提高机械性能性能子纤维和CDW复合材料在铺路块:多个AI技术与性能分析
G Uday Kiran1, G Nakkeeran2, Dipankar Roy1
1Department of Civil Engineering, Madanapalle Institute of Technology and Science, Madanapalle, Andhra Pradesh, 517325, India.
Scientific reports
|December 31, 2024
概括
这项研究使用人工智能来预测使用建筑和拆除废弃物 (CDW) 和天然纤维的铺路块属性. 渐变增强 (GB) 和人工神经网络 (ANN) 在预测机械特性方面表现出卓越的表现.
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
- 人工智能的人工智能
背景情况:
- 建筑和拆除废物 (CDW) 面临着处置方面的挑战.
- 将CDW等回收材料纳入建筑可以促进可持续性.
- 天然纤维具有提高材料性能的潜力.
研究的目的:
- 优化和预测铺路块的机械性能.
- 评估各种AI方法在预测材料性能方面的有效性.
- 调查CDW和天然纤维的使用,以替代细砂料.
主要方法:
- 响应表面方法 (RSM) 用于优化.
- 机器学习算法包括支持向量机 (SVM),梯度增强 (GB),人工神经网络 (ANN) 和随机森林 (RF) 进行预测.
- 分析了结合水泥,天然细聚合物,CDW和子纤维的铺路块.
主要成果:
- 所有评估的AI方法 (RSM,SVM,GB,ANN,RF) 都表现出强大的预测能力.
- 使用根平均平方误差 (RMSE),平均平方误差 (MSE),平均绝对误差 (MAE) 和相关系数 (R) 来评估性能.
- 与SVM和RF相比,渐变增强 (GB) 和人工神经网络 (ANN) 的预测性能得到了提高.
结论:
- 人工智能方法对于预测CDW和天然纤维铺路块的机械性能是有效的.
- 在可持续建筑中,GB和ANN对准确的物质属性预测特别有希望.
- 这项研究支持在开发环保建筑材料时使用回收的CDW和天然纤维.
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