Since evapotranspiration typically makes up the largest portion of the terrestrial water cycle, it is one of the most crucial factors in determining how much water is available. This study evaluated four models (Penman-FAO-24(PF), Penman-Monteith -FAO-56(PM), Penman-Kimberly(PK), and Jensen-Haise (JH)) utilized frequently to calculate monthly reference crop evapotranspiration (ET_0) values for Ramadi irrigation project (fourth stage). The statistical indicators considered were the root mean square error (RMSE), Mean Absolute Error (MAE), Relative Error (RE), Correlation Coefficient (R^2) and mean bias error (MBE), models were used to estimate evapotranspiration, and by calculating evapotranspiration for Al-Ramadi city according to the climate data available to us, The (PF) model had the lowest MBE = 0.02945, greatest RMSE = 29.369, and highest R = 0.9641 values among the four models, demonstrating that it is the best. The JH model, which achieved the highest values of MBE = 0.00978 and RSME = 58.509, was the least accurate of the models.. The study's conclusions may be useful to farmers, decision-makers, and local water organizations in assessing irrigation water requirements, planning, and effective use of water resources.
To classification groundwater quality in the study area, three wells were drilled at a depth of 10m and selected two locations across Al Warrar Canal to represent their water quality. Water samples were collected from these wells and the Warrar Canal to examine water quality. Then results were compared against the World Health Organization (WHO) limits to study the Index of Water Quality (WQI). WQI was calculated according to the Canadian Council of Ministers of the Environment (CCME), and the quality of water was evaluated for domestic and irrigation uses. The samples were tested for electrical conductivity, pH, temperature, total dissolved solids, chloride, total hardness, nitrate, and alkalinity according to the standard methods. The results of laboratory analysis showed significant differences among the wells and Warrar Canal water quality in the measured parameters according to WHO limits. Due to many human activities like urbanization, agrarian overflow, drainage of untreated sewage, and industrialization, high values of trace elements and heavy metals were recorded in wells three. For agriculture purposes, the results show that the water in the three wells is very high salinity, where the Warrar Canal is high salinity, and Canal water causes saline and alkali damages. It was recommended that the WQI in three wells was poor water quality whereas, marginal water quality was pointed in AL Warrar Canal.
One main natural hazard resulting from interactions between rainfall-runoff is flooding. Extreme precipitation causes surface water flow to rise, hence breaking river systems and causing flooding. This work models and forecasts flood dynamics in the Kunhar River basin with the Hydrologic Engineering Centre's Hydrologic Modelling System (HEC-HMS). It shows how well the technology can combine several hydrological data points to precisely project flood paths. These studies have repeatedly shown the dependability of HEC-HMS over many geographical and climatic environments, therefore confirming its fit for thorough hydrological research. HEC-HMS included obtaining thorough datasets comprising historical hydrological data from NASA, spatial information from ARCGIS, and meteorological data from WAPDA in addition to flow rates and water levels. We started the basin model in HEC-HMS by including Digital Elevation Models (DEMs) to define watershed boundaries and record topography. Terrain preprocessing came next to solve discontinuities and guarantee correct water flow modelling. Combining several datasets, the model was designed to reflect the coordinate system and underwent hydrological study to replicate surface water flow, accumulation, and stream networks inside the basin. With subbasin-10 showing the largest peak flow of 132 cubic meters per second during severe rainfall events, especially on August 7, 2013, results revealed that HEC-HMS effectively forecasted peak discharges in the subbasins of the Kunhar River. With an estimate 90% accuracy rate, this proved the great dependability of HEC-HMS in flood prediction. The results show that the model can help with flood management planning and foresee flood circumstances. HEC-HMS's value in designing flood barriers and enhancing watershed management particular to each subbasin. It emphasizes the need of revised hydrological models to consider land use and increasing temperature.
Composite columns are frequently used in constructing high-rise structures because they can minimize the size of the building's columns while increasing the floor plan's usable space. This study aims to create a nonlinear 3D finite element model for square composite columns designed for solid and hollow columns with various multi-skin tubes subjected to loads at eccentricities of (30 and 60) mm, compressive strength, and mesh size using the ABAQUS software. The comparison was based on the experimental data of six references of composite columns. While the compressive strength of concrete increases, the stiffness of the composite column rise. The ratio of concrete compressive strength values for composite column increased by (0, 12.3, 17.8, and 26.7 percent) for (fc'=25, 31.96, 35, and 40) MPa, respectively. The results of the different mesh sizes (20, 40, and 60) mm are showing; The experimental results and the finite element solution developed using the (20 X20) mm element correspond well. The nonlinear finite element analysis method was used, and the finite element outputs results were confirmed to be in favorable agreement with the experimental data
Collapse of gypseous soils may cause excessive settlement and serious damage to engineering structures. Various improvement approaches, such as mechanical techniques and chemical additions, have been used to reduce the collapsibility of these soils. The odometer test has traditionally been used to assess the collapsibility of the improved gypseous soils; however, because the small size of test specimens, this method may not adequately reflect field conditions. In this research, a laboratory model test of 600 x 600 x 600 mm with a model footing of 100 x 100 mm was developed to measure the collapse characteristics of a gypseous soil. The top layer underneath the footing was improved by compaction, cement kiln dust (CKD), geogrid, and a combination between CKD and geogrid. The top layer was improved at two values of thickness of 50 and 100 mm. The results obtained from this study indicate that the values collapsibility settlement reduction factor for compacted soil and the soil treated with CKD were 75 and 82%, 89% receptively. These values increased up to 95 % when a combination of CKD and geogrid was applied. As discussed herein, the aforementioned treatment methods can effectively be used to improve the collapsibility of gypseous soils.
In recent years, hospital waste has been one of the most serious issues in Iraq and other parts of the world. The current study aims to measure and analyze hospital waste output across all departments at the Ramadi Teaching Hospital. The data on waste generation rates gathered for the study were primarily based on existing records of field management of hospital waste over the course of eight months (one week per month) for all departments in the hospital; however, some random sampling information was provided to supplement the data. The results revealed that the estimated rate of medical waste creation at Ramadi hospital was between 144 and 188 kg/day, whereas the general (non-medical) waste generation was between (240-278) kg/day. In terms of patient numbers and per occupied bed, the average medical waste generation rates were from 0.60 to 0.90 kg/patient/day and (0.85-1.11) kg/bed/day, respectively, whereas the average general trash generation rates ranged from 0.86 to 1.15 kg/patient/day and 1.42-1.64 kg/bed/day. The recent analysis concluded that the hospital's segregation procedure is still inefficient, and there is room for improvement in terms of reducing hazardous medical waste creation
Organic soils are problematic soil for various engineering applications due to their high compressibility and low shear strength which need to be improved. For many soil improvement techniques, using waste materials, such as fly ash (FA), is a practical and sustainable process. In this research, FA and geopolymer were used e used to reduce organic soil's compressibility. A one-dimensional consolidation test was performed to evaluate the organic soil's consolidation and compressibility properties. The geopolymer was prepared using 20% FA and of sodium hydroxide ratio and sodium silicate alkali solutions. The geopolymer specimens were first cured for 2 hours at 45 and 65 oC, then cured for further 28 days at room temperature. The consolidation test results showed that FA-based geopolymer is effective in stabilizing organic soils due to the observed improvement in the compressibility, consolidation, and permeability characteristics. The compression index decreased by 98.16%, and the permeability decreased by 95%.
Crack monitoring of pavements is an ever-evolving technology with new crack identification technologies being introduced frequently. Although older technologies consisted of physical removing the pavement section using coring, however new methods are available that are non-destructive and yield a higher performance than conventional technologies. This paper compiles various crack monitoring technologies such as wireless sensor networks, photo imaging, laser imaging, 3D road surface profile scans, acoustics wave propagation technology, embedded strain sensors and onboard vehicle sensors that majorly use an artificial intelligence algorithm to identify and categorize the cracks. The research also includes the use of convolutional neural network that can be used to analyze pavement images and such neural network can localize and classify the cracks for crack initiation and propagation stage. The research concludes with the favor of using the optical imaging technology called Syncrack which serves better performance in terms of time of prediction by 25% and accuracy by 30% when compared to other sensing technologies.