Articles in This Issue
Abstract
This study aims to achieve the optimal design of a groundwater desalination plant using reverse osmosis in the Umm- Qasr area in Basra / Iraq. Field and laboratory data for groundwater were collected. Groundwater samples were collected from five observation wells and analyzed for chemical and physical properties every two months from June 2023 to April 2024. Temperature, pH, total dissolved solids (TDS), and silt density index (SDI) were measured at field. The RO plant was divided to four train with 398.5 m3/hr feedwater for each train and was designed by using the (WAVE) 1.82 software employing different design for each train, recovery rate (50%, 60%, and 65%), number of stages (single-stage and two-stage), and types of membranes (SW30XLE400, SW30XLE440 and SEAMAXX440). The number of membranes per pressure vessel were (6 and 8), with a total membranes number of (264 and 228). The results show the groundwater is very saline with the average value of TDS was 15206 mg/l and very low turbidity and SDI with average values of 0.35 NTU and 0.92 min-1, respectively. T
Abstract
Accurate prediction of concrete compressive strength remains a critical challenge in structural engineering due to the complex, non-linear relationships between mixture components and mechanical properties. This study presents a comprehensive machine learning framework for predicting concrete compressive strength, incorporating both deterministic and probabilistic approaches. We analyzed 1030 laboratory-tested concrete specimens with eight input variables to evaluate four distinct algorithms: linear regression, support vector regression, random forest, and artificial neural networks. Additionally, we developed a Monte Carlo dropout neural network to quantify prediction uncertainty through Bayesian approximation. Results demonstrate that ensemble tree-based methods achieved superior predictive performance with a root mean square error of 5.47 MPa and coefficient of determination of 0.884, significantly outperforming conventional approaches at the 0.01 significance level. Feature importance analysis revealed that curing age and cement content collectively account for 66% of the predictive variance, while water content exhibits the expected negative correlation with strength development. However, uncertainty quantification through Monte Carlo dropout revealed systematic undercalibration, achieving only 38.4% coverage of the nominal 95% prediction interval. These findings provide engineers with quantitative insights for mixture design optimization while emphasizing the importance of uncertainty awareness in computational predictions for structural applications.
Abstract
Assessing land suitability for irrigation is crucial in boosting agricultural production, particularly in arid regions like Al Teeb, Iraq, where water scarcity poses significant challenges. This study evaluates the irrigation potential of the Al Teeb area by integrating GIS with a multi-criteria decision analysis (MCDA) approach. The research aims to estimate and classify land based on physical and environmental factors, including soil texture, salinity, organic carbon content, available water capacity (AWC), slope, land use/land cover (LULC), proximity to rivers, and road accessibility. These criteria were prioritized using the Analytical Hierarchy Process (AHP), with higher weights assigned to soil texture and slope due to their influence on water retention and erosion risk. Data for the analysis were collected from field soil sampling, satellite imagery, SRTM elevation data, and thematic maps. Laboratory results and FAO guidelines were applied to assess soil characteristics, while slope and other spatial data were processed in ArcGIS. The integration of these factors through weighted overlay analysis produced an irrigation suitability map, categorizing the land as highly suitable, moderately suitable, marginally suitable, or unsuitable. The results of the irrigation suitability map shows that 2.05% (36.75 km²) of the study area is permanently unsuitable (N) for agricultural production, while 32.66% (584.94 km²) is marginally suitable (S3), 44.82% (802.71 km²) is moderately suitable, and 20.47% (366.62 km²) is highly suitable. Consequently, about 65% of the area is classified as moderately to highly suitable for irrigation.