Abstract:

In order to improve the prediction accuracy of foundation pit deformation and the prediction stability, the structural parameters of BP neural network are optimized by genetic algorithm. The BP neural network is combined with grey model to establish a GABP neural network model. The preliminary prediction of the deformation sequence of the foudnation pit is realized by the model. And then the residual error is optimized by chaos theory; and the GABP neural network model considering the optimization of chaotic characteristics is further constructed based on the residual sequence. Finally, the SR test is introduced to the judgment of the deformation trend of the foundation pit to verify the accuracy of the prediction results. The case study shows that by using progressive optimization of the genetic algorithm and chaos theory, the prediction accuracy can be improved gradually, the validity of the prediction model is verified, and the consistency of the prediction results with the SR test results is better, so as to show that the reliability of the prediction model is high.

Key words: deep foundation pit, GABP neural network, SR test, deformation prediction, trend judgment