NTU Journal of Engineering and Technology

This paper presents the influence of numerous limits on compressive strength of fly ash-based geopolymer concrete mixtures optimized by Taguchi method using Minitab program. The major goal is to find the optimal ratios that may give the greatest compressive strength of geopolymer concrete. A total of twenty fife mixtures of geopolymer concrete with fife mixtures of normal concrete were assessed considering the effect of inclusion of Alkali-Activator to binder ratio (AA/B), NaOH concentration (M), Influence of Na2SiO3 to NaOH ratio (SS/SH) and effect of binder content (B). Different molarities was used in this study: 8, 10, 12, 14, and 16. Flay ash by way of waste material is used in ratios of (15, 17.5, 20, 22.5, and 25) % and substituted via 90 % of ordinary Portland cement to output the geopolymer concrete. Also, sodium silicate to sodium hydroxide are utilized in ratios of 1, 1.5, 2, 2.5, and 3. The alkali activator was made using sodium silicate, sodium hydroxide, and water in ratios of (0.3, 0.35, 0.4, 0.45, and 0.5). One type of locally crushed coarse aggregate was used instead of normal coarse aggregate that has sizes of 19 mm, as well as, fine aggregate. The examined mechanical property is characterized by compressive strength. The outcomes specify that the geopolymer concrete exhibits The highest compressive strength has been reached when the alkali-activator to binder ratio equal to 0.45, NaOH concentrations equal to 16, Na2SiO3 to NaOH ratio equal to 1 and binder content reached to 25 %. Also, the result showed that the compressive increases as the curing age increases. Moreover, scanning electron microscopy was evaluated to show the geopolymer concrete microstructure.

This study adapts the geometry of CFRP used as a strengthening material to improve the shear strength of reinforced concrete corbels. The experimental work investigated using CFRP with specific geometry that focused on the size and magnitude of CFRP installation. The sizes are (1/3, 2/3 & full) the height of the cross-section, and the magnitude represented by multi-layers of CFRP. The results of the experimental work revealed that the performance of the CFRP near the neutral axis most contributed to the increase in the shear with 33.6 % in the middle third compared to 46.25 % for fully strengthened. The results also showed that using the second layer of CFRP was less efficient than the first layer due to debonding between the strips and the concrete surface before the rupture of the CFRP strips.

The city of Kirkuk is facing a real crisis in increasing air pollution due to the oil facilities and the lack of trees and their uneven distribution, which exacerbated the problem. This study aims to demonstrate the effect (size, type, and distribution) of urban trees on air pollution in Kirkuk, with a focus on simulating the dispersion of pollutants resulting from the oil refinery. using the Gaussian dispersion model. We will assess the role of existing trees in reducing pollution and explore the potential benefits of planting new trees and their role in reducing air pollution. The study's results inform urban tree’s role in reducing air pollution and strategies for integrating them into city planning. The Gaussian model adds scientific rigor, accurately simulating and evaluating pollution distribution. This methodology serves as a strong basis for analyzing tree effects on air pollution in Kirkuk and other urban areas, aiding evidence-based decision-making to improve air quality and public health.

The main objective of this work is to accurately predict in irrigation and hydraulic systems the discharge coefficient of the used sharp-crested inclined dams. Training algorithms on radial basis function RBF and multilayer perceptron MLP, and input variables such as weir height, length, inclination, and flow rates. From a tilted weir, researchers have used these training techniques as a model for various neural networks. In addition, the performance of CFNN is better than other neural networks such as RBF and MLP. The discharge coefficient Cd was the output variable. 95 test results were analysed. CFNN achieved a significant reduction in mean square errors (MSE), with values of 9.4363×10-12 and 1.6336×10-05, respectively, in the training and testing phases.