Rock abrasivity is an important factor that affects tool wear and tool parameter design, construction efficiency, and cost budgeting during rock excavation. The Cerchar abrasivity index (CAI) is used as a standard parameter to characterize the abrasiveness of rocks. It can be obtained through the Cerchar abrasivity test in a laboratory. However, conventional laboratory Cerchar abrasivity tests and petrophysical and mechanical tests are associated with certain problems, such as long cycles, high costs, and measurement delays, which cannot serve the determination of rock abrasiveness in field. Digital drilling technology is an efficient in situ test method that can effectively link drilling parameters with rock properties. Based on the test results for the CAI and physical and mechanical properties of 13 groups of rock samples collected from southwestern China, this paper focuses on the correlations of the CAI value with rock strength, petrographic characteristics and drilling parameters. By adopting a flat bottom diamond bit that mainly relies on grinding, according to the principle of mechanical balance and energy conservation during rock drilling, the grinding energy per unit volume of rock (ηe) is defined and derived. The univariate regression analysis results show that the uniaxial compressive strength (UCS) and equivalent quartz content (EQC) are the most important factors for explaining CAI and that the CAI can be well estimated using the ηe. Additionally, two optimal regression models for predicting CAI were established using stepwise multiple regression analysis. UCS and ηe were introduced into the model, and the use of both can predict approximately 96% of the variance in the CAI. In addition, a performance evaluation of the models proposed in this paper and previously published CAI prediction models that the prediction models established in this paper are verify statistically more reasonable and reliable than the previously proposed models. The research methods and results provide a new method for the rapid and accurate determination of rock abrasiveness in engineering field.

岩石的磨蚀性是影响岩石开挖过程中工具磨损和工具参数设计，施工效率以及成本预算的重要因素。Cerchar磨耗指数（CAI）用作表征岩石磨蚀性的标准参数。可以通过实验室的Cerchar耐磨性测试获得。但是，常规的实验室Cerchar磨蚀性测试以及岩石物理和机械测试会带来某些问题，例如长周期，高成本和测量延迟，这些问题无法用于确定岩石的磨蚀性。数字钻探技术是一种有效的原位测试方法，可以有效地将钻探参数与岩石特性联系起来。基于对西南地区采集的13组岩石样品的CAI和物理力学性能的测试结果，重点研究了CAI值与岩石强度，岩石学特征和钻井参数的相关性。通过采用主要依靠磨削的平底金刚石钻头，根据凿岩时的机械平衡和节能原理，确定并推导出单位体积岩石的磨削能量（ηe）。单变量回归分析结果表明，单轴抗压强度（UCS）和等效石英含量（EQC）是解释CAI的最重要因素，并且可以使用ηe很好地估计CAI。此外，使用逐步多元回归分析建立了两个用于预测CAI的最佳回归模型。将UCS和ηe引入模型，并且两者的使用都可以预测CAI中约96％的方差。另外，对本文提出的模型和先前发布的CAI预测模型进行性能评估，认为本文中建立的预测模型比先前提出的模型在统计上更合理，更可靠。研究方法和结果为在工程领域快速，准确地确定岩石的磨蚀性提供了一种新方法。对本文提出的模型和先前发布的CAI预测模型的性能评估表明，本文建立的预测模型在统计上比先前提出的模型更合理，更可靠。研究方法和结果为在工程领域快速，准确地确定岩石的磨蚀性提供了一种新方法。对本文提出的模型和先前发布的CAI预测模型的性能评估表明，本文建立的预测模型在统计上比先前提出的模型更合理，更可靠。研究方法和结果为在工程领域快速，准确地确定岩石的磨蚀性提供了一种新方法。