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基于Kriging的替代数据丰富人工神经网络,预测透性水泥稳定基的强度和透性
Xiaoming Wang1, Yuanjie Xiao2,3, Wenqi Li1
1School of Civil Engineering, Central South University, Changsha, China.
Nature communications
|June 7, 2024
概括
这项研究引入了一种新的框架,用于使用有限的数据预测透性水泥稳定基材 (PCBM) 的机械强度和透性. 基于Kriging的代理模型辅助人工神经网络 (KS-ANN) 显著提高了预测准确性,并优化了材料设计.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 准确预测透式水泥稳定基材 (PCBM) 的性能至关重要,但由于稀缺的测试数据而受到限制.
- 现有的方法难以可靠地预测机械强度和透性,实验结果不足.
研究的目的:
- 用有限的数据开发一个可靠的框架来预测PCBM机械强度和透性.
- 提高PCBM性能评估和设计优化预测模型的准确性.
主要方法:
- 开发了一个基于战争的代理模型辅助人工神经网络 (KS-ANN) 框架.
- 使用统计分布模型,马尔科夫链蒙特卡罗模拟和基于战争的替代模型来丰富有限的测试数据.
- 在丰富的数据集上训练了一个人工神经网络 (ANN),用于财产预测.
主要成果:
- 该KS-ANN模型准确地捕获数据分布,并有效地预测PCBM的机械强度和透性.
- 与传统的ANN模型相比,机械强度预测准确度提高了21%.
- 开发了一个优化函数,用于确定PCBM的最佳水泥含量和压缩力.
结论:
- KS-ANN框架为PCBM性能评估和设计提供了成本效益高,快速的解决方案.
- 这项研究证明了将机器学习与有限数据的材料的统计建模相结合的有效性.
- 优化的设计参数使PCBM能够同时满足机械强度和透性要求.
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