人工神经网络,基于回收粗聚合物的压力强度的机器学习建模,基于自压缩混凝土
P Jagadesh1, Afzal Hussain Khan2, B Shanmuga Priya1
1Department of Civil Engineering, Coimbatore Institute of Technology, Coimbatore, Tamil Nadu.
PloS one
|May 13, 2024
概括
人工神经网络 (ANN) 准确预测了自压缩回收粗聚合混凝土 (SCRCAC) 的压力强度. 在602个数据集上训练的ANN模型显示出强大的预测能力,通过实验结果验证.
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
- 计算智能是一种计算智能.
背景情况:
- 自压缩回收粗聚合混凝土 (SCRCAC) 提供了可持续的建筑替代品.
- 准确预测SCRCAC压力强度对于结构完整性和性能至关重要.
- 强度预测的传统方法可能耗时且资源密集.
研究的目的:
- 开发和评估人工神经网络 (ANN) 模型,用于预测SCRCAC的28天压力强度.
- 为了比较不同ANN配置的性能,包括规范化技术和传输函数.
- 通过实验结果和其他机器学习 (ML) 技术来验证ANN预测.
主要方法:
- 收集并处理了602个SCRCAC混合设计数据集.
- 开发了ANN模型,使用七个输入变量:水泥含量,水,聚合物,添加剂.
- 采用了两种规范化技术和三种转移函数,在MATLAB中运行了六种模型.
- 使用k倍交叉验证 (k=7) 并将ANN与随机森林,额外树木,XGBoost和LightGBM进行比较.
主要成果:
- 规范化技术2和TANSING传输函数产生了最好的ANN模型性能.
- 最好的模型在训练中达到0.78的确定系数,在测试中达到0.86的确定系数.
- ANN的预测与实验压力强度值 (39.067 MPa与38.36 MPa) 密切匹配,相关系数为1.
- 在敏感分析中,ANN与其他ML技术相比,显示出更高的预测准确度.
结论:
- ANN模型是预测SCRCAC的压力强度的有效和有效工具.
- 开发的ANN模型可以指导混合设计,减少对广泛实验测试的需求.
- 源自ANN的混合设计表现出与标准设计相比的新鲜和硬化性能,支持可持续的建筑实践.
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