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Self-compacting concrete (SCC) became a strong candidate for various construction applications owing to its excellent workability, low labor demand, and enhanced finish-ability, and because it provides a solution to the problem of mechanical vibration and related noise pollution in urban settings. However, the production of Portland cement (PC) as a primary constituent of SCC is energy-intensive, contributing to about 7% of global carbon dioxide (CO2) emissions. Conversely, the use of alternative geopolymer binders (GBs) in concrete can significantly reduce the energy consumption and CO2 emissions. In addition, using GBs in SCC can produce unique sustainable concrete with unparallel engineering properties. In this outlook, this work investigated the development of some eco-efficient self-compacting geopolymer concretes (SCGCs) obtained by incorporating different dosages of fly ash (FA) and ground blast furnace slag (GBFS). The structural, morphological, and mechanical traits of these SCGCs were examined via non-destructive tests like X-ray diffraction (XRD) and scanning electron microscopy (SEM). The workability and mechanical properties of six SCGC mixtures were examined using various measurements, and the obtained results were analyzed and discussed. Furthermore, an optimized hybrid artificial neural network (ANN) coupled with a metaheuristic Bat optimization algorithm was developed to estimate the compressive strength (CS) of these SCGCs. The results demonstrated that it is possible to achieve appropriate workability and mechanical strength through 50% partial replacement of GBFS with FA in the SCGC precursor binder. It is established that the proposed Bat-ANN model can offer an effective intelligent method for estimating the mechanical properties of various SCGC mixtures with superior reliability and accuracy via preventing the need for laborious, costly, and time-consuming laboratory trial batches that are responsible for substantial materials wastage.
Iman Faridmehr; Moncef Nehdi; Ghasan Huseien; Mohammad Baghban; Abdul Sam; Hassan Algaifi. Experimental and Informational Modeling Study of Sustainable Self-Compacting Geopolymer Concrete. Sustainability 2021, 13, 7444 .
AMA StyleIman Faridmehr, Moncef Nehdi, Ghasan Huseien, Mohammad Baghban, Abdul Sam, Hassan Algaifi. Experimental and Informational Modeling Study of Sustainable Self-Compacting Geopolymer Concrete. Sustainability. 2021; 13 (13):7444.
Chicago/Turabian StyleIman Faridmehr; Moncef Nehdi; Ghasan Huseien; Mohammad Baghban; Abdul Sam; Hassan Algaifi. 2021. "Experimental and Informational Modeling Study of Sustainable Self-Compacting Geopolymer Concrete." Sustainability 13, no. 13: 7444.
The impact of microbial calcium carbonate on concrete strength has been extensively evaluated in the literature. However, there is no predicted equation for the compressive strength of concrete incorporating ureolytic bacteria. Therefore, in the present study, 69 experimental tests were taken into account to introduce a new predicted mathematical formula for compressive strength of bacterial concrete with different concentrations of calcium nitrate tetrahydrate, urea, yeast extract, bacterial cells and time using Gene Expression Programming (GEP) modelling. Based on the results, statistical indicators (MAE, RAE, RMSE, RRSE, R and R2) proved the capability of the GEP 2 model to predict compressive strength in which minimum error and high correlation were achieved. Moreover, both predicted and actual results indicated that compressive strength decreased with the increase in nutrient concentration. In contrast, the compressive strength increased with increased bacterial cells concentration. It could be concluded that GEP2 were found to be reliable and accurate compared to that of the experimental results.
Hassan Amer Algaifi; Ali S. Alqarni; Rayed Alyousef; Suhaimi Abu Bakar; M.H. Wan Ibrahim; Shahiron Shahidan; Babatunde Abiodun Salami. Mathematical prediction of the compressive strength of bacterial concrete using gene expression programming. Ain Shams Engineering Journal 2021, 1 .
AMA StyleHassan Amer Algaifi, Ali S. Alqarni, Rayed Alyousef, Suhaimi Abu Bakar, M.H. Wan Ibrahim, Shahiron Shahidan, Babatunde Abiodun Salami. Mathematical prediction of the compressive strength of bacterial concrete using gene expression programming. Ain Shams Engineering Journal. 2021; ():1.
Chicago/Turabian StyleHassan Amer Algaifi; Ali S. Alqarni; Rayed Alyousef; Suhaimi Abu Bakar; M.H. Wan Ibrahim; Shahiron Shahidan; Babatunde Abiodun Salami. 2021. "Mathematical prediction of the compressive strength of bacterial concrete using gene expression programming." Ain Shams Engineering Journal , no. : 1.
In recent years, the increase in the use of agricultural fertilizers in industrial development has produced poisonous inorganic ions such as nitrates in water and soil. Nitrates in drinking water which may come from nitrogen fertilizers are a potential health risk. Removal of nitrates from the environment is a big challenge. Following the series of investigation, the present study proposes the multiwall carbon nanotubes functionalized with mesoporous silica-nitrenium ions (CNT-MS-N) as a novel adsorbent for removing nitrate ions (NO3−) from aqueous solution. The ability of CNT-MS-N to remove nitrate ions from aqueous solutions was studied at different operating conditions. The maximum removal (98%) was obtained under the optimum conditions: adsorbent dosage of 70 mg and pH 7 and for initial concentration of 80 (ppm) at 30 °C for 5 h contact time. FTIR spectroscopy showed the contribution of amine, amide groups in removing nitrate and the FESEM-EDX results confirmed the adsorption of nitrate ions on the function groups of CNT-MS-N. In addition, nonlinear and linear isotherms and kinetics models were used to evaluate the equilibrium adsorption results. The coefficient of determination (R2) was used to determine the best-fit model expected by each approach. The results showed that the non-linear Langmuir isotherm model is a better way to achieve adsorption parameters illustrating the adsorption of nitrate ions onto CNT-MS-N with R2 (0.9829). Likewise, it was found that the nonlinear Pseudo-second order rate model using the non-linear regression approach better predicted experimental results with R2 (0.9921). The present investigation confirmed the nonlinear method as an appropriate technique to predict the optimum adsorption isotherm and kinetic data.
Wahid Ali Hamood Altowayti; Norzila Othman; Pei Sean Goh; Abdullah Faisal Alshalif; Adel Ali Al-Gheethi; Hassan Amer Algaifi. Application of a novel nanocomposites carbon nanotubes functionalized with mesoporous silica-nitrenium ions (CNT-MS-N) in nitrate removal: Optimizations and nonlinear and linear regression analysis. Environmental Technology & Innovation 2021, 22, 101428 .
AMA StyleWahid Ali Hamood Altowayti, Norzila Othman, Pei Sean Goh, Abdullah Faisal Alshalif, Adel Ali Al-Gheethi, Hassan Amer Algaifi. Application of a novel nanocomposites carbon nanotubes functionalized with mesoporous silica-nitrenium ions (CNT-MS-N) in nitrate removal: Optimizations and nonlinear and linear regression analysis. Environmental Technology & Innovation. 2021; 22 ():101428.
Chicago/Turabian StyleWahid Ali Hamood Altowayti; Norzila Othman; Pei Sean Goh; Abdullah Faisal Alshalif; Adel Ali Al-Gheethi; Hassan Amer Algaifi. 2021. "Application of a novel nanocomposites carbon nanotubes functionalized with mesoporous silica-nitrenium ions (CNT-MS-N) in nitrate removal: Optimizations and nonlinear and linear regression analysis." Environmental Technology & Innovation 22, no. : 101428.