Khan Kaffayatullah, Salami Babatunde Abiodun, Jamal Arshad, Amin Muhammad Nasir, Usman Muhammad, Al-Faiad Majdi Adel, Abu-Arab Abdullah M, Iqbal Mudassir
Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, P.O. Box 380, Al-Hofuf 31982, Saudi Arabia.
Interdisciplinary Research Center for Construction and Building Materials, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.
Materials (Basel). 2022 Aug 24;15(17):5823. doi: 10.3390/ma15175823.
The depletion of natural resources of river sand and its availability issues as a construction material compelled the researchers to use manufactured sand. This study investigates the compressive strength of concrete made of manufactured sand as a partial replacement of normal sand. The prediction model, i.e., gene expression programming (GEP), was used to estimate the compressive strength of manufactured sand concrete (MSC). A database comprising 275 experimental results based on 11 input variables and 1 target variable was used to train and validate the developed models. For this purpose, the compressive strength of cement, tensile strength of cement, curing age, Dmax of crushed stone, stone powder content, fineness modulus of the sand, water-to-binder ratio, water-to-cement ratio, water content, sand ratio, and slump were taken as input variables. The investigation of a varying number of genetic characteristics, such as chromosomal number, head size, and gene number, resulted in the creation of 11 alternative models (M1-M11). The M5 model outperformed other created models for the training and testing stages, with values of (4.538, 3.216, 0.919) and (4.953, 3.348, 0.906), respectively, according to the results of the accuracy evaluation parameters root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The R2 and error indices values revealed that the experimental and projected findings are in extremely close agreement. The best model has 200 chromosomes, 8 head sizes, and 3 genes. The mathematical expression achieved from the GEP model revealed that six parameters, namely the compressive and tensile strength of cement, curing period, water−binder ratio, water−cement ratio, and stone powder content contributed effectively among the 11 input variables. The sensitivity analysis showed that water−cement ratio (46.22%), curing period (25.43%), and stone powder content (13.55%) were revealed as the most influential variables, in descending order. The sensitivity of the remaining variables was recorded as w/b (11.37%) > fce (2.35%) > fct (1.35%).
河砂自然资源的枯竭及其作为建筑材料的可用性问题促使研究人员使用机制砂。本研究调查了用机制砂部分替代普通砂制成的混凝土的抗压强度。预测模型，即基因表达式编程（GEP），用于估计机制砂混凝土（MSC）的抗压强度。一个包含基于11个输入变量和1个目标变量的275个实验结果的数据库被用于训练和验证所开发的模型。为此，将水泥抗压强度、水泥抗拉强度、养护龄期、碎石最大粒径、石粉含量、砂的细度模数、水胶比、水灰比、含水量、砂率和坍落度作为输入变量。对不同数量的遗传特征（如染色体数、头部大小和基因数）进行研究，产生了11个替代模型（M1 - M11）。根据精度评估参数均方根误差（RMSE）、平均绝对误差（MAE）和决定系数（R2）的结果，M5模型在训练和测试阶段的表现优于其他创建的模型，其值分别为（4.538, 3.216, 0.919）和（4.953, 3.348, 0.906）。R2和误差指数值表明实验结果和预测结果非常吻合。最佳模型有200条染色体、8个头部大小和3个基因。从GEP模型得到的数学表达式表明，在11个输入变量中，水泥抗压强度、水泥抗拉强度、养护期、水胶比、水灰比和石粉含量这六个参数有显著贡献。敏感性分析表明，水灰比（46.22%）、养护期（25.43%）和石粉含量（13.55%）是影响最大的变量，按降序排列。其余变量的敏感性记录为水胶比（11.37%）>水泥抗压强度（2.35%）>水泥抗拉强度（1.35%）。
