Prediction Method of Characteristic Value of Foundation Bearing Capacity Based on Machine Learning Algorithm
DOI:
https://doi.org/10.13052/ejcm2642-2085.3122Keywords:
Machine learning, RBF neural network, improved relief algorithm, foundation bearing capacity, characteristic value, predictionAbstract
In this paper, a prediction method of characteristic value of foundation bearing capacity based on machine learning algorithm is proposed. Firstly, the influencing factors of foundation bearing capacity are analyzed, and then the prediction parameters of foundation pressure strength and foundation strength are calculated. The prediction error was obtained by comparing the difference between the predicted value and the actual intensity, which was used as the optimization value to improve the accuracy of the prediction results of the characteristic values of the subsequent bearing capacity. Then, by calculating the characteristic parameters of foundation mechanics and establishing the boundary conditions of foundation bearing capacity, the mathematical model of foundation bearing capacity is constructed, so as to complete the analysis of the mechanical characteristics of foundation bearing capacity. The analysis results and foundation strength prediction parameters are input into the RBF neural network model. On the basis of optimizing parameter weights by the improved Relief algorithm, the prediction results of characteristic values of foundation bearing capacity are obtained by using the hyperparameters of THE RBF neural network algorithm. Experimental results show that the prediction results of this method are always in a controllable range, and the prediction error rate is between 1.21% and 1.35%, and the prediction time is between 30.1 min and 32.5 min, indicating that this method has high prediction accuracy and timeliness.
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