基于PSO优化参数的VDM-GRU深底坑的变形预测研究
Ronggui Liu1, Qing Zhang2, Feifei Jiang3
1Faculty of Civil Engineering and Mechanics, Jiangsu University, Zhenjiang 212013, China.
这项研究引入了一种新的PSO-VMD-GRU模型,用于准确预测深层基础坑变形. 该模型通过结合变化模式分解和封闭循环单元,显著提高了预测准确性,超过了传统方法.
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
- 机器学习应用 机器学习应用
背景情况:
- 深层基础坑变形预测对于建筑质量和安全至关重要.
- 非线性和不稳定的变形数据给传统的预测方法带来了挑战.
- 现有的方法很难在时间序列变形数据中捕捉相关性.
研究的目的:
- 为深层基础坑变形开发一个先进的预测模型.
- 为了利用变量模式分解 (VMD) 进行非静止数据和门式循环单位 (GRU) 复杂的时间序列.
- 为了提高地质工程应用中的预测准确性和可靠性.
主要方法:
- 提出了一个混合模型,整合了粒子群集优化 (PSO),变化模式分解 (VMD) 和门式循环单位 (GRU).
- 使用PSO优化VMD以将数据分解为内在模式函数 (IMFs) 和GRU模型用于IMF预测.
- 通过总结个别IMF的加权预测值来进行集体预测.
主要成果:
- 与PSO-GRU相比,PSO-VMD-GRU模型在原始数据 (0.502毫米) 和EMD分解 (0.462毫米) 上实现了明显较低的平均绝对误差 (0.127毫米).
- 与LSTM,GRU和PSO-LSTM模型相比,平均平方误差显著减少 (高达85.17%).
- 该模型还显示,与PSO-LSTM相比,计算时间减少了8.57%,并通过增加预测步骤保持了准确性.
结论:
- VMD分解通过提高数据适用性来提高变形预测模型的准确性.
- 该PSO-VMD-GRU模型提供可靠的准确性和广泛适用于深层基础坑工程.
- 预测步骤大小增加会对变形预测的准确性产生负面影响.
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