为解决目前缺少明确的渣土改良效果评价指标及评价方法，无法快速判定渣土改良效果以及缺陷的问题，首先，采用自主研制的室内试验装置，得到不同黏粒体积分数砂土地层改良渣土的性质（压缩率、坍落度、屈服剪切强度）；其次，基于室内试验结果，应用聚类分析结合机器学习的方法，提出将屈服剪切强度、坍落度及竖向荷载为200 kPa时的压缩率作为渣土改良效果的评价指标；再次，应用评价指标将改良渣土分为3类，确定不同种类改良渣土的特性分布区间；最后，采用XGBoost算法，建立渣土改良效果预测模型，选择膨润土泥浆注入比、膨润土泥浆质量分数、未改良砂土颗粒粒径d₁₀、黏粒体积分数、泡沫注入比作为预测模型输入值，模型预测准确率达到80%。

In current earth pressure balance shield tunneling projects, the effectiveness of soil conditioning is commonly evaluated using slump tests or empirical judgment. A uniform standard is applied to assess the conditioning effectiveness for different soils, resulting in evaluation criteria that lack specificity and adaptability. Due to the lack of clear evaluation metrics and assessment method for soil conditioning effectiveness, it is difficult to quickly assess the conditioning results and identify deficiencies. This often leads to reduced construction efficiency and increasing engineering issues such as cutterhead clogging, surface settlement, and screw conveyor spraying. To address the aforementioned technical challenges, a self-developed indoor experimental apparatus is employed to investigate the properties of conditioned soil in sandy soil layers with varying clay content, including compressibility ratio, consistency coefficient, and yield shear strength. Based on indoor experimental results, a method combining cluster analysis with machine learning is proposed to evaluate the soil conditioning effectiveness by applying yield shear strength, slump, and compression ratio (200 kPa) as evaluation indicators. The proposed evaluation criteria classify conditioned soils into three categories, defining the characteristic distribution ranges for each type. The XGBoost algorithm is adopted to establish a predictive model for soil conditioning effectiveness. The selected input variables for the predictive model include bentonite slurry injection ratio, bentonite slurry concentration, particle size of unconditioned sandy soil, clay content, and foam injection ratio, yielding a prediction accuracy rate of 80%.