Qadir Warzer Mohammed-Sarwar, Rafiq Al Zahawi Serwan Khurshid, Mohammed Ahmed Salih
Civil Engineering Department, College of Engineering, University of Sulaimani, Kurdistan 46001, Iraq.
Materials (Basel). 2024 Aug 1;17(15):3807. doi: 10.3390/ma17153807.
This study aims to develop systematic multiscale models to accurately predict the compressive strength of cement mortar for tile adhesive applications, specifically tailored for applications in the construction industry. Drawing on data from 200 cement mortar tests conducted in previous studies, various factors such as cement/water ratios, curing times, cement/sand ratios, and chemical compositions were analyzed through static modeling techniques. The model selection involved utilizing various approaches, including linear regression, pure quadratic, interaction, M5P tree, and artificial neural network models to identify the most influential parameters affecting mortar strength. The analysis considered the water/cement ratio, testing ages, cement/sand ratio, and chemical compositions, such as silicon dioxide, calcium dioxide, iron (III) oxide, aluminum oxide, and the pH value. Evaluation metrics, such as the determination coefficient, mean absolute error, root-mean-square error, objective function, scatter index, and a-20 index, were employed to ensure the accuracy of the compressive strength estimates. Additionally, empirical equations were utilized to predict flexural and tensile strengths based on the compressive strength of the cement mortar for tile adhesive applications.
本研究旨在开发系统的多尺度模型，以准确预测用于瓷砖胶粘剂的水泥砂浆的抗压强度，该模型是专门为建筑行业应用量身定制的。借鉴先前研究中进行的200次水泥砂浆试验的数据，通过静态建模技术分析了诸如水泥/水比、养护时间、水泥/砂比和化学成分等各种因素。模型选择涉及使用多种方法，包括线性回归、纯二次、交互、M5P树和人工神经网络模型，以识别影响砂浆强度的最具影响力的参数。分析考虑了水/水泥比、测试龄期、水泥/砂比以及化学成分，如二氧化硅、氧化钙、氧化铁、氧化铝和pH值。采用诸如决定系数、平均绝对误差、均方根误差、目标函数、散射指数和a - 20指数等评估指标来确保抗压强度估计的准确性。此外，还利用经验方程根据用于瓷砖胶粘剂的水泥砂浆的抗压强度来预测抗折强度和抗拉强度。
