基于PSO-LSSVM模型的埋藏钢管爆炸振动速度的预测
Hongyu Zhang1,2,3, Shengwu Tu1,2, Senlin Nie1,2
1Hubei Key Laboratory of Blasting Engineering, Jianghan University, Wuhan 430056, China.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
准确预测爆炸负载下的埋藏管道振动速度对于安全至关重要. 一个粒子集群优化-最小平方支持向量机 (PSO-LSSVM) 模型为这些复杂的工程挑战提供了卓越的准确性.
科学领域:
- 地质技术工程 地质技术工程
- 结构动力学 结构动力学
- 计算智能是一种计算智能.
背景情况:
- 在爆炸振动下,邻近的埋藏管道的安全运行至关重要.
- 精确预测峰值振动速度对于工程安全至关重要.
- 在复杂的喷气条件下,现有的方法可能缺乏精度.
研究的目的:
- 开发和验证一个高度准确的模型,用于预测受爆负荷的埋藏管道的峰值振动速度.
- 将拟议模型的性能与传统方法进行比较.
主要方法:
- 利用灰色相关性分析对基于爆发模型测试数据的影响因素的灵敏度分析.
- 开发了一个粒子优化优化最小方程支持向量机 (PSO-LSSVM) 模型.
- 将PSO-LSSVM预测与BP神经网络和经验公式进行了比较.
主要成果:
- 该PSO-LSSVM模型实现了91.51%的合适相关系数 (R2).
- 该模型显示了2.95%的低根平均平方误差 (RMSE) 和8.69%的平均相对误差 (MRE).
- 99.03%的高纳什系数 (NSE) 表示出色的预测准确性和概括能力.
结论:
- 在预测管道峰值振动速度方面,PSO-LSSVM模型显著超过BP神经网络和经验公式.
- 这种先进的模型提供了一种可靠的方法来评估爆炸振动下的埋藏管道安全性.
- 该研究提供了一种在复杂的工程场景中预测振动速度的新方法.
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