为研究深基坑开挖过程中的受力变形规律，以长春地铁34 m深基坑为研究对象，采用MIDAS GTS对各个工况下基坑开挖进行模拟研究，得到不同工况下地表沉降、围护结构变形、轴力变化规律，将模拟数据与实际监测数据进行对比，验证模型的准确性，并根据所建立的模型提取各工况下最大地表沉降值，以开挖深度、支撑数量和降水量作为输入参数，对深基坑地表沉降进行预测。首先将模型提取的地表沉降值作为样本数据，通过K均值聚类算法将样本数据进行归类，为径向基神经网络确定基函数中心，构建K均值聚类算法K-RBF预测模型，进而对地表沉降进行预测。根据工程实例结果表明，K-RBF预测模型较普通RBF模型更接近实际监测数据，与实际监测数据相比，K-RBF的均方误差为0.212 mm、平均绝对误差为0.278 mm、最大绝对误差为1.11 mm，具有较好的预测精度。
To study the stress deformation law in the process of
deep foundation pit excavation, a case study is conducted on a 34 mdeep
foundation pit of Changchun metro, and the MIDAS GTS is adopted to simulate the
foundation pit excavation process under various conditions, obtaining the
surface settlement, deformation of retaining structure, and axial force
variation law. Furthermore, the simulated results are compared with the
monitoring results to verify the feasibility of the model. The surface
settlements under various conditions are extracted according the model
established, and the excavation depth, support amount, and dewatering volume
are taken as input parameters to predict the surface settlement of the
foundation pit. First, the maximum surface settlement extracted from the model
is taken as the sample data, and the sample data are classified by Kmeans clustering algorithm to determine the basis function center
for the radial basis neural network. Then, the Kmeans
clustering algorithm Kradial basis function(RBF)
prediction model is constructed to predict the surface settlement. The
applicable results show that the surface settlement prediction results by KRBF prediction model is closer to the actual monitoring data than
that of traditional RBF model. Compared with the actual monitoring data, the
mean square error, the mean absolute error, and the maximum absolute error of KRBF are 0.212 mm, 0.278 mm, and 1.11 mm, respectively, indicating a
good prediction accuracy.
