Mao Yimin, Licai Zhu, Feng Li, Nanehkaran Yaser A, Zhang Maosheng
School of Information and Engineering, Shaoguan University, Shaoguan, 512005, Guangdong, China.
School of Information Engineering, Yancheng Teachers University, Yancheng, 224002, Jiangsu, People's Republic of China.
Sci Rep. 2023 Nov 27;13(1):20807. doi: 10.1038/s41598-023-46219-4.
Azarshahr County in the northwest of Iran is predominantly covered by Azarshahr travertine, a prevailing sedimentary rock. This geological composition has led to extensive open-pit mining activities, particularly in the western and southwestern parts of the county. The rock's drillability and resistance to excavation play a pivotal role in determining its overall durability and hardness, crucial factors that influence the mining process. These attributes are intimately tied to the compressive strength of the rock. Accurate assessment of rock strength is vital for devising reliable excavation methodologies at mining sites. However, conventional approaches for analyzing rock strength have limitations that undermine the precision of strength estimations. In response, this study endeavors to leverage artificial intelligence techniques, specifically the Multilayer Perceptron (MLP), to enhance the prediction of travertine's compressive strength. To formulate a robust model, a comprehensive database containing data from 150 point-load index (I) tests on Azarshahr travertine was compiled. This dataset serves as the foundation for the development of the MLP-based predictive model, which proves instrumental in projecting rock compressive strength. The model's accuracy and efficacy were rigorously assessed using the Receiver Operating Characteristic (ROC) curve, employing both training and testing datasets. The modeling outcomes reveal impressive results. The estimated R-squared coefficient attained an impressive value of 0.975 for axial strength and 0.975 for diametral strength. The overall accuracy, as indicated by the Area Under the Curve (AUC) metric, stands at an impressive 0.968. These exceptional performance metrics underscore the efficacy of the MLP model in accurately predicting compressive strength based on the point-load index of samples. The implications of this study are substantial. The predictive model, empowered by the MLP approach, has profound implications for excavation planning and drillability assessment within the studied region's travertine deposits. By facilitating accurate forecasts of rock strength, this model equips mining endeavors with valuable insights for effective planning and execution.
伊朗西北部的阿扎尔沙赫尔县主要被阿扎尔沙赫尔钙华覆盖，这是一种常见的沉积岩。这种地质构成导致了广泛的露天采矿活动，特别是在该县的西部和西南部地区。岩石的可钻性和抗挖掘性在决定其整体耐久性和硬度方面起着关键作用，这些因素对采矿过程至关重要。这些属性与岩石的抗压强度密切相关。准确评估岩石强度对于设计矿场可靠的挖掘方法至关重要。然而，传统的岩石强度分析方法存在局限性，会降低强度估计的精度。为此，本研究致力于利用人工智能技术，特别是多层感知器（MLP），来提高对钙华抗压强度的预测。为了构建一个强大的模型，编制了一个包含150次对阿扎尔沙赫尔钙华进行点荷载指数（I）测试数据的综合数据库。该数据集为基于MLP的预测模型的开发奠定了基础，该模型在预测岩石抗压强度方面发挥了重要作用。使用接收者操作特征（ROC）曲线，同时使用训练和测试数据集，对模型的准确性和有效性进行了严格评估。建模结果显示出令人印象深刻的结果。轴向强度的估计决定系数R²达到了令人印象深刻的0.975，径向强度的R²也为0.975。曲线下面积（AUC）指标显示的总体准确率高达0.968。这些优异的性能指标突出了MLP模型基于样本点荷载指数准确预测抗压强度的有效性。本研究的意义重大。由MLP方法支持的预测模型对研究区域钙华矿床的挖掘规划和可钻性评估具有深远影响。通过促进对岩石强度的准确预测，该模型为采矿工作提供了有价值的见解，有助于有效规划和执行。
