通过机器学习阐明含有飞和纳米二氧化的水泥材料的质性质
Ankang Tian1, Yue Gu1, Zhenhua Wei2
1College of Civil and Transportation Engineering, Hohai University, Nanjing 211100, China.
Nanomaterials (Basel, Switzerland)
|November 8, 2024
概括
机器学习可以准确地预测水泥材料的粘度. 一个循环神经网络 (RNN) 模型,特别是堆叠的LSTM,在预测可持续建筑材料的质特性方面表现出卓越的表现.
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.
- 计算科学 计算科学
背景情况:
- 混凝土的风学性质对于提高机械性能和材料可持续性至关重要.
- 由于许多影响因素,预测这些属性具有挑战性,需要先进的计算方法.
- 机器学习为大数据的背景下预测建筑材料特性提供了强大的解决方案.
研究的目的:
- 开发用于结合飞灰和纳米二氧化的水泥材料的学行为的预测模型.
- 评估各种机器学习算法在预测剪切应力和明显粘度曲线方面的有效性.
- 确定最准确的模型,可靠地预测水泥材料粘度.
主要方法:
- 使用了四种机器学习模型:随机森林,XGBoost,人工神经网络 (ANN) 和具有堆叠长短期记忆 (LSTM) 层的循环神经网络 (RNN).
- 模型经过训练和验证,以预测剪切速率与剪切应力以及剪切速率与明显粘度关系.
- 进行了超参数调整以优化模型性能.
主要成果:
- 该RNN (堆叠LSTM) 模型表现出优异的预测准确性,无论是剪速与剪应力 (R2 = 0.9582) 和剪速与表面粘度 (R2 = 0.9257) 曲线.
- 堆叠LSTM模型显示出出色的统计参数和合适效果.
- 对比分析表明,RNN (堆叠LSTM) 与其他模型相比具有更好的概括能力.
结论:
- RNN (堆叠LSTM) 模型对于预测含有飞灰和纳米二氧化的水泥材料的质性质非常有效.
- 这种先进的机器学习方法为未来预测水泥材料粘度提供了可靠的工具.
- 这些发现有助于通过精确的风学表征来开发更可持续和高性能混凝土.
相关概念视频
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01:21Pozzolans
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01:24Hydration of Cement
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