A predictive model of ultimate bearing capacity for large-diameter and super-long steel pipe piles is established based on Support Vector Machine(SVM) and Independent Component Analysis(ICA). Firstly the FastICA algorithm of independent component analysis is imported to abstract the independent components

which can better express the essence in sample data and keep statistics independent without correlation as well as obey the Gaussian distribution

from large-diameter and super-long steel pipe pile data measured in an actual engineering. Secondly

by determining the support vector machine as classifier

whose input are the previously extracted components and whose output is the predicted bearing capacity

ICASVM_Q model is established to predict bearing capacity of large-diameter and super-long steel pipe piles. By data testing of a bridge engineering

it is shown that the predicted results of ICASVM_Q are better than SVM_Q which uses original engineering data as input of SVM model. Studies indicated that the modeling method by combining SVM and ICA used to predict ultimate bearing capacity of large-diameter and super-long steel pipe piles is feasible and the predicting bearing capacity of the ICASVM_Q model can be referred by designers in engineering. This method can also serve as a reference of intelligent forecast in other areas.