Conventional methods were tested to predict the properties of soil struggle to accurately obtain its compression modulus Es.In this study we developed a nonparametric integrated method of optimization based on machine learning to calculate Es and compare it with the traditional model of regression to this end.We chose 203 groups of physical and mechanical indices of samples of peaty soil from the Kunming Metro Line 5 Exhibition Center for this purpose.Eight important physical indices were used as the input set,and the weights and thresholds of the input layer,hidden layer,and output layer of the BP neural network were optimized by using a genetic algorithm.The correlation coefficient,accuracy,and root mean-squared error were used to optimize the parameters of the algorithm,and the resulting model was applied to a variety of soils and compared with prevalent methods in the area.Finally,the predictive performance of the proposed method was compared with the results of the relevant empirical formula.The results showed that the GA-BP neural network-based method was highly adaptable to the analysis of samples,converged quickly,and generated accurate and reliable results.This method could be used to predict multiple parameters of soft soil.
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