使用中央复合材料设计和神经网络对多废弃物泡地质聚合物的建模和优化
Ammar Noui1, Ahmed Abderraouf Belkadi1, Amirouche Berkouche1,2
1LIMEEDD Laboratory, Department of Civil Engineering, Mohamed El-Bachir El-Ibrahimi University of Bordj Bou Arreridj, El-Anasser, 34030, Algeria.
Scientific reports
|November 17, 2025
概括
这项研究使用回收混凝土砂,玻璃粉和枣树纤维开发了可持续的地质聚合物复合材料. 一个人工神经网络模型优化了配方,提高了压力强度和绝热性能,创造了生态高效的建筑材料.
科学领域:
- 材料科学 材料科学 材料科学
- 可持续建筑 可持续建筑
- 废弃物价值化 废弃物价值化 废弃物价值化
背景情况:
- 越来越多的环境和能源效率问题需要可持续的建筑材料.
- 发泡地质聚合物复合材料具有改善机械和热性能的潜力.
- 使用回收混凝土砂 (RCS),玻璃粉 (GP) 和枣树纤维 (DPF) 解决了废物管理和资源效率问题.
研究的目的:
- 设计和优化含有RCS,GP和DPF的泡地质聚合物复合材料.
- 为了提高复合材料的压力强度和隔热性能.
- 为了比较中央复合设计 (CCD) 和人工神经网络 (ANN) 模型的预测和优化能力.
主要方法:
- 开发出具有不同比例RCS,GP和DPF的泡地质聚合物复合材料.
- 对CCD和ANN建模策略进行比较分析,以预测复合性能.
- 使用训练和测试的数据,对开发的模型进行实验验证.
主要成果:
- 人工神经网络 (ANN) 模型表现出比中央复合设计 (CCD) 模型更高的预测准确度,由更高的R2值和0.86.8的可取性指数证明.
- 对ANN优化的配方 (35.4%RCS,4.96%GP,0.23%DPF) 实现了4.41MPa的压力强度和0.141W/mK的热导率.
- ANN提供了增强的预测能力,尽管其解释性需要进一步调查.
结论:
- 包含建筑和农业废物的泡地质聚合物复合材料显示出对生态效率高的建筑材料的巨大潜力.
- ANN建模是优化可持续复合材料性能的一种强大工具.
- 对ANN可解释性的进一步研究可以增强其在材料设计中的应用.
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