机器学习模型的性能比较,用于预测混凝土压力强度
Amit Kumar Sah1, Yao-Ming Hong1
1Nanhua University, Chiayi 62248, Taiwan.
Materials (Basel, Switzerland)
|May 11, 2024
概括
机器学习准确地预测了混凝土的压力强度,超过了传统方法. 人工神经网络 (ANN) 模型为建筑应用提供了最有效和最精确的结果.
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
- 计算科学 计算科学
背景情况:
- 传统的混凝土压力强度测试是耗时和复杂的.
- 准确的强度预测对于建筑安全和效率至关重要.
- 机器学习为更快,更可靠的预测提供了潜力.
研究的目的:
- 评估和比较各种机器学习模型的性能,以预测混凝土的压力强度.
- 确定最准确和最有效的模型,用于建筑行业的实际应用.
主要方法:
- 使用了四种机器学习模型:人工神经网络 (ANN),多重线性回归,支持向量机器和回归树.
- 预处理了1030个具体样本的数据集,并将其分为培训 (70%) 和测试 (30%) 集.
- 性能被评估使用指标,如平均绝对偏差,根平均平方误差,相关系数和平均绝对百分比误差. 该ANN模型包括进一步验证 (15%) 和测试 (15%) 的子集.
主要成果:
- 人工神经网络 (ANN) 模型与多重线性回归,支向量机器和回归树相比,表现出更高的性能.
- 在预测混凝土压力强度方面,ANN模型实现了更高的准确性和效率.
- 评估指标证实了ANN的预测能力.
结论:
- 机器学习,特别是ANN模型,为确定混凝土压力强度提供了一种简化和高效的方法.
- 这种方法可以显著减少与传统测试方法相关的时间和复杂性.
- 这些发现支持在建筑行业采用ML模型进行增强材料评估.
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