为更精准预测基坑开挖卸荷引起的周边地表沉降，通过改进神经网络算法（neural network algorithm, NNA），提出一种具有信息反馈和反向学习机制的神经网络优化算法（neural network algorithm with feedback mechanism and reverse learning, FBRLNNA），并结合反向传播（back propagation, BP）神经网络构建地表沉降预测模型，将提出的沉降预测模型在青岛15号线地铁工程基坑中进行应用与验证。基于18种基准函数比较FBRLNNA与9种竞争优化算法的表现，仿真试验表明，FBRLNNA在80%的基础基准函数上均表现出更优的性能。对比分析FBRLNNA-BP模型及其他4种模型的基坑沉降预测结果，FBRLNNA-BP模型的均方误差（mean squared error, MSE）、平均绝对误差（mean absolute error, MAE）、均方根误差（root mean square error, RMSE）及决定系数（R²）均最佳，沉降预测结果误差小于5%，表明该预测模型具有更好的沉降预测精度。研究成果可为基坑开挖引发的地表沉降预测提供了新的方法和参考。

To more accurately predict the surrounding ground surface settlement resulting from unloading during foundation pit excavation, a neural network algorithm with feedback mechanism and reverse learning (FBRLNNA) was proposed in this study, which was based on the neural network algorithm (NNA). By integrating this with back propagation (BP) neural networks, a surface settlement prediction model was constructed and subsequently applied and validated in the foundation pit of Qingdao Metro Line 15. In comparative tests with nine optimization algorithms across 18 benchmark functions, superior optimization results were achieved by FBRLNNA in 80% of the cases. Additionally, an analysis of the FBRLNNA-BP model against four other settlement prediction models shows that it performs best in terms of mean squared error (MSE), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²), with prediction errors maintained within 5%, demonstrating the model's enhanced accuracy for settlement prediction. The research results provide a novel approach and valuable reference for predicting surface settlement induced by foundation pit excavation.