This paper presents a settlement prediction method based on PSO optimized SVM for improving the accuracy of foundation pit settlement prediction. Firstly, the method uses the SA algorithm to improve the traditional PSO algorithm, and thus, the overall optimization-seeking ability of the PSO algorithm is improved. Secondly, the improved PSO algorithm is used to train the SVM algorithm. Finally, the optimal SVM model is obtained, and the trained model is used in foundation pit settlement prediction. The results suggest that the settling results obtained from the optimized model are closer to the actual values and also more advantageous in indicators such as RMSE. The fitting value R2 = 0.9641, which is greater, indicates a better fitting effect. Thus, it is indicated that the improvement method is feasible.
In order to better determine the model parameters and improve the safety and stability of construction, an optimized identification method of constitutive model parameters with improved RCGA is proposed by combining the current optimization theory. The experimental results indicate that the improved RCGA algorithm proposed in the study has stronger recognition capabilities and optimization effects than the traditional NSGA-II algorithm and determine the parameters of the constitutive model more accurately. Moreover, it is found that the parameters obtained by the proposed improved algorithm have validity and accuracy, when parameter
P
0
′
=
50
kPa, comparing the obtained optimal parameters with the real laboratory clay parameters.
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