计算机与现代化 ›› 2024, Vol. 0 ›› Issue (07): 106-111.doi: 10.3969/j.issn.1006-2475.2024.07.016

• 算法设计与分析 • 上一篇    下一篇

基于DEFA-LSSAR的水利工程边坡力学参数预测模型



  

  1. (1.西安交通工程学院土木工程学院,陕西 西安 710300; 2.西安交通工程学院机械与电气工程学院,陕西 西安 710300)
  • 出版日期:2024-07-25 发布日期:2024-08-08
  • 基金资助:
    陕西省教育厅科学研究计划项目(23JP087); 陕西省自然科学基础研究计划项目(2023-JC-YB-464); 国家自然科学基金资助项目(52279140); 西安交通工程学院中青年基金资助项目(2022KY-36)

Prediction Model of Hydraulic Engineering Slope Mechanical Parameters Based on DEFA-LSSAR

  1. (1. College of Civil Engineering, Xi’an Traffic Engineering Institute, Xi’an 710300, China;
    2. School of Mechanical and Electrical Engineering, Xi’an Traffic Engineering Institute, Xi’an 710300, China)
  • Online:2024-07-25 Published:2024-08-08

摘要: 为了解决现有水利工程边坡力学参数预测模型准确率偏低的问题,利用最小二乘支持向量机LSSAR对水利工程边坡力学参数(弹性模量E)进行预测,结合改进的萤火虫算法对模型进行优化,提出一种基于DEFA-LSSAR的水利工程边坡力学参数预测模型。将本文所提模型分别与樽海鞘群算法、果蝇算法和哈里斯鹰优化算法优化的LSSAR模型进行对比。分析结果表明,所提出的模型预测准确率最高,达94%以上,且具有最小的适应度值,验证了所提模型的有效性和正确性。

关键词: 水利工程, 边坡稳定性, 最小二乘支持向量机, 萤火虫算法, 参数预测

Abstract: In order to solve the problem of low accuracy of existing hydraulic engineering slope mechanical parameter prediction models, the least squares support vector machine LSSAR is used to predict the hydraulic engineering slope mechanical parameters (elastic modulus E), and the improved firefly algorithm is used to optimize the model. A hydraulic engineering slope mechanical parameter prediction model based on DEFA-LSSAR is proposed. We compare the model proposed in this article with the LSSAR model optimized by the Salp Swarm Algorithm, Drosophila Algorithm, and Harris Eagle Optimization Algorithm, respectively. The analysis results show that the proposed model has the highest prediction accuracy, reaching over 94%, and has the smallest fitness value, verifying the effectiveness and correctness of the proposed model in this article.

Key words: water conservancy project, slope stability, least squares support vector machine, firefly algorithm, parameter prediction

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