针对天津7号线区间盾构隧道下穿老旧建筑物，采用基于测量机器人的自动化监测获取大量建筑物监测变形数据，通过机器学习算法研究了建筑物瞬时沉降与平均速度、推力、注浆量、盾构距离、注浆压力等盾构施工参数之间的关系，并建立建筑物沉降预测模型。结果表明：盾构施工下穿老旧建筑物时，在 -50 ~ 70 m范围内老旧建筑沉降均会受盾构施工影响；盾构施工诱发差异沉降的重要原因，是建筑物不同立面的沉降差异明显；盾构施工参数注浆量、推力、平均速度与瞬时沉降为正相关关系，其中盾构距离对于建筑物沉降影响最大；盾构隧道下穿老旧建筑物的施工参数合理，建筑物的沉降变形在合理范围内；通过机器学习算法建立的预测模型，其预测沉降曲线与监测实测沉降曲线基本吻合，具有较好的预测能力。为后续盾构隧道施工地表沉降的预测和控制提供参考。

Shield tunnel construction impacts old buildings in urban areas. Extensive building deformation data was collected for Tianjin Metro Line 7's shield tunnel passing beneath old buildings using automated measurement robots. Machine learning algorithms were applied to analyze the correlation between building instantaneous settlement and shield construction parameters such as average velocity, thrust, grouting volume, shield distance, and grouting pressure. A predictive model for building settlement was established. The results show that old buildings within -50 ~ 70 m are affected by shield construction. Differential settlement is significant, with noticeable differences on various facades. Grouting volume, thrust, and average velocity positively correlate with instantaneous settlement, with shield distance having the greatest impact. Reasonable construction parameters ensure building settlement and deformation remain within acceptable limits. The machine learning-based predictive model closely aligns with actual settlement curves, demonstrating robust predictive capabilities. This provides valuable insights for predicting and controlling surface settlement in future shield tunnel projects.