Abstract:

The equation of exponential curve model, which used to predict single pile limit bearing capacity, is super-set and nonlinear. Traditional optimization methods including the least squares method on the parameters regression of the exponential curve model often make the predicted results with a great deviation because of computational complexity and artificial factors. Therefore, the real coding based accelerating genetic algorithm (RAGA) is used to optimize the parameters and theoretical limit bearing capacity of exponential curve model. The RAGA solution program is compiled in the software MATLAB environment. Then some contrastive model applications to branch-piles is given. The results show that the RAGA based exponential curve model can better fit the measured data and effectively predict the limit bearing capacity, and RAGA is a high effective algorithm with many good properties such as high efficiency, high precision, fast computing speed, small artificial factors, etc.

Key words: exponential curve model, parameter optimization, genetic algorithm, single pile limit bearing capacity, pile with expanded branches and plates