基于数据的框架,用于预测废玻璃聚合物混凝土的机械性能
Kennedy C Onyelowe1,2, Shadi Hanandeh3, Viroon Kamchoom4
1Department of Civil Engineering, College of Eng & Eng Technology, Michael Okpara University of Agriculture, Umudike, Nigeria. kennedychibuzor@kiu.ac.ug.
Scientific reports
|July 2, 2025
概括
这项研究引入了一个新的AI框架,用于使用废玻璃预测混凝土的性能. 灰狼优化器 (Wolf) 算法在预测可持续混凝土设计的机械性能方面表现出卓越的准确性.
科学领域:
- 材料科学与工程 材料科学与工程
- 土木工程 土木工程是指土木工程.
- 人工智能和机器学习
背景情况:
- 可持续建筑材料对于减少对环境的影响至关重要.
- 废玻璃聚合物为传统混凝土组件提供了可行的替代品.
- 预测新型混凝土混合物的机械性能对于结构完整性至关重要.
研究的目的:
- 开发和评估一个数据驱动的框架,用于预测废玻璃聚合物混凝土的机械性能.
- 为了比较六种先进的元启发优化算法的性能,预测压力强度,拉力强度,密度和缩.
- 用灵敏度分析确定影响混凝土性能最有影响力的输入参数.
主要方法:
- 使用了六个元启发算法:蝙蝠算法,子搜索算法,大象群群优化,火虫算法,犀牛优化算法和灰狼优化器.
- 通过使用统计指标评估模型性能:SSE,MAE,MSE,RMSE,准确性,R2和KGE.
- 使用霍夫曼和园丁的方法和SHAP技术进行了敏感性分析,以确定特征的重要性.
主要成果:
- 火和灰狼优化器 (狼) 算法实现了所有机械性质的最高预测准确度.
- 沃尔夫算法表现出卓越的概括能力,较低的错误率和与实验结果的高相关性,使其成为表现最佳的模型.
- 水与粘合剂的比率被确定为影响废玻璃聚合物混凝土机械性能最有影响的参数.
结论:
- 拟议的AI框架,特别是使用Wolf算法,有效地预测废玻璃聚合混凝土的机械性能.
- 这些发现强调了水与粘合剂比率在优化可持续混凝土混合设计方面发挥的关键作用.
- 这项研究支持将人工智能和循环经济原则整合到建筑中,以实现具有成本效益和环保的材料开发.
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