为解决隧道洞渣作为骨料母岩时的适用性判别难题，运用梯度提升（GB）、极端梯度提升（XGB）、支持向量回归（SVR）和随机森林（RF）4种机器学习模型提出以洞渣母岩性能为输入端的洞渣粗骨料压碎指标预测方法，并开展洞渣母岩特征重要性分析及其对压碎指标的影响规律分析。研究结果表明： 1）XGB模型的预测效果最佳，在测试集上决定系数（R²）和均方根误差（E_RMS）的平均值分别为0.90和3.51%。2）洞渣母岩各性能对压碎指标的影响程度排序为饱和抗压强度（重要度量值0.33）＞块体干密度（0.28）＞吸水率（0.05）＞软化系数（0.04）＞云母质量分数（0.01）＞硫化物及硫酸盐质量分数（0）。3）洞渣母岩强度的提升可增强洞渣粗骨料的抗压碎能力；高块体干密度反映洞渣母岩高硬度、低孔隙率及致密结构，可提升洞渣粗骨料抗压碎能力；高吸水率洞渣母岩因内部孔隙多，易降低洞渣粗骨料抗压碎能力；高软化系数的洞渣母岩制备的粗骨料在水作用下结构和强度下降幅度较小，具有更优的抗压碎性能。4）洞渣母岩中的云母和硫化物及硫酸盐虽对压碎指标影响程度较低，但会削弱混凝土的整体性能。

To determine the feasibility of parent rock for aggregates using tunnel slag, a method is proposed for predicting the crushing index of coarse aggregate from tunnel slag using four machine learning models: gradient boosting (GB), extreme GB (XGB), support vector regression, and random forest, with the parent rock properties of the slag as inputs. Additionally, an analysis of the importance of the parent rock characteristics of the slag and their influence patterns on the crushing index is conducted. The results show that: (1) The XGB model exhibits the best prediction effect, with an average R²  of 0.90 and root mean square error of 3.51% on the test set. (2) The impact of parent-rock properties of tunnel slag on the crushing index ranges from highest to lowest as follows: saturated uniaxial compressive strength (importance value 0.33), bulk dry density (0.28), water absorption (0.05), softening coefficient (0.04), mica content (0.01), and sulfide/sulfate content (0). (3) Increasing the strength of parent rock boosts the anticrushing ability of coarse aggregate from tunnel slag. High bulk density signifies hard, low-porosity rock with a dense structure, enhancing this ability. High water absorption, due to more pores in the rock, reduces the anti-crushing property. A high softening coefficient means less strength loss in water, indicating better anti-crushing performance. (4) Although mica and sulfides/sulfates in parent rock have minor effects on the crushing index, they can weaken the overall performance of concrete.