使用可解释的方法在混凝土中使用飞灰/泥来估计废混凝土的压力强度:优化和图形用户界面 (GUI)
Yakubu Dodo1, Kiran Arif2, Mana Alyami3
1Architectural Engineering Department, College of Engineering, Najran University, Najran, Kingdom of Saudi Arabia.
Scientific reports
|February 26, 2024
概括
这项研究使用机器学习来预测飞灰/渣土基地质聚合物混凝土 (FASBGPC) 的机械性能. 集成模型,特别是使用AdaBoost的ANN,可以准确预测压力强度,识别改善混凝土设计的关键影响因素.
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:
- 地质聚合物混凝土通过减少土木工程应用中的二氧化碳 (CO2) 排放,为环境带来好处.
- 地质聚合物混凝土的混合设计和现场应用存在挑战,需要先进的预测工具.
- 飞灰/渣土基地质聚合物混凝土 (FASBGPC) 是一种可持续的替代品,但其机械性能需要精确的预测.
研究的目的:
- 开发和验证用于预测FASBGPC机械特性的机器学习算法.
- 确定影响FASBGPC压力强度的最有影响力的参数.
- 创建一个用户友好的图形用户界面 (GUI),以便在土木工程中实际应用预测模型.
主要方法:
- 利用基于人工的机器学习算法,特别是多层感知神经网络 (MLPNN) 上的AdaBoost和Bagging,以创建组合模型.
- 训练模型使用156个数据点的数据集,包括GGBS,性激活剂,飞灰,SP剂量,NaOH度,聚合物和温度.
- 采用统计验证技术,包括80/20数据分割,K折交叉验证 (CV),平均绝对误差 (MAE),根平均平方误差 (RMSE) 和R平方 (R2) 进行准确性评估.
主要成果:
- 使用人工神经网络 (ANN) 和AdaBoost组合模型表现出卓越的性能,达到0.914的R2值和最小的误差.
- 转换重要性分析确定了GGBS含量,NaOH度和温度是影响FASBGPC压力强度的最重要的因素.
- 沙普利的分析证实了GGBS,NaOH度和温度对FASBGPC的机械性质的重大影响.
结论:
- 集成机器学习方法,特别是使用AdaBoost的ANN,非常适合为FASBGPC构建强大可靠的预测模型.
- 开发的预测模型及其GUI为优化FASBGPC混合设计和土木工程中的现场应用提供了宝贵的工具.
- 精确预测机械性能有助于在建筑中更广泛地采用可持续的地质聚合物混凝土.
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