使用基因表达和人工智能编程,预测环保塑料砂块的压力强度
Bawar Iftikhar1,2, Sophia C Alih3, Mohammadreza Vafaei1
1School of Civil Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia.
Scientific reports
|July 27, 2023
概括
这项研究开发了模型来预测塑料砂块 (PSPB) 的压力强度. 多表达式编程 (MEP) 显示出卓越的准确性,将沙粒大小和纤维含量作为关键因素.
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:
- 塑料砂块 (PSPB) 提供了一个可持续的建筑选择,利用塑料废物并减少对水泥的依赖.
- 现有的模型缺乏准确预测这些环保块的压力强度的能力.
- 推进可持续建筑需要准确的预测模型,用于像PSPB这样的新材料.
研究的目的:
- 开发和比较实证模型来预测塑料砂块 (PSPB) 的压力强度.
- 用先进的计算方法确定影响PSPB压力强度的关键参数.
- 为优化PSPB组成提供预测工具,以提高结构性绩效.
主要方法:
- 利用基因表达编程 (GEP) 和多表达编程 (MEP) 来创建预测模型.
- 使用135个压力强度的数据集开发的模型具有7个输入参数的结果.
- 进行了灵敏度分析,以确定输入变量对压力强度的影响.
主要成果:
- 与GEP模型 (R2 = 0.87) 相比,MEP模型在预测压力强度方面表现出更高的准确性 (R2 = 0.91).
- 灵敏度分析确定了砂粒大小和纤维百分比作为关键因素,它们共同对压力强度贡献了大约50%.
- GEP和MEP都显示了预测和实际压力强度值之间的显著关系.
结论:
- MEP是开发PSPB压力强度的准确预测模型的高效方法.
- 优化砂粒大小和纤维含量对于最大限度地提高塑料砂块的结构完整性至关重要.
- 这些发现支持PSPB在绿色建筑倡议中的更广泛采用,促进环境保护和经济效益.
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