Abstract:
Aiming at the problem of traditional evaluation methods of deep foundation pit for selecting the retaining structure type, based on the statistical theory and following the principle of security, economic and reasonable, a Fisher discriminant analysis(FDA) model for selecting the retaining structure type for deep foundation pit is established. 10 selected indicators which influence selection of deep excavation program are taken into account as discriminant factors, and the supporting schemes for deep foundation pit are classified into 5 groups, viz. gravity of the cement-soil type, soil nailing wall, pile anchors, pile supports and underground continuous wall. After training and testing 64 sets of measured data, the discriminant functions of FDA are solved, the re-substitution method is introduced to verify the stability of FDA model and the ratio of mis-discrimination is 14.1%. Another 10 groups of measured data are tested as forecast samples by the proposed model, and the correct rate is equal to 100%. Therefore, the feasibility of the proposed model is validated. Moreover, the proposed model is adopted for the New World Center Project in China, and the prediction results are in line with the artificial neural network(ANN) and the actual situation. The result shows that the deep foundation pit supporting structure lectotype decision of FDA model has excellent discriminant performance and the resubstitution error rate is low. It is easy and efficient to make discriminant analysis using this model and it provides efficient method to select deep excavation retaining structure and a practical new approach to choose the structural type of deep foundation pit optimization.
