Abstract: The compression deformation is an important factor that affecting the magnitude of the settlement. If its value is too large, it will induce the major geological hazards. The compression modulus is an important index. Predicting the magnitude of the compression modulus is of great significance for preventing geological hazards. Throughout basic principles of the generalized regression neural network, with the conventional soil property indicators as the input vector and the compressive modulus (E_S1-2) as the output vector, the maximum and minimum relative errors of the network output results are 10.73% and 0%, respectively. The mean square error is 0.6831, which is in good agreement with the true value, so the model can be used to predict the compressive modulus. Comparing the mean square error under different smoothing factor values, the mean square error at 0.9 is smaller than other values, so the smoothing factor value should be selected correctly during the parameter setting process. To prove the performance of the prediction model throughout the generalized regression neural network, compared with other algorithms based on the mean square error, the mean square error of the generalized regression neural network prediction model is less than that of other algorithms, indicating that the prediction model throughout the generalized regression neural network has superior performance based on prediction models constructed by other algorithms.

Key words: Prediction, Compression Modulus, Generalized Regression Neural Network, Soil Physical Property Index, Smoothing Factor