机器学习方法用于预测含有再生青路面的青混凝土的裂耐受性指数 (CTIndex)
Lan Ngoc Nguyen1, Thanh-Hai Le2, Linh Quy Nguyen2
1University of Transport and Communications, Hanoi, Vietnam.
PloS one
|October 26, 2023
概括
机器学习模型可以预测青混凝土的预测.
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
- 数据科学数据科学数据科学
背景情况:
- 裂是青混凝土损坏的一个重要形式.
- 诸如重复加载,材料老化和结构问题等因素会导致裂.
- 该CT指数量化了青混凝土的抗裂性能.
研究的目的:
- 构建和比较机器学习模型,用于预测青混凝土的CT指数.
- 确定用于CT指数预测的最有效的机器学习方法.
- 确定影响CT指数的关键因素.
主要方法:
- 利用了来自交通技术大学的107个CT指数数据样本.
- 开发和评估了三个机器学习模型:随机森林 (RF),K-最近邻居 (KNN) 和多变量自适应回归脊柱 (MARS).
- 使用蒙特卡洛模拟验证模型准确性,评估RMSE,MAE,MAPE和R2等指标.
主要成果:
- 随机森林 (RF) 模型表现出卓越的性能.
- 通过4.75毫米的聚合物含量 (AP4.75) 被确定为CT指数最有影响的因素.
- 其他重要因素包括再生青路面含量 (RAP) 和含量 (BC).
结论:
- 该研究成功地确定了RF作为最佳的AI方法,用于准确的CT指数预测.
- 这项研究有助于快速准确地确定青混凝土的抗裂性能.
- 了解影响因素可以指导材料选择,以提高耐用性.
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