使用MEP和GEP预测纤维增强自压混凝土 (FR-SCC) 的28天压力强度
Waleed Bin Inqiad1, Muhammad Shahid Siddique1, Mujahid Ali2
1Military College of Engineering (MCE), National University of Science and Technology (NUST), Islamabad, 44000, Pakistan.
Scientific reports
|July 27, 2024
概括
基因表达编程 (GEP) 和多表达编程 (MEP) 用于预测纤维增强自压混凝土 (FR-SCC) 的压力强度. GEP提供了一个更简单的方程,误差更低,使其适合实际应用.
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.
- 计算智能是一种计算智能.
背景情况:
- 自压缩混凝土 (SCC) 与普通混凝土相比,具有流动性和隔离性等优势.
- 将纤维纳入SCC增强了诸如柔性,延展能力和抗拉强度等属性.
- 精确预测纤维增强自压混凝土 (FR-SCC) 的强度对于其工业采用至关重要.
研究的目的:
- 为FR-SCC的28天压力强度开发新的预测模型.
- 为了比较基因表达编程 (GEP) 和多表达编程 (MEP) 的性能,以预测强度.
- 为实际工程应用验证开发的模型.
主要方法:
- 从文献中编制了一个包含六个输入参数的数据集 (水与水泥的比率,二氧化烟雾,聚合物,纤维,超塑剂).
- 用基因表达编程 (GEP) 和多表达编程 (MEP) 算法来构建预测模型.
- 模型性能使用误差指标进行评估,例如平均绝对误差 (MAE),a20指数和目标函数 (OF).
主要成果:
- 两种GEP和MEP模型都展示了FR-SCC压力强度的预测能力.
- 与MEP模型 (OF = 0.031) 相比,GEP模型产生了一个更简单的预测方程,误差值较低 (OF = 0.029).
- 敏感性分析和外部验证证实了GEP和MEP模型用于预测FR-SCC强度的可靠性.
结论:
- 基因表达编程 (GEP) 是一种高效的工具,用于预测纤维增强自紧缩混凝土的压力强度.
- 开发的GEP模型为估计FR-SCC强度提供了一个实用和准确的方法,促进其更广泛的工业应用.
- 该研究验证了计算智能技术在优化混凝土混合设计和性能预测方面的潜力.
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