基于机器学习的概率模型和UHPC梁的剪切强度容量的面向设计公式
Kun Yang1, Jiaqi Xu1,2, Xiangyong Ni3
1CNEC Innovation Technology Co., Ltd., Shanghai 201702, China.
Materials (Basel, Switzerland)
|October 29, 2025
概括
工程师现在可以使用机器学习模型预测超高性能混凝土 (UHPC) 梁剪切能力. 这些模型提供了准确的预测和不确定性波段,有助于更安全的结构设计.
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
- 结构工程 结构工程
背景情况:
- 设计超高性能混凝土 (UHPC) 梁用于剪切是复杂的,因为多个设计因素的相互作用.
- 现有的方法可能无法完全捕捉影响剪切能力的协同效应.
研究的目的:
- 为UHPC光束剪切能力开发准确的预测模型.
- 量化与剪切能力预测相关的不确定性.
- 为工程师提供实用的设计工具.
主要方法:
- 编制了一个大型,精心策划的数据库,用于UHPC梁的实验室剪切测试.
- 机器学习模型的开发和验证,以预测剪切能力.
- 制定一个简化的,面向设计的方程.
主要成果:
- 机器学习模型实现了对剪切能力的准确点预测.
- 最好的模型提供了95%的预测频段,大约95%的测试结果都在这个范围内.
- 为了实现电子表格,开发了一个用户友好的设计公式.
结论:
- 开发的机器学习模型和设计公式提高了UHPC光束剪切能力的预测.
- 这些工具为工程师提供了提高选选项的能力,检查设计的不确定性边缘,并选择保守的值.
- 透明和可实施的方法支持初步尺寸,验证和评估UHPC成员与共同代码变量保持一致.
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