The reduction of gases emissions as one of its most significant long-term strategies in any country in the world. Many Iraqi cities suffered from the uncontrolled increasing in the number of vehicles which has a positive relationship with the emission of gases especially the carbon monoxide. This study aims to evaluate the effect of traffic characteristics such as logarithm of average flow, the percentage of heavy vehicles, and free flow speed on the emission of carbon monoxide. The study selected the main roads in Al- Ramadi network, the data was collected for traffic characteristics and carbon monoxide between 2018 to 2020. A random parameters approach was used to develop a model to estimate the carbon monoxide emission for 345 roadway segments, this approach was used due to the ability of this method to account the heterogeneity that raised from the traffic characteristics which led to predict more accurate results than other approaches. The results of the random parameters model show that the carbon monoxide emission increased due to increase of logarithm of average flow, the percentage of heavy vehicles, and free flow speed. The model results show that the parameters of logarithm of average flow, the percentage of heavy vehicles, and free flow speed was varied a cross the roadway segments.
Dams are considered as the best solution to conserve water especially in arid and semi-arid regions. This study aims to design a small dams series to conserve rainfall water. Mathematical model is proposed to optimize these dams height and locations, its named as Optimal Height And Location Model (OHALM). In this study, new method is introduced to estimate the optimal water level and volume of storage by combining between the digital elevation model generated by the Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) data, and the proposed model (OHALM). Two dams sites were selected for checking the validity of proposed method. The results of the present study showed that the error percentage increase or decrease from reference value by 3.5%, 13% for water level in Al-Rutba dam and Horan 3 dam respectively, and by 6.63%, 35.8% from volume of storage in Al-Rutba dam and Horan 3 dam respectively. The relative error shows a big difference from the actual data, which is a positive percentage for storing additional quantities of rainwater. That means the proposed program is better than the existing dam design, and thus the possibility of using this method to determine the optimal height of the proposed water harvesting sites.
In recent years, a number of researchers have adopted the wet packing (WP) approach to design different types of concrete mixes. Particle grading is a key to the optimization of the wet compactness density; for that reason, all empty spaces that exist in between large-size particles need to be completely filled with particles of smaller size. Previously-conducted studies in this field have been focused on measuring the particle size distribution’s packing density (PD) of the of granular matrices is the purpose of investigating how to increase the PD of cementitious materials. Thus, literature lacks models capable of predicting the optimal PD value. The current study collected and analyzed 216 datasets in order to construct a model for accurate prediction of PD. The main datasets were organized into two categories: modeling datasets and validation datasets. To configure the model in the best way, a hybrid gravitational search algorithm-artificial neural network (GSA-ANN) was also developed in this study. The findings confirmed ANN as an effective alternative for measuring the ultimate PD of cementitious pastes. ANN provided high levels of accuracy, practicality, and effectiveness in the process of predicting the PD value. Based on the final results, the implementation of the hybrid GSA-ANN technique causes a significant decrease in the number of tests conducted on experimental samples, which results in not only saving time and money, but also reducing the CO2 emission volume.