Abstract:Current approaches for back analysis of displacements have some shortcomings. For optimization theory-based approach,it is easy to get trapped at local optimalization,instable in identifying optimal solution,and slow in converging for many parameters to be inversed. For artificial neural network based-approach,it is difficult to converge with desired accuracy when the search space is relatively large,and,furthermore,to get the sole inversion results because of the indefiniteness of trained network. The genetic algorithm-based approach can get the optimal solution only if experience-based interference is implemented. The approach based on artificial neural networks and genetic algorithms is effective only if the search space is relatively small. As a result,new approach for back analysis of displacements has to be established to overcome the shortcomings. Adaptive neuro-fuzzy inference system is used to establish the new approach for back analysis of displacements. The approach has been used to inverse the mechanical parameters of a prescribed elasto-plastic problem. The inversed results show that this approach can rapidly get a stable and accurate solution within a relatively large solution space and the approach is superior to current approaches.