使用双核和元启发算法改进的相关性矢量机器预测岩石爆炸期间的地面振动.
Yewuhalashet Fissha1,2, Jitendra Khatti3, Hajime Ikeda4
1Department of Geosciences, Geotechnology, and Materials Engineering for Resources, Graduate School of International Resource Sciences, Akita University, Akita, 010-8502, Japan. yowagaye@gmail.com.
Scientific reports
|August 28, 2024
概括
本研究介绍了优化的相关向量机 (RVM) 模型,用于预测岩石喷造成的地面振动. PSO_DRVM模型MD29表现出卓越的性能,提高了采矿和土木工程项目的安全性.
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.
- 环境监测 环境监测
背景情况:
- 岩石爆破造成的地面振动带来了重大的环境和安全风险.
- 准确预测峰值粒子速度 (PPV) 对于减轻这些危险至关重要.
- 现有的方法需要强大的预测模型来进行有效的风险评估.
研究的目的:
- 开发和比较新的相关向量机 (RVM) 模型,用于预测采石场爆炸中的PPV.
- 确定用于地面振动估计的最有效的RVM模型.
- 为工程师提供一个工具,为振动预测选择最佳参数.
主要方法:
- 采用传统和优化的RVM模型进行PPV预测.
- 使用粒子群优化 (PSO) 方法来提高RVM性能 (PSO_DRVM).
- 评估了33个RVM模型,包括建议的PSO_DRVM模型MD29,使用各种性能指标.
主要成果:
- 所有测试的RVM模型都实现了超过0.85的性能得分,这表明预测准确度很强.
- 与其他RVM模型相比,PSO_DRVM模型MD29表现出卓越的性能.
- 关键性能指标包括RMSE为16.2272毫米/秒,R值为0.9175和IOA为0.8239.
结论:
- 优化的RVM模型,特别是PSO_DRVM MD29,对于预测爆炸造成的地面振动非常有效.
- 该研究为选择适当的内核函数和超参数提供了宝贵的见解.
- 这项研究可以显著提高矿业和民用行业的安全协议和运营效率.
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