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.
LIU Chunmei, JIANG Wei, GONG Yafeng, LIN Siyuan
. Analysis and Prediction of Foundation Pit Deformation
Based on Kmeans Improved Radial Basis Function Neural
Network[J]. Tunnel Construction, 2021
, 41(S2)
: 246
-254
.
DOI: 10.3973/j.issn.2096-4498.2021.S2.031
