One of the most important industries and building operations that cause carbon dioxide emissions is the cement and concrete-related industries that consume about 6 million Btus per metric ton and release about 1 metric ton CO₂. Reducing cement consumption while using nanomaterials as cement replacement is favored for environmental protection reasons. In this study, the effect of nanoclay (NC) as an additive to the cement paste was evaluated and quantified. Scanning Electronic Microscope (SEM), X-ray diffraction (XRD), Thermogravimetric Analysis (TGA), Fourier-Transform Infrared Spectroscopy (FTIR), and Raman Spectroscopy analysis were used to identify the cement and nanoclay. Experimental tests and modeling were conducted to predict the cement paste's flow properties like yield stress, shear strength (shear stress limit), viscosity, and stress at the failure stress of cement paste. The cement paste modified with nanoclay was tested at a water-to-cement ratio (w/c) of 0.35 and 0.45 and temperatures ranging from 25⁰C to 75⁰C. The addition of NC increased the ultimate shear strength (τ_max) and the yield stress (τ_o) from 22.5% to 54.4% and from 26.3% to 203%, respectively based on the NC content, w/c, and temperature. TGA tests showed that the 1% nanoclay additive reduces the weight loss of the cement at 800⁰C by 74% due to the interaction with the nanoclay with the cement paste. The nonlinear regressions model (NLR), and Artificial Neural Network (ANN) technical approaches were used for the qualifications of the flow of slurry and stress at the failure of the cement paste modified with nanoclay. Based on the static analysis assessments, the rheological properties and compressive strength of cement paste modified with nanoclay can be well predicted in terms of w/c, nanoclay content, temperature, and curing time using two different simulation techniques. Among the used approaches and based on the experimental data set, the model made based on the NLR models is the most reliable model to predict rheological properties and compression strength of the cement and it is performing better than the ANN model. The coefficient of the correlation (R), mean absolute error (MAE), and root mean square error (RMSE) concluded that the nanoclay content is the most important parameter for rheological estimation and compression strength of cement paste.

导致二氧化碳排放的最重要的行业和建筑运营之一是水泥和混凝土相关行业，每公吨消耗约 600 万英热单位并释放约 1 公吨 CO ₂. 出于环保原因，在使用纳米材料替代水泥的同时减少水泥消耗是有利的。在这项研究中，评估和量化了纳米粘土 (NC) 作为水泥浆添加剂的影响。使用扫描电子显微镜 (SEM)、X 射线衍射 (XRD)、热重分析 (TGA)、傅里叶变换红外光谱 (FTIR) 和拉曼光谱分析来鉴定水泥和纳米粘土。进行了实验测试和建模以预测水泥浆的流动特性，如屈服应力、剪切强度（剪切应力极限）、粘度和水泥浆破坏应力下的应力。用纳米粘土改性的水泥浆在 0.35 和 0.45 的水灰比 (w/c) 和 25⁰C 到 75⁰C 的温度范围内进行了测试。_max ) 和屈服应力 (τ _o) 从 22.5% 到 54.4% 和从 26.3% 到 203%，分别基于 NC 含量、w/c 和温度。TGA 测试表明，由于纳米粘土与水泥浆的相互作用，1% 的纳米粘土添加剂使水泥在 800⁰C 下的重量损失减少了 74%。采用非线性回归模型（NLR）和人工神经网络（ANN）技术方法对纳米粘土改性水泥浆体破坏时的泥浆流动和应力进行鉴定。基于静态分析评估，可以使用两种不同的模拟技术在水灰比、纳米粘土含量、温度和固化时间方面很好地预测纳米粘土改性水泥浆的流变特性和抗压强度。在使用的方法中并基于实验数据集，基于 NLR 模型建立的模型是预测水泥流变特性和抗压强度最可靠的模型，其性能优于 ANN 模型。相关系数 (R)、平均绝对误差 (MAE) 和均方根误差 (RMSE) 得出结论，纳米粘土含量是水泥浆流变学评估和抗压强度的最重要参数。