Geopolymer Concrete Compressive Strength Data Set for ML

this data set was prepared as part of a scientific paper conducted on the prediction of geopolymer concrete compressive strength using NNs. the data includes various input features such as fly ash amount, NaOH amount, GGBFS amount, recycled aggregate amount and ... against compressive strength. the data was gathered through experimental means in the laboratory with same ingredient, equipment and operators thus may include high bias against more variant data sets. this data set can be used in AI and Machine Learning models to further study the properties of geopolymer concrete.

本数据集为一项依托神经网络（Neural Networks, NNs）开展地质聚合物混凝土（geopolymer concrete）抗压强度预测研究的科学论文所制备。数据集包含粉煤灰掺量、氢氧化钠（NaOH）掺量、粒化高炉矿渣粉（GGBFS，Ground Granulated Blast Furnace Slag）掺量、再生骨料掺量等多项输入特征，以及各特征对应的抗压强度数据。该数据集通过实验室实验采集得到，实验全程采用统一的原料、设备与操作人员，因此相较于样本分布更具多样性的数据集，本数据集存在较高的偏差。本数据集可应用于人工智能（AI）与机器学习（Machine Learning）模型，用于进一步探究地质聚合物混凝土的性能特性。

搜集汇总

背景与挑战

背景概述

该数据集用于预测地质聚合物混凝土的抗压强度，包含飞灰量、NaOH量等实验输入特征，数据来源于实验室控制条件下的实验，可能具有偏差，适用于AI和机器学习模型的研究和应用。