| description abstract | Tunnels are often designed by uncertain geotechnical data. In order to reduce these uncertainties, back analysis is commonly selected to re-estimate the assumed parameters. This paper presents a novel, intelligent back analysis method combining fuzzy systems, imperialistic competitive algorithm, and numerical analysis. The proposed methodology comprises three phases. First, a database of a real case study and numerical analysis are used to develop the training and testing data of the study. In the second phase, the nonlinear relationship of two sets of parameters, including geomechanical parameters of the soil mass and the zone stress conditions, with surface settlement is investigated by three fuzzy models. These models are designed by three methods including particle swarm optimization, imperialistic competitive algorithm, and integration of nearest neighborhood clustering with gradient descent training. In the last phase, imperialistic competitive algorithm is employed one more time to implement the back analysis procedure in the three tuned fuzzy models. Finally, verification of the models is done with the numerical analysis on the results of back analysis, and then the results are compared with the measured values of settlements. The results introduced the particle swarm optimization tuned fuzzy model as the most accurate intelligent model. | |
