基于ABC-BP模型的基坑地表沉降预测

目前基坑工程普遍仅关心地表沉降值是否超出监测预警值，缺乏对基坑地表沉降短期实时预测的有效方法，降低了基坑安全性。利用人工蜂群算法优化BP神经网络的组合模型可合理预测基坑地表沉降。首先，结合灰色相关度理论，对输入变量进行筛选，提高网络结构的高效性；接着，利用人工蜂群算法优化BP神经网络初始值，实现对地表沉降累计最大值及位置的预测；最后，将ABC-BP模型与其他常见神经网络预测模型对比，验证模型有效性。从预测和对比结果中可知，ABC-BP模型训练及预测结果的平均相对误差为3.27%，均方根误差为3.87，验证模型有效。

Prediction of Surface Settlement of Foundation Pit Based on ABC-BP Model

At present, the foundation pit engineering generally only cares whether the ground settlement value exceeds the monitoring and early warning value, and it lacks an effective method for short-term and real-time prediction of foundation pit surface settlement, which reduces the safety of the foundation pit. Using artificial bee colony algorithm to optimize the combination model of BP neural network can reasonably predict the settlement of the foundation pit surface. First, the gray correlation theory was combined to filter the input variables to improve the efficiency of the network structure. Then, the artificial bee colony algorithm was used to optimize the initial value of the BP neural network to realize the prediction of the cumulative maximum value and location of the surface settlement. Finally, ABC-BP model was compared with other common neural network prediction models to verify the validity of the model. It can be seen from the prediction and comparison results that the average relative error of ABC-BP model training and prediction results is 3.27%, and the root mean square error is 3.87, verifying that the model is effective.