Abstract:

The larger the soil compression coefficient, the higher the compressibility of the soil. Excessive compressibility of the soil is likely to cause geological hazards and cause irreversible losses. Predicting the soil compression coefficient has far-reaching significance for the prevention and minimization of geological hazards. Based on the principles of the BP neural network, with conventional physical parameters as input vector and compression coefficient as output vector, the use of the BP neural network with a strong nonlinear mapping ability and genetic algorithm can solve the problem of the BP neural network easily falling into the local optimal value. Based on constructing a compression coefficient prediction model by using the BP neural network, the genetic algorithm is used to optimize the model. After the optimization, the prediction performance is greatly improved. The maximum relative error and the minimum relative error are 22.38% and 1.13%, respectively. In engineering terms, an error within 25% is an acceptable range, so the prediction model can be used as an alternative tool to predict the compression factor to a certain extent.

Key words: Soil Compression Coefficient, BP Neural Network, Genetic Algorithm, Prediction