Katatchambo Atchadeou Yranawa, Bingöl Şinasi
Department of Civil Engineering, Tokat Gaziosmanpaşa University, Tokat, Turkey.
Sci Rep. 2025 Apr 4;15(1):11652. doi: 10.1038/s41598-025-96772-3.
This study investigated the predictability of the compressive strength (CS) of geopolymeric mortars based on blast furnace slag (BFS) and steel mill slag (SMS). For this purpose, the study consists of two parts. In the first part of the study, BFS and SMS, two different types of slag were used as binders in 11 different proportions. At the end of the curing period, the weight, ultrasonic pulse velocity (UPV) and compressive strength of the mortars were determined. In the second part of the study, the compressive strength was predicted using regression analysis (CRA), multivariate adaptive regression spline (MARS), random forest (RF), multiple additive regression trees (TreeNet) and artificial neural networks (ANN). The model performance of the methods was compared using root mean square error (RMSE), mean absolute error (MAE) and Nash-Sutcliffe efficiency (NSE) performance statistics. When comparing the performance of the developed prediction models, the power function method was found to produce the best predictions among the regression-based methods. For the MARS, TreeNet and RF models, the TreeNet model produced the best prediction, while for the ANN_5 and ANN_10 models, the ANN_5 model produced the best prediction. In general, it can be concluded that the models developed with ANN can predict the compressive strength of mortars with a very high accuracy. Significant economic and time savings can be achieved with the developed models. In addition, the CS values of geopolymeric mortars prepared with different proportions of slag types and activator can be predicted without waiting for 7-28 days of curing.
本研究调查了基于高炉矿渣（BFS）和钢厂矿渣（SMS）的地质聚合物砂浆抗压强度（CS）的可预测性。为此，该研究分为两个部分。在研究的第一部分，将两种不同类型的矿渣BFS和SMS以11种不同比例用作粘结剂。养护期结束时，测定了砂浆的重量、超声脉冲速度（UPV）和抗压强度。在研究的第二部分，使用回归分析（CRA）、多元自适应回归样条（MARS）、随机森林（RF）、多元加法回归树（TreeNet）和人工神经网络（ANN）预测抗压强度。使用均方根误差（RMSE）、平均绝对误差（MAE）和纳什-萨特克利夫效率（NSE）性能统计量比较了这些方法的模型性能。在比较所开发预测模型的性能时，发现幂函数法在基于回归的方法中产生的预测效果最佳。对于MARS、TreeNet和RF模型，TreeNet模型产生的预测效果最佳，而对于ANN_5和ANN_10模型，ANN_5模型产生的预测效果最佳。总体而言，可以得出结论，用人工神经网络开发的模型能够非常准确地预测砂浆的抗压强度。所开发的模型可以显著节省经济成本和时间。此外，无需等待7至28天的养护期，就可以预测用不同比例的矿渣类型和活化剂制备的地质聚合物砂浆的CS值。
