基于PSO-SVM模型的深度挖掘土壤参数逆转和变形预测
Jing Zhao1, Longhui Chen1, Hongyin Yang2
1China Railway 11th Bureau Group First Engineering Co., Ltd., Xiangyang 441199, China.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
本研究引入了一种粒子群优化支持矢量机 (PSO-SVM) 模型,用于在深度挖掘过程中准确确定土壤参数. 这种方法提高了有限元分析的精度,并有效地预测了保留结构变形.
科学领域:
- 地质技术工程 地质技术工程
- 计算力学 计算力学 计算力学
背景情况:
- 在深度挖掘过程中,土壤参数发生变化,影响有限元素分析的准确性.
- 准确的土壤参数对于预测保持结构行为至关重要.
研究的目的:
- 使用PSO-SVM模型开发土壤参数的反转方法.
- 为了提高深度挖掘中有限元素分析的精度.
- 为了准确预测保持结构的变形.
主要方法:
- 使用粒子群集优化 (PSO) 来优化支向量机 (SVM) 参数 (C和g).
- 通过直角实验设计和有限元模拟,建立了水平位移和土壤参数之间的非线性映射.
- 从监测数据中逆转了土壤参数,并验证了它们的可靠性.
主要成果:
- 在峰值最大水平位移时,达到<1mm的绝对误差.
- 将最大相对误差从18.96%降低到7.63%.
- 证明了反向土壤参数的高精度和改进的有限元模拟精度.
结论:
- 在深度挖掘中,PSO-SVM反转方法显著提高了土壤参数的准确性.
- 该方法有效地反映了土壤在施工过程中的机械性能.
- 倒置参数可以在安全值内可靠地预测随后的支结构变形.
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