The presence of construction wastes such as clay bricks, glass, wood, plastic, and others in large quantities causes serious environmental problems in the world. Where these wastes can be used to preserve the natural resources used in construction and reduce the impact of this problem on the environment, it also works to reduce the problem of high loads of concrete blocks. Clay bricks aggregate (AB) can be recycled as coarse aggregate and replaced with volumetric proportions of coarse aggregate by ( 5% and 10%), as well as the use of clay brick powder (PB) by replacing its weight of cement (5% and 10%) and reduced in the manufacture of concrete blocks (blocks). Four mixtures will be prepared and tested to learn how to re

The aim was to design a MATLAB program to calculate the phreatic surface of the multi-well system and present the graphical shape of the water table drawdown induced by water extraction. Dupuit’s assumption is the base for representing the dewatering curve. The program will offer the volume of water to be extracted, the total number of wells, and the spacing between them as well as the expected settlement of soil surrounding the dewatering foundation pit. The dewatering well arrangement is required in execution works, and it needs more attention due to the settlement produced from increasing effective stress.

In this research the performance of 5G mobile system is evaluated through the Ergodic capacity metric. Today, in an­­y wireless communication system, many parameters have a significant role on system performance. Three main parameters are of concern here; the source power, number of antennas, and transmitter-receiver distance. User equipment’s (UEs) with equal and non-equal powers are used to evaluate the system performance in addition to using different antenna techniques to demonstrate the differences between SISO, MIMO, and massive MIMO. Using two mobile stations (MS) with different distances from the base station (BS), resulted in showing how using massive MIMO system will improve the performance than the standar

The ability to produce load-bearing masonry units adopting ACI 211.1 mix design using (1:3.2:2.5) as (cement: fine aggregate: coarse aggregate) with slump range (25-50mm) which can conform (dimension, absorption, and compressive strength) within IQS 1077/1987 requirements type A was our main goal of the study. The ability to use low cement content (300 kg/m3) to handle our market price products since the most consumption in wall construction for low-cost buildings was encouraging. The use of (10 and 20%) of LECA as partial volume replacement of coarse aggregate to reduce the huge weight of masonry blocks can also be recommended. The types of production of the load-bearing masonry units were A and B for (

Sewer sediment deposition is an important aspect as it relates to several operational and environmental problems. It concerns municipalities as it affects the sewer system and contributes to sewer failure which has a catastrophic effect if happened in trunks or interceptors. Sewer rehabilitation is a costly process and complex in terms of choosing the method of rehabilitation and individual sewers to be rehabilitated.  For such a complex process, inspection techniques assist in the decision-making process; though, it may add to the total expenditure of the project as it requires special tools and trained personnel. For developing countries, Inspection could prohibit the rehabilitation proceeds. In this study, the researchers propos

Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att

A substantial percentage of the world’s energy consumption (almost 40%) and carbon dioxide (CO2) emissions (around 37%) come from the construction industry, especially schools. This work presents a new hybrid artificial intelligence (AI) engineering model that aims to maximize energy performance on campuses in a holistic way. Modules for data-driven forecasting, metaheuristic optimization, and real-time adaptive control are all part of the concept. A thorough energy simulation of a university campus building is used in conjunction with the AI model to assess its performance through a co-simulation framework. Findings show that yearly peak electricity demand may be reduced by 18.7% and total site energy consumption by 22.4% when co