决策树模型用于估计地质聚合物混凝土的压力强度
Ji Zhou1, Zhanlin Su2, Shahab Hosseini3
1College of Civil and Environmental Engineering, Hunan University of Science and Engineering, Yongzhou 425199, China.
Mathematical biosciences and engineering : MBE
|February 2, 2024
概括
预测地质聚合物混凝土 (CSGPoC) 的压力强度对于可持续建筑至关重要. 与决策树和随机森林模型相比,极端梯度增强 (XGBoost) 机器学习模型在预测CSGPoC方面表现出更高的准确性.
科学领域:
- 材料科学:专注于新型建筑材料的开发和应用.
- 土木工程:研究混凝土复合材料的结构性质和性能.
- 数据科学:利用机器学习用于材料科学应用中的预测建模.
背景情况:
- 绿色混凝土使用地质聚合物凝作为水泥替代品,为环境带来好处.
- 精确预测地质聚合物混凝土 (CSGPoC) 的压力强度是必不可少的,但由于复杂的材料相互作用而具有挑战性.
- 目前用于CSGPoC测量的方法是劳动密集型和昂贵的,需要预测建模方法.
研究的目的:
- 开发和评估用于准确预测CSGPoC的机器学习模型.
- 为了比较基础学习者 (决策树) 和超级学习者模型 (随机森林,XGBoost) 的预测性能.
- 通过使用各种输入参数来确定最有效的机器学习模型来估计CSGPoC.
主要方法:
- 收集了259个CSGPoC样本的数据集,其中80%用于培训,20%用于测试.
- 输入变量包括飞灰,地面颗粒高炉渣 (GGBS),化学添加剂 (Na2SiO3,NaOH),聚合物和混合比率.
- 机器学习模型,包括决策树 (DT),随机森林 (RF) 和极端梯度增强 (XGBoost) 被实施并使用12个性能指标进行评估.
主要成果:
- 在预测CSGPoC方面,XGBoost模型取得了最高的准确性,这在训练和测试数据集上的卓越性能指标中得到了证明.
- XGBoost的关键性能指标包括高的R平方 (R2) 0.986和低的平均绝对百分比误差 (MAPE) 6.553在测试集.
- 对比分析显示,XGBoost显著优于DT和RF,在测试组中达到0.9857的R2,而DT为0.8969和RF为0.9424.
结论:
- XGBoost模型对于预测地质聚合物混凝土 (CSGPoC) 的压力强度非常有效和准确.
- 这种预测能力可以减少对广泛实验测试的需求,节省绿色混凝土开发的时间和资源.
- 该研究强调了先进的机器学习技术在优化可持续建筑材料的制定和应用方面的潜力.
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