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Abstract

To accurately predict the permanent deformation of subgrade soil under the coupling effect of freeze-thaw cycles and traffic loads,a hybrid neural network model based on long short-term memory (LSTM ) and Transformer was proposed,building upon the results of dynamic triaxial tests.This model effectively captured the temporal dependencies and complex interactive effects among input variables,thereby significantly enhancing the accuracy and generalization ability of permanent deformation prediction of subgrade soil.The results indicate that higher confining pressure improves the resistance of subgrade soil to deformation,but its permanent deformation behavior is still significantly influenced by the magnitude of cyclic loading and the number of freeze-thaw cycles.A comparative analysis with traditional empirical regression models validates the superiority of the proposed hybrid model in addressing nonlinear deformation issues.Furthermore,sensitivity analysis results demonstrate that confining pressure and fluid limit are the primary factors affecting the permanent deformation of subgrade soil.This study can provide reference and guidance for the construction of durable subgrade in seasonally frozen areas.In the design stage,it is essential to select fillers carefully and implement anti-freezing structures,while during the operation and maintenance stage,attention should be paid to overloading phenomena.

Publication Date

2-22-2025

DOI

10.14048/j.issn.1671-2579.2025.01.008

First Page

67

Last Page

72

Submission Date

March 2025

Reference

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