盾构隧道工程中，由于横向地表沉降监测数据稀疏，传统数据驱动模型难以实现精准预测。针对盾构隧道施工横向地表沉降实测数据缺失问题，提出一种基于多保真残差神经网络（MRNN）的横向地表沉降预测方法，融合理论解析解、有限元模拟与工程实测等多源数据，构建多层嵌套的残差学习框架。首先，通过低保真度（解析解）与中保真度（有限元模拟）数据验证MRNN架构的可行性； 随后，引入高保真度（实测）数据训练残差模型，逐级修正预测结果。试验结果表明： 中低保真度模型在有限元数据测试集上决定系数（R²）达0.99，模型具有可行性； 融合实测数据后，MRNN模型在工程实测数据上的R²为0.91，平均绝对误差（Ｅ_MAE）为0.76 mm，优于单一神经网络模型（R²=0.34），验证了不同保真度数据对模型的贡献； 与其他几种常用模型（随机森林、支持向量回归和极端梯度提升）进行对比分析，3个模型预测结果（R²分别为0.70、0.48、0.33）均低于MRNN模型， MRNN模型在小样本数据情境下更具优势。研究证实，MRNN框架能够有效整合不同精度和成本的数据源，利用大量低成本理论数据捕捉全局趋势，并借助少量高精度实测数据实现局部修正，为解决盾构施工中小样本条件下的地表沉降高精度预测提供了新的技术路径。

In shield tunneling, traditional data-driven models struggle to achieve accurate predictions due to the sparsity of transverse surface settlement monitoring data. To address this challenge, a transverse surface settlement prediction method based on a multi-fidelity residual neural network (MRNN) is proposed. This approach integrates multi-source data, including theoretical analytical solutions, finite element simulations, and engineering measurements, to construct a multi-level nested residual learning framework. First, the feasibility of the MRNN architecture is validated using low-fidelity (analytical solution) and mediumfidelity (finite element simulation) data. High-fidelity (measured) data are then introduced to train the residual model, progressively refining the prediction results. Experimental results reveal the following: (1) The low- and medium-fidelity models achieve a coefficient of determination (R²) of 0.99 on the finite element data test set, confirming the model’s feasibility; (2) After measured data are incorporated, the MRNN model achieves an R² of 0.91 and mean absolute error of 0.76 mm on engineering measured data, significantly outperforming the single neural network model (R²=0.34), thus highlighting the importance of varying fidelity data; (3) Compared with other commonly used models, such as random forest, support vector regression, and extreme gradient boosting(R² of 0.70, 0.48, and 0.33, respectively), the MRNN model yields superior results. The MRNN model shows distinct advantages in scenarios with small sample sizes. These results confirm that the MRNN framework can effectively integrate data sources of varying accuracy and cost, leveraging large amounts of low-cost theoretical data to capture global trends while employing limited high-precision measured data for local refinement. This approach offers a new technical pathway for achieving high-precision predictions of surface settlement in shield tunneling, even under conditions of limited sample data.