基于深度学习的环境友好的地质聚合物混凝土压力强度的预测
1Department of Civil Engineering, Firat University, Elazig, Turkey. htanyildizi@firat.edu.tr.
概括
这项研究使用深度学习预测了地质聚合物混凝土的压力强度. 深度长期短期记忆 (LSTM) 网络实现了99.23%的准确性,优于其他环境友好型混凝土开发模型.
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.
- 环境科学 环境科学
背景情况:
- 水泥生产是温室气体排放的主要来源,有助于全球变暖.
- 地质聚合物混凝土由飞灰和渣等废物合成,提供了一个环保的替代方案,减少能源消耗和排放.
- 准确预测地质聚合物混凝土的特性对于其更广泛的采用至关重要.
研究的目的:
- 使用先进的机器学习模型预测环保地质聚合物混凝土的压力强度 (fc).
- 为了比较一个深度长短期内存 (LSTM) 网络与支持向量回归 (SVR),最小平方增强 (LSBoost) 和多重线性回归 (MLR) 模型的预测性能.
- 通过灵敏度分析识别影响地质聚合物混凝土压力强度的关键输入变量.
主要方法:
- 开发并比较了四种预测模型:深度LSTM,SVR,LSBoost和MLR.
- 使用的输入变量包括摩尔比率,性溶液度,固化温度,固化日和液体与飞灰质量比率.
- 根据预测的压力强度 (fc) 评估模型准确性,并进行灵敏度分析以确定变量重要性.
主要成果:
- 深度LSTM模型实现了最高的预测准确率99.23%.
- LSBoost,MLR和SVR模型的精度分别较低,分别为98.08%,88.03%和78.57%.
- 灵敏度分析显示,固化温度是影响地质聚合物混凝土压力强度的最重要因素.
结论:
- 深度LSTM是一种非常准确和有效的模型,用于预测环保地质聚合物混凝土的压力强度.
- 这些发现支持将地质聚合物混凝土作为可持续建筑材料的使用.
- 优化固化温度对于提高地质聚合物混凝土性能至关重要.
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