机器学习预测高强度RCA混凝土使用化学活性飞灰和纳米
Muhammad Adil Khan1, Muhammad Shoaib Ashraf1, Kennedy C Onyelowe2,3
1Civil Engineering Department, The University of Faisalabad, Faisalabad, Punjab, Pakistan.
Scientific reports
|March 26, 2025
概括
回收混凝土聚合物 (RCA) 与补充混凝土材料 (SCM) 创造了可持续的,高强度的混凝土. 机器学习准确地预测性能,增强耐用建筑材料.
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
- 可持续建筑 可持续建筑
背景情况:
- 回收混凝土聚合物 (RCA) 与天然聚合物相比,往往具有较低的机械性能和耐久性.
- 补充性水泥材料 (SCM) 对于提高使用RCA的混凝土性能至关重要.
- 开发可持续建筑材料是减少环境影响的全球优先事项.
研究的目的:
- 研究将RCA与纳米二氧化和化学活性飞灰 (SCM) 结合起来,以制造高强度,耐久的混凝土的有效性.
- 在具体应用中解决和克服RCA固有的局限性.
- 使用机器学习开发RCA混凝土性能准确的预测模型.
主要方法:
- 一个全面的实验计划,包括生产和测试420个样本的压力强度和240个样本的抗酸性.
- 纳米二氧化和化学活性飞灰作为SCM与RCA的结合.
- 机器学习算法的应用,包括决策树,随机森林,XG-Boost和Ada Boost用于性能预测.
主要成果:
- 在RCA混凝土的机械性能和耐用性方面,在加入SCM后,观察到显著的改善.
- 对于压力强度,XG-Boost表现出最高的预测精度 (R2 = 0.995).
- 随机森林在抗酸性方面取得了更高的预测准确度 (R2 = 0.909).
结论:
- SCM有效地提高了使用RCA制成的混凝土的机械性能和耐久性.
- 机器学习模型为预测可持续混凝土混合物的性能提供了可靠的框架.
- 该研究有助于通过优化RCA利用来推进可持续和弹性建筑材料的开发.
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