深度LSTM和机器学习模型的比较,用于预测飞灰/砂基地质聚合物混凝土的压力强度
Ceren Kina1, Harun Tanyildizi2, Mohd Mustafa Al Bakri Abdullah3,4
1Faculty of Engineering and Natural Sciences, Department of Civil Engineering, Malatya Turgut Ozal University, Malatya, Türkiye.
Scientific reports
|September 25, 2025
概括
深度学习的长短期记忆 (LSTM) 模型准确地预测了地聚合物混凝土的压力强度,优于机器学习算法. 这加快了环保混凝土开发,减少了对飞灰/地面颗粒高炉渣混凝土的实验测试.
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.
- 人工智能的人工智能
背景情况:
- 使用飞灰 (FA) 和地面颗粒高炉渣 (GGBFS) 生产地质聚合物混凝土 (GPC) 通过减少二氧化碳足迹和废物,为环境带来好处.
- 实验性确定GPC压力强度 (fc) 是资源密集型;人工智能模型可以提供更快的替代方案.
- 基于FA/GGBFS的GPC是一种可持续的建筑材料,但预测其性能需要有效的方法.
研究的目的:
- 为了比较深度长短期记忆 (LSTM) 模型与各种机器学习 (ML) 算法对地质聚合物混凝土压力强度的预测性能.
- 确定影响FA/GGBFS基GPC压力强度的最有影响力的输入参数.
- 评估不同的AI模型在估计GPC fc.时的准确性和可靠性.
主要方法:
- 收集了以前对FA/GGBFS基GPC研究的数据,包括FA,GGBFS,聚合物和其他混合物设计参数的化学成分和数量.
- 深度LSTM和ML算法包括人工神经网络 (ANN),引导集成 (Bagging),最小平方增强 (LSBoost) 和K-最近邻居 (kNN) 被用于预测.
- 进行了灵敏度分析,以确定输入变量对GPC压力强度的影响.
主要成果:
- 长短期记忆 (LSTM) 模型实现了最高的预测准确度,R平方值为0.98.
- 人工神经网络 (ANN) 和最小平方提升 (LSBoost) 也表现出强的表现,分别为0.95和0.94的R平方值.
- 细聚合物含量被确定为影响GPC压力强度的最有影响力的参数,而K-Nearest-Neighbours (kNN) 表示预测能力较差.
结论:
- 深度LSTM模型在预测基于FA/GGBFS的地质聚合物混凝土的压力强度方面明显优于传统的ML算法.
- 开发的AI模型,特别是LSTM,提供了一种可靠和高效的方法来估计GPC fc,减少了对广泛实验测试的需求.
- 使用人工智能准确预测GPC属性,有助于优化可持续混凝土混合设计,并促进更广泛地采用环保建筑材料.
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