通过结合物理实验和混合组合人工智能算法来预测含有内部裂的可持续水泥砂的气体透性
Zhiming Chao1,2, Chuanxin Yang1, Wenbing Zhang1
1College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai 200135, China.
Materials (Basel, Switzerland)
|August 12, 2023
概括
在破裂的水泥砂中预测气体透性是一项挑战. 一个新的机器学习模型,思维进化算法调整的自适增强算法-反向传播人工神经网络 (MEA-ABA-BPANN),准确地估计受裂大小和数量影响的透性.
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
- 计算智能是一种计算智能.
背景情况:
- 内部裂显著影响可持续水泥砂的气体透性,影响结构完整性.
- 由于许多变量和复杂的机制,预测这种透性是复杂的.
研究的目的:
- 开发一种创新的机器学习算法,用于预测破裂可持续水泥砂的气体透性.
- 建立一个分析公式,用于气体透性预测,可供非专家使用.
主要方法:
- 一个整体机器学习模型,将思维进化算法 (MEA) 与自适应增强算法-反向传播人工神经网络 (ABA-BPANN) 集成在一起.
- 与传统模型相比进行比较分析,例如粒子集群优化 (PSO),遗传算法 (GA) 和极端学习机器 (ELM).
- 利用了1452次气体透性测试的数据.
主要成果:
- 与其他模型相比,MEA调整的ABA-BPANN模型在预测气体透性方面表现出更高的准确性.
- 灵敏度分析表明裂尺寸和数量比应力条件更有影响.
- 从模型中衍生出的分析公式有助于直接预测透性.
结论:
- 优化了MEA的ABA-BPANN算法提供了一种强大而准确的方法来估计破裂的可持续水泥砂中的气体透性.
- 衍生式为工程师和研究人员提供了一个实用的工具.
- 这些发现为设计耐用水泥结构提供了关键的见解.
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