Prediction of the undrained shear strength of clay from CPTu data using artificial neural network
Xie Wenqiang1 Cai Guojun2 Wang Rui1 Zhang Jianmin1
1. National Engineering Laboratory for Green & Safe Construction Technology in Urban Rail Transit, Tsinghua University, Beijing 100084, China;
2. Southeast University, Nanjing 211189, China
Abstract：A method for the prediction of the undrained shear strength of clay based on CPTu data is developed using an artificial neural network model. In-situ CPTu and vane shear tests are conducted on three sites to acquire CPTu and the corresponding undrained shear strength data from 33 locations, which are used to establish and validate the artificial neural network model. The influence of input data dimension, hidden layer number, neurons in hidden layers, and improved training algorithm on the prediction error and stability of the model is analyzed. Based on the analysis results, a model structure is chosen for the prediction of undrained shear strength. After machine learning through the training set, the proposed model is able to effectively predict the undrained shear strength of clay based on the tip resistance and pore water pressure obtained from CPTu data. The predictions are in good agreement with the vane shear test results. Compared with traditional empirical undrained shear strength estimation equations, the proposed artificial neural network model can significantly improve the correlation and reduce the error between predicted and measured values.
谢文强 蔡国军 王 睿 张建民. 基于CPTu数据的黏性土不排水抗剪强度#br#
神经网络预测[J]. 土木工程学报, 2019, 52(S2): 35-41.
Xie Wenqiang Cai Guojun Wang Rui Zhang Jianmin. Prediction of the undrained shear strength of clay from CPTu data using artificial neural network. 土木工程学报, 2019, 52(S2): 35-41.
