使用深度学习和多目标优化优化可持续混合混凝土混合物
Rupesh Kumar Tipu1, Preeti Rathi2, Kartik S Pandya3
1Department of Civil Engineering, School of Engineering & Technology, K. R. Mangalam University, Sohna, Gurugram, 122103, Haryana, India. rupesh.kumar@krmangalam.edu.in.
Scientific reports
|May 10, 2025
概括
这项研究引入了一个新的框架,将深度神经网络和多目标优化结合起来,用于可持续的混凝土混合设计. 它实现了显著的成本和水泥降低,同时提高了压力强度,为绿色建筑提供了实际的解决方案.
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.
- 计算科学 计算科学
背景情况:
- 开发可持续混凝土对于减少建筑对环境的影响至关重要.
- 优化混凝土混合设计涉及平衡竞争目标,如强度,成本和环境足迹.
- 传统方法往往难以有效地探索混合设计中的复杂权衡.
研究的目的:
- 开发一个集成的数据驱动框架,用于设计环保的混凝土混合物.
- 利用深度神经网络 (DNN) 和多目标优化来优化混凝土的性能.
- 为从业者提供决策支持工具,以寻求具有成本效益和可持续的具体解决方案.
主要方法:
- 一个深度神经网络 (DNN) 模型被训练在一个全面的数据集的混凝土混合参数和固化条件.
- 贝叶斯超参数调整用于优化DNN配置,实现高预测精度 (R2=0.936,RMSE=5.71 MPa).
- 多目标粒子群集优化 (MOPSO) 算法被用来确定最佳的混合设计,平衡强度,成本和水泥减少.
主要成果:
- 优化混凝土混合物实现了超过50 MPa的压力强度,降低了高达25%的水泥.
- 与标准混凝土混合物相比,总成本降低了15%.
- 特性重要性分析确定了水泥含量和混凝土年龄是影响压力强度的关键因素.
结论:
- 拟议的框架有效地整合了DNN和MOPSO,用于设计高性能,可持续的混凝土.
- 数据驱动的方法为绿色混凝土技术提供了有价值的见解,并促进了建筑中的实际应用.
- 这种方法可以发现最佳的混合比例,以提高强度,降低成本并最大限度地减少对环境的影响.
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