人工蜂群优化随机森林模型,用于预测飞灰混凝土压力强度
Manish Bali1, Ved Prakash Mishra1, Anuradha Yenkikar1,2
1School of Engineering, Amity University Dubai Campus, Dubai, 25314, UAE.
MethodsX
|June 23, 2025
概括
预测混凝土的压力强度 (CS) 现在通过新的计算模型更快. 这种人工蜂群优化的随机森林模型准确地预测飞灰混凝土 (FAC) 的CS.
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
- 计算建模 计算建模
背景情况:
- 传统的混凝土压力强度 (CS) 评估是耗时和破坏性的.
- 飞灰混凝土 (FAC) 提供可持续建筑的好处,但需要准确的强度预测.
- 优化混凝土混合设计对于结构完整性和性能至关重要.
研究的目的:
- 开发一个数据驱动的计算模型来预测FAC的CS.
- 为了提高随机森林 (RF) 算法的预测准确度,使用人工蜂群 (ABC) 优化.
- 确定最佳的飞灰替换水平,以最大限度地提高混凝土的压力强度.
主要方法:
- 使用了168个FAC混合设计的实验数据集,其中具有不同的飞灰,钢纤维和水与粘合剂比率.
- 训练和评估了五种回归模型,使用R平方 (R2),根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 等指标.
- 射频模型的超参数使用ABC算法进行了优化,以创建混合ABC-RF模型.
主要成果:
- 拟议的ABC-RF模型实现了0.95的高R2,优于其他回归和深度学习模型.
- 该模型发现,大约30%的飞灰替代结果是最佳的CS.
- 扫描电子显微镜 (SEM) 分析证实在最佳飞灰含量下形成密集的C-S-H矩阵.
结论:
- 混合ABC-RF模型为预测FAC压力强度提供了准确和有效的方法.
- 生物灵感优化技术对于建模土木工程材料非常有效.
- 这种方法通过优化FAC利用来支持可持续的建筑实践.
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