使用WD-IPSO-GRU模型预测道斜坡的表面位移
Guoqing Ma1, Xiaopeng Zang1, Shitong Chen2
1College of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China.
Scientific reports
|September 5, 2024
概括
一个新的混合模型结合了波形分解和一个改进的粒子群优化调节的门反复单元,准确地预测了道挖掘坡度稳定的表面位移,提高了地质工程评估.
科学领域:
- 地质技术工程 地质技术工程
- 计算智能是一种计算智能.
- 数据科学数据科学数据科学
背景情况:
- 斜坡稳定性评估对于道挖掘安全至关重要.
- 准确预测表面位移是监测斜坡稳定的关键.
- 现有的方法可能会在复杂的时间序列位移数据上扎.
研究的目的:
- 开发一种混合模型,用于预测底部超负荷斜坡中道挖掘期间的表面位移.
- 提高斜坡稳定性评估的准确性和可靠性.
- 为了克服特征提取和参数优化方面的挑战,对位移时间序列数据进行优化.
主要方法:
- 构建了一个混合模型,将波纹分解 (WD) 集成为特征提取和门式循环单元 (GRU) 进行预测.
- 优化了GRU超参数,使用了改进的粒子集群优化 (IPSO) 算法,结合了遗传算法概念.
- 应用了WD来分解位移数据和放弃技术,以防止GRU过度装配.
主要成果:
- WD-IPSO-GRU模型显著提高了表面位移预测的准确性.
- 与原始数据相比,WD预处理提高了精度28% (01) 和45.9% (02).
- IPSO优化进一步提高了准确度的76% (01) 和56.7% (02).
结论:
- WD-IPSO-GRU模型提供了一个强大的解决方案,用于预测道挖掘过程中斜坡的表面位移.
- 该模型在地质技术应用中显示出强大的概括能力和可靠性.
- 验证了在评估斜坡稳定性和确保道施工安全方面实际使用的潜力.
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