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作 者:张安顺 ZHANG Anshun(China Railway 11th Bureau Group Corporation Limited,Wuhan,Hubei 430061,China)
机构地区:[1]中铁十一局集团有限公司,湖北武汉430061
出 处:《中外公路》2025年第1期67-72,共6页Journal of China & Foreign Highway
基 金:国家重点研发计划项目(编号:2023YFB2603900)。
摘 要:为了准确预测冻融循环和交通荷载耦合作用下路基土的永久变形,该文在动三轴试验结果的基础上,提出了一种基于长短期记忆网络LSTM和Transformer的混合神经网络模型。该模型能够有效捕捉输入变量之间的时间依赖关系和复杂交互影响,从而能够显著提高路基土永久变形预测的精度和泛化能力。结果表明:较高的围压提高了路基土的抗变形能力,但其永久变形行为仍受循环荷载幅值和冻融循环次数的影响。通过与传统经验回归模型的对比分析,验证了所提出的混合模型在处理非线性变形问题方面的优越性。由敏感性分析结果可知:围压和液限是影响路基土永久变形的主要因素。该结论可为季冻区耐久性路基建造提供参考与借鉴,并发现设计阶段应优选填料并设置防冻结构,运维阶段须控制超载现象。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.
关 键 词:路基工程 永久变形 冻融循环 动三轴试验 混合神经网络模型
分 类 号:U416.1[交通运输工程—道路与铁道工程]
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