使用机器学习对活性废料的有效压缩强度预测
Chien-Hua Hsu1, Hao-Yu Chan1, Ming-Hui Chang1
1Material and Chemical Research Laboratories, Industrial Technology Research Institute, Hsinchu 31040, Taiwan.
Materials (Basel, Switzerland)
|July 13, 2024
概括
机器学习 (ML) 模型使用工业废物预测活性材料 (AAM) 的压力强度. 这种方法显著提高了准确性,并加速了AAM设计,减少了传统的试错方法.
科学领域:
- 材料科学 材料科学 材料科学
- 土木工程 土木工程是指土木工程.
- 数据科学数据科学数据科学
背景情况:
- 活性材料 (AAMs) 为传统的水泥材料提供了可持续的替代品.
- 制定AAM通常涉及劳动密集型的试错方法.
- 工业废物流,如高炉渣,飞灰,减少渣和废玻璃,是AAM的可行前体.
研究的目的:
- 整合机器学习 (ML) 来预测和优化AAMs的压力强度.
- 通过减少对实验试错的依赖来简化AAM制定过程.
- 通过使用ML识别影响AAM压力强度的关键特征.
主要方法:
- 开发和验证一个随机森林 (RF) ML模型.
- 使用了一组数据集,其中包括来自四个工业废物流的42个样本.
- 在模型可靠性评估中采用五重交叉验证.
- 执行精细的数据处理以识别有影响力的特征.
主要成果:
- 射频模型在预测准确度方面取得了显著改善,从0.05增加到0.62.
- 实验验证证了ML模型的有效性,实现了所需的强度值.
- 与初始实验相比,压力强度得到了59.65%的改善.
- ML推的配方只需要大约5分钟的设计时间.
结论:
- ML技术,特别是射频,对于预测和优化AAM压力强度非常有效.
- 机器学习集成大大加快了AAM设计和开发过程.
- 该研究表明了ML在可持续建筑材料开发中的变革潜力.
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