机器学习基础模型用于预测后拉伸的UHPC平板板块的穿孔切削能力
Dina M Ors1, Mohamed Ramadan2,3, Ahmed M Farghal Maree3
1Faculty of Engineering and Technology, Future University, New Cairo, Egypt. Dina.mohamed@fue.edu.eg.
Scientific reports
|February 17, 2024
概括
本研究引入了在后应力超高性能混凝土平板板块中穿孔切割的校正因子. 机器学习模型可以准确预测这些因素,从而提高了ACI-318和EC2代码的适用性.
科学领域:
- 土木工程 土木工程是指土木工程.
- 结构工程 结构工程
- 材料科学 材料科学 材料科学
背景情况:
- 穿孔剪切是平板板块的关键故障模式.
- 现有的设计代码 (ACI-318,EC2) 可能无法完全捕捉后应力超高性能混凝土 (PT-UHPC) 板块的行为.
- 准确预测穿孔切削能力对于安全高效的结构设计至关重要.
研究的目的:
- 开发PT-UHPC平板板块的ACI-318和EC2穿孔剪切公式的校正因数.
- 为了提高PT-UHPC平板板冲孔能力的现有设计代码的准确性.
- 利用机器学习技术来预测这些校正因子.
主要方法:
- 通过使用PT-UHPC平板板的实验数据验证了有限元法 (FEM) 模型.
- 使用FEM进行了参数研究,以生成ACI-318和EC2.2的数据库.
- 应用遗传编程 (GP),人工神经网络 (ANN) 和进化多项式回归 (EPR) 来预测校正因子.
主要成果:
- 经过验证的FEM模型准确地预测了穿孔剪切能力.
- 机器学习模型在预测冲孔最终负载时达到约96%的准确性.
- ACI-318的校正因数主要取决于混凝土的压力强度和柱子的面积比.
- EC2校正因子主要受到混凝土的压力强度和剪切增强能力的影响.
结论:
- 开发的ML模型为PT-UHPC平板板的ACI-318和EC2代码提供了准确的校正因数.
- 这些发现提高了这些先进的混凝土结构的穿孔剪切设计的可靠性.
- 该研究强调了影响PT-UHPC平板板的穿孔切削能力的关键参数.
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