Rock Load Height Prediction for Large-Scale Caverns Using Numerical Analysis
سال انتشار: 1404
نوع سند: مقاله ژورنالی
زبان: فارسی
مشاهده: 2
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شناسه ملی سند علمی:
JR_ANM-15-45_002
تاریخ نمایه سازی: 29 دی 1404
چکیده مقاله:
Determination of rock load values plays a crucial role in the stability analysis and design of underground structures, particularly in ensuring the safety and cost-effectiveness of support systems. Rock load height serves as a vital parameter for determining the required support in underground openings. Over the years, numerous researchers have developed various methods to estimate rock load height, often based on parameters such as rock quality, opening width, and uniaxial compressive strength. However, the combined effects of additional key parameters, including the ratio of horizontal to vertical stress (K ratio) and overburden height, have not been thoroughly investigated in a unified framework. This study addresses this gap by incorporating these parameters to propose a new empirical relationship for estimating rock load height. Numerical analyses were performed using a safety factor contour of ۲.۰ to evaluate the rock load heights in cavern roofs under diverse conditions. The results of this comprehensive analysis were compared with existing methods, demonstrating good agreement and validating the reliability of the proposed approach. The new relationship offers a significant advantage by accounting for the influence of varying overburden heights and horizontal-to-vertical stress ratios, thus providing more precise estimations tailored to site-specific conditions. Furthermore, the study introduces a novel equation that links vertical displacement in the cavern roof to rock load height. This innovative approach provides a practical tool for integrating monitoring data into stability assessments. By bridging theoretical insights with real-world applications, the proposed methodology advances the understanding and prediction of rock load behavior, ensuring safer and more effective underground design practices.
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نویسندگان
Abolfazl Abdollahipour
School Of Mining Engineering, College Of Engineering, University Of Tehran, Tehran, Iran
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