利用机器学习增强用于深部金属矿井斜坡设计的稳定性图

IF 7 1区 工程技术 Q1 ENGINEERING, GEOLOGICAL International Journal of Rock Mechanics and Mining Sciences Pub Date : 2024-07-31 DOI:10.1016/j.ijrmms.2024.105837
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引用次数: 0

摘要

人工可控的斜坡结构参数直接影响地下矿产开采的安全性和经济性。由于岩体的复杂性和不同的斜坡失效机制,目前的斜坡设计仍主要依赖于稳定性图等经验方法。本研究旨在利用机器学习技术增强稳定性图。首先,我们编制了一个包含 980 条无支撑斜坡记录的数据集,这可能是迄今为止文献中最大的同类数据集。这是通过广泛的文献综述和整理额外的 289 条以前未包括在内的中国矿井记录而实现的。数据分析显示,超过 90% 的记录属于 0-100 的稳定系数和 0-20 米的水力半径范围。采用了九种有监督的机器学习算法并对其进行了训练,以测试其在分区形式和预测准确性方面的性能。结果发现,神经网络算法的整体性能最佳。最后,使用 Keras 框架的神经网络建立了一个新的多层感知器模型,以生成安全系数概率曲线,然后用于构建稳定性图。为便于实际使用,还进一步制定了不稳定区域内安全系数曲线拟合数学函数。与其他经验稳定图相比,我们的新方法能让设计人员更有效、更可靠地根据现场具体条件和技术支持系统选择安全系数来确定稳定状态,从而为斜坡设计提供更好的指导。
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Enhancing stability graphs for stope design in deep metal mines using machine learning

Stope structural parameters, which are human-controllable, directly impact the safety and economics performance of underground mineral extraction. Current stope design still relies heavily on empirical methods such as stability graphs, due to the complex nature of rock masses and varied stope failure mechanisms. This study aims to enhance stability graphs with machine learning techniques. Firstly, a dataset of 980 records from unsupported stopes was compiled, representing perhaps the largest dataset of its kind so far in the literature. This was achieved through extensive literature review and the collation of an additional 289 records from Chinese mines which were previously not included. An analysis of data reveals that over 90 % of the records fall within a stability coefficient of 0–100 and a hydraulic radius of 0–20 m. Secondly, a stability graphs optimization process was established using Python, eliminating the subjectivity of partitioning. Nine supervised machine learning algorithms were employed and trained to test their performance in partitioning form and predicting accuracy. It was found that the neural network algorithm demonstrated the best overall performance. At last, a neural network with the Keras framework was used to establish a new multilayer perceptron model to generate safety factor probability curves, which were then used to construct the stability graph. To facilitate practical use, mathematical functions fitting safety factor curves within the unstable zone were further formulated. Compared with other empirical stability graphs, our new approach allows designers to more efficiently and reliably select safety factors to determine the stability state according to site specific conditions and technical support systems, thereby providing enhanced guidance for stope design.

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来源期刊
CiteScore
14.00
自引率
5.60%
发文量
196
审稿时长
18 weeks
期刊介绍: The International Journal of Rock Mechanics and Mining Sciences focuses on original research, new developments, site measurements, and case studies within the fields of rock mechanics and rock engineering. Serving as an international platform, it showcases high-quality papers addressing rock mechanics and the application of its principles and techniques in mining and civil engineering projects situated on or within rock masses. These projects encompass a wide range, including slopes, open-pit mines, quarries, shafts, tunnels, caverns, underground mines, metro systems, dams, hydro-electric stations, geothermal energy, petroleum engineering, and radioactive waste disposal. The journal welcomes submissions on various topics, with particular interest in theoretical advancements, analytical and numerical methods, rock testing, site investigation, and case studies.
期刊最新文献
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