评估含有废玻璃粉的可持续混凝土的综合建模方法:有限元素和深度学习视角
Megha Gupta1, Ishwor Thapa2, Sufyan Ghani2
1Department of Civil Engineering, Sharda University, Greater Noida, India. megha.gupta@sharda.ac.in.
概括
本研究引入了有限元法 (FEM) 和深度学习 (DL) 模型,以评估使用废玻璃粉 (WGP) 的混凝土. 研究证实,用WGP取代10%的水泥可以优化可持续建筑的压力强度.
科学领域:
- 材料科学 材料科学 材料科学
- 土木工程 土木工程是指土木工程.
- 可持续建筑 可持续建筑
背景情况:
- 建筑中对可持续实践的日益增长的需求需要替代材料.
- 废玻璃粉 (WGP) 成为混凝土中传统水泥材料的有希望的环保替代品.
研究的目的:
- 开发和验证有限元法 (FEM) 和深度学习 (DL) 模型,用于评估结合WGP的混凝土的结构和机械性能.
- 研究WGP比例对混凝土的压力强度和微观结构行为的影响.
- 优化混凝土混合设计,以提高机械性能和可持续性.
主要方法:
- 描述WGP物理属性的特性.
- 开发一个全面的FEM模型来模拟WGP修改混凝土在各种负载下.
- 整合DL模型,特别是卷积神经网络 (CNN),用于预测压力强度.
- 对FEM和DL模型结果与实验数据的验证.
主要成果:
- 通过用WGP取代10%的水泥,可以达到最佳的压力强度,FEA和实验室实验证实了这一点.
- 在CNN模型展示了高可靠性,预测压力强度超过92%的准确性在训练和测试期间.
- FEM模拟提供了关于WGP混凝土的变形,应力分布和故障机制的见解.
结论:
- 废玻璃粉 (WGP) 是生产环保混凝土的可行材料,有助于可持续建筑.
- 开发的FEM和DL模型有效地预测了WGP修改混凝土的性能,有助于混合设计的优化.
- 调查结果支持建筑行业越来越多地使用WGP来减少对环境的影响.
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