Tehran,https://civilica.com/doc/2652172
ارائهشده در
مجموعه مقالات سومین کنفرانس بین المللی بوکسیت، آلومینا و آلومینیوم5 اسفند 1404 · تهران