Advances in technology and environmental issues allow the building industry to use ever more high-performance engineered materials. In this study, the hardness of concrete mixtures with high-volume fly ash (HVFA) has been evaluated and modeled for the LEED (Leadership for Energy and Environmental Design). High-performance building materials may have greater strength, ductility, external factor resistance, more environmentally sustainable construction, and lower cost than conventional building materials. To overcome the mentioned matter, this study aims to establish systematic multiscale models to predict the compressive strength of concrete mixes containing a high volume of fly ash (HVFA) and to be used by the construction industry with no theoretical restrictions. For that purpose, a wide experimental data (a total of 450 tested HVFA concrete mixes) from different academic research studies have been statically analyzed and modeled. For that purpose, Linear, Nonlinear Regressions, Multi-logistic Regression, M5P-tree, and Artificial Neural Network (ANN) technical approaches were used for the qualifications. In the modeling process, most relevant parameters affecting the strength of concrete, i.e. fly ash (class C and F) incorporation ratio (0–80% of cement's mass), water-to-binder ratio (0.27–0.58), and gravel, sand, cement contents and curing ages (3–365 days). According to the correlation coefficient (R) and the root mean square error, the compressive strength of HVFA concrete can be well predicted in terms of w/b, fly ash, cement, sand, and gravel densities, and curing time using various simulation techniques. Among the used approaches and based on the training data set, the model made based on the ANN, M5P-tree, and Non-linear regression models seem to be the most reliable models. The results of this study suggest that the M5Ptree-based model is performing better than other applied models using training and testing datasets. The maximum and minimum percentage of error between the actual test results and the outcome of the prediction using MLR, LR, M5P-tree, and ANN were 0.03–43%, 0.03–54%, 0.04–33%, and 0.03–41% respectively. Based on the outcomes from the models and statistical assessments such as coefficient of determination (R²), mean absolute error (MAE) and the root mean square error (RMSE), the models M5P-tree, ANN, and MLR respectively were predicted the compressive strength of the HVFA concrete very well with a high value of R² and low values of MAE and RMSE based on the comparison with experimental data. The sensitivity investigation concludes that the curing time is the most dominating parameter for the prediction of the compressive strength of HVFA concrete with this data set.

技术和环境问题的发展使建筑业可以使用更多的高性能工程材料。在这项研究中，已经对高掺量粉煤灰（HVFA）的混凝土混合物的硬度进行了评估，并建立了LEED（能源与环境设计领导力）模型。与常规建筑材料相比，高性能建筑材料可能具有更高的强度，延展性，耐外部因素，更环保的建筑以及更低的成本。为了克服上述问题，本研究旨在建立系统的多尺度模型，以预测包含大量粉煤灰（HVFA）的混凝土混合物的抗压强度，并在建筑业中使用而不受理论限制。为了这个目的，对来自不同学术研究的大量实验数据（总共450种经测试的HVFA混凝土混合物）进行了静态分析和建模。为此，将线性，非线性回归，多元逻辑回归，M5P树和人工神经网络（ANN）技术方法用于资格认定。在建模过程中，影响混凝土强度的最相关参数包括粉煤灰（C和F类）的掺入比（水泥质量的0-80％），水灰比（0.27-0.58）和砾石，沙子，水泥含量和固化时间（3–365天）。根据相关系数（R）和均方根误差，可以使用各种模拟技术从w / b，粉煤灰，水泥，沙子和砾石密度以及固化时间方面很好地预测HVFA混凝土的抗压强度。在所使用的方法中，并基于训练数据集，基于ANN，M5P树和非线性回归模型的模型似乎是最可靠的模型。这项研究的结果表明，基于M5Ptree的模型使用训练和测试数据集的性能优于其他应用模型。实际测试结果与使用MLR，LR，M5P-tree和ANN的预测结果之间的最大和最小误差百分比为0.03–43％，0.03–54％，0.04–33％和0.03–41％分别。基于模型和统计评估的结果，例如确定系数（R 这项研究的结果表明，基于M5Ptree的模型使用训练和测试数据集的性能优于其他应用模型。实际测试结果与使用MLR，LR，M5P-tree和ANN的预测结果之间的最大和最小误差百分比为0.03–43％，0.03–54％，0.04–33％和0.03–41％分别。基于模型和统计评估的结果，例如确定系数（R 这项研究的结果表明，基于M5Ptree的模型使用训练和测试数据集的性能优于其他应用模型。实际测试结果与使用MLR，LR，M5P-tree和ANN的预测结果之间的最大和最小误差百分比为0.03–43％，0.03–54％，0.04–33％和0.03–41％分别。基于模型和统计评估的结果，例如确定系数（R²）分别预测模型M5P-tree，ANN和MLR分别很好地预测了HVFA混凝土的平均绝对误差（MAE）和均方根误差（RMSE），R ²值高而R ²值低根据与实验数据的比较得出MAE和RMSE的值。敏感性研究得出的结论是，固化时间是使用该数据集预测HVFA混凝土抗压强度的最主要参数。