基于机器学习的预测巴西混凝土的压力强度:双数据集研究
Vitor Pereira Silva1, Ruan de Alencar Carvalho1, João Henrique da Silva Rêgo1
1Department of Civil and Environmental Engineering, SG-12, University of Brasília (UnB), Brasilia 70910-900, Brazil.
Materials (Basel, Switzerland)
|July 29, 2023
概括
机器学习 (ML) 使用人工神经网络 (ANN) 准确预测混凝土的强度. 结合巴西和国际数据,显著提高了混凝土压力强度的预测模型准确性.
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:
- 机器学习 (ML) 提供了基于组件特性预测混合物特性的高效方法.
- 在土木工程中,ML技术越来越多地用于混凝土混合物设计.
- 通过国家数据集验证ML的适用性对于更广泛的采用至关重要.
研究的目的:
- 评估人工神经网络 (ANN) 在预测巴西混凝土压力强度方面的有效性.
- 使用国家 (巴西) 和国际 (Yeh) 数据集来比较ANN性能.
- 确定最佳的数据组合,以预测坚固的混凝土强度.
主要方法:
- 使用了一个人工神经网络 (ANN) 模型.
- 输入变量包括水泥,渣,波佐拉纳,水,添加剂,细聚合物,粗聚合物和年龄.
- 响应变量是7天和28天的混凝土压力强度.
- 评估了五种不同的培训/测试场景,包括孤立和组合数据集.
主要成果:
- 仅使用巴西或国际数据的预测模型产生了不满意的结果.
- 将巴西和国际数据集结合为培训和测试数据集,导致R平方值更高.
- 数据集的结合表明,混凝土压力强度的预测模型得到了显著改进.
结论:
- 人工神经网络在预测混凝土的压力强度方面表现有前途,在巴西的背景下.
- 整合国内和国际数据可以提高基于机器学习的具体物业预测的准确性和可靠性.
- 综合数据集对于在土木工程应用中开发强大的预测模型至关重要.
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