Research on HC-LSSVM Model for Soft Soil Settlement Prediction Based on Homotopy Continuation Method

Prediction of soft soil settlement is an important research topic in the field of civil engineering, and the least square support vector machine is one of the commonly used prediction methods at present. Nonetheless, the existing LSSVM models have problems of low search efficiency in the search proc...

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Bibliographic Details
Published in:Applied sciences 2021-11, Vol.11 (22), p.10666
Main Authors: Cui, Guangjun, Xiong, Shenghua, Zhou, Cuiying, Liu, Zhen
Format: Article
Language:English
Subjects:
Accuracy
Civil engineering
Continuation methods
Error correction
HC-LSSVM model
homotopy continuation method
Lagrange multiplier
Learning
LSSVM model
Methods
model parameter solution
Neural networks
Optimization
Predictions
Retention
Search process
Settlement analysis
Soft ground
soft soil settlement prediction
Soil settlement
Support vector machines
System theory
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Summary:Prediction of soft soil settlement is an important research topic in the field of civil engineering, and the least square support vector machine is one of the commonly used prediction methods at present. Nonetheless, the existing LSSVM models have problems of low search efficiency in the search process and lack of global optimal solution in the search results. In order to solve this problem, based on the leave-one-out cross-validation method, the homotopy continuation method was used to optimize the LSSVM model parameters, and then the HC-LSSVM model was constructed with the goal of minimizing the sum of squares of the prediction error of the full sample retention one. Finally, the rationality and correctness of the model are verified by engineering application. The results show that the HC-LSSVM model constructed in this study can accurately predict the settlement of soft ground, which is superior to the common LSSVM model and solves the problem that the parameters of LSSVM model cannot be solved optimally. The research results provide a new method for prediction of soft soil settlement.
ISSN:2076-3417
2076-3417
DOI:10.3390/app112210666