研究一种混合极端学习机器与Dingo优化算法相结合,用于模拟沙混合物中的液化触发
Mohammed Majeed Hameed1,2, Adil Masood3, Aman Srivastava4
1Department of Civil Engineering, Al-Maarif University College, Ramadi, Iraq. mohmmag1@gmail.com.
Scientific reports
|May 11, 2024
概括
一个新的混合模型将极端学习机器 (ELM) 与Dingo优化算法 (DOA) 结合起来,准确地预测土壤液化阻力. 这种先进的模型,具有用户友好的GUI,增强了地质工程危险评估.
科学领域:
- 地质技术工程 地质技术工程
- 地震工程的工程是地震工程.
- 计算智能是一种计算智能.
背景情况:
- 土壤液化是一个重要的地震危险,在松散,和的土壤中造成灾难性的土壤失效.
- 精确预测液化阻力对于降低危险,风险评估和地质工程进步至关重要.
研究的目的:
- 引入一种新的混合模型,即具有Dingo优化算法 (ELM-DOA) 的极端学习机器,用于估计基于应变能量的液化阻力.
- 将ELM-DOA模型的性能与ELM,ANFIS-FCM和ANFIS-Sub等传统方法进行比较.
- 评估数据预处理技术 (线性与非线性规范化) 对模型预测准确性的影响.
主要方法:
- 开发一种混合ELM-DOA模型用于液化阻力预测.
- 与现有模型进行比较分析:极端学习机器 (ELM),具有模糊C-Means的自适应神经模糊推理系统 (ANFIS-FCM) 和ANFIS子集群.
- 应用两个数据预处理方法:传统的线性和非线性规范化.
主要成果:
- 与线性规范化相比,非线性规范化在所有模型中显著提高了约25%的预测性能.
- 该ELM-DOA模型表现出卓越的准确性,实现了最低的RMSE (484.286 J/m3),MAPE (24.900%),MAE (404.416 J/m3) 和最高的R2 (0.935).
- 为ELM-DOA模型开发了一个图形用户界面 (GUI),以提高工程师和研究人员的实用性.
结论:
- 拟议的混合ELM-DOA模型,通过非线性规范化来增强,为评估土壤液化阻力提供了一个高度准确和有效的工具.
- 开发的GUI可方便用户方便地访问模型的预测,从而提高其在地质工程中的实际实用性.
- 该研究强调了混合智能模型和先进数据预处理的潜力,以减轻地震引起的液化危险.
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