使用极端机器学习来估计基岩纤维增强高土混合物的压力强度
Zeynep Bala Duranay1, Yasemin Aslan Topçuoğlu2, Zülfü Gürocak2
1Electrical Electronics Engineering Department, Technology Faculty, Firat University, Elazig 23119, Türkiye.
Materials (Basel, Switzerland)
|January 25, 2025
概括
这项研究引入了极端学习机器 (ELM) 的新应用,用于预测基岩纤维增强粘土的不受限制的压力强度. 与传统的实验方法相比,ELM模型准确地估计了土壤强度,节省了时间和资源.
科学领域:
- 地质技术工程 地质技术工程
- 材料科学 是一种材料科学.
- 人工智能的人工智能
背景情况:
- 研究用玄武纤维增强的粘土的不受限制的压力强度 (qu).
- 解决地质工程中准确预测模型的需求.
- 突出了将极端学习机器 (ELM) 应用于基岩纤维增强粘土的新奇性.
研究的目的:
- 使用极端学习机器 (ELM) 估计基岩纤维增强粘土的未受限制的压力强度 (qu).
- 开发一个与实验数据密切匹配的预测模型,减少对广泛物理测试的需求.
- 为人工智能在土壤力学中的应用提供新的见解.
主要方法:
- 试验性制备具有不同含水量 (20-35%) 和玄武岩纤维增强 (0-3%) 的粘土混合物.
- 基于实验结果的极端学习机器 (ELM) 模型的开发.
- 用5倍交叉验证与实验数据和其他AI模型进行比较,验证ELM模型.
主要成果:
- 该ELM模型表现出高预测准确度,R值为0.9976和RMSE为0.0001.
- ELM的预测与实验结果密切一致,证实了它的可靠性.
- ELM的性能优于其他人工智能模型,交叉验证RMSE为0.000174.
结论:
- 极端学习机器 (ELM) 是一个非常有效和准确的工具,用于预测基岩纤维增强粘土的压力强度.
- 开发的ELM模型为实验方法提供了可靠,高效的替代方案,节省了劳动力,材料和时间.
- 这项研究验证了ELM在各种强化和水含量条件下估计土壤强度的能力.
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