基于GPSO-BP神经网络及其应用的机械参数的后分析
Song Shi1, Yichen Miao2, Cheng Di3
1College of Water Resource and Hydropower, Sichuan University, Chengdu, 610065, China.
Scientific reports
|March 31, 2025
概括
这项研究引入了一种新的GA-PSO-BP神经网络模型,用于在地下工程中准确的岩石质量参数反转. 与传统方法相比,该模型在数值模拟中显著提高了准确性和效率.
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
- 在工程领域的人工智能.
背景情况:
- 确定岩石质量参数的传统方法通常会产生不适合地下工程中的数值模拟的结果.
- 使用移位监测的回流分析为参数确定提供了一个有前途的替代方案.
研究的目的:
- 开发和验证一个先进的神经网络模型,用于精确反转岩石质量的机械参数 (E,μ,φ,c).
- 为了提高深埋道设计和施工中的数值模拟的可靠性.
主要方法:
- 一个使用直角和均设计的实验方案,用于生成神经网络的训练数据.
- 开发一种混合基因算法-粒子优化-反向传播 (GA-PSO-BP) 神经网络模型.
- 应用GPSO-BP模型用于反转岩石质量参数和随后的前向数值模拟.
主要成果:
- 与BP,GA-BP和PSO-BP模型相比,GPSO-BP模型显示出更高的融合速度和准确性.
- 该模型在小数据集和复杂问题上表现出色,显示出更好的数据拟合和排名分析得分.
- 使用GPSO-BP衍生的参数进行前模拟,在四个监测项目中平均误差为4.34%的低误差.
结论:
- 拟议的GA-PSO-BP神经网络模型为地下工程中的岩石质量参数逆转提供了一种有效和准确的方法.
- 这种方法显著提高了数值模拟的可靠性,从而为深埋道提供了更好的设计和施工结果.
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