The main aim of this research is to introduce financing cost optimization and different financing alternatives. There are many studies about financing cost optimization. All previous studies considering the cost of financing have many shortcomings, some considered only one source of financing as a credit line without taking into account different financing alternatives. Having only one funding alternative powers, restricts contractors and leads to a very specific financing model. Although it is beneficial for the contractor to use a long-term loan to minimize interest charges and prevent a substantial withdrawal from his credit line, none of the existing financial-based planning models have considered long-term loans in

In this paper the analysis of the corrosion basic types destructions on an example of buildings constructions is carried out and the basic requirements to materials for designs in construction are defined. A classification of corrosion damage is proposed, which became a prerequisite for selecting the main indicators of materials that maximally affect the quality and durability. The main methods for determining the main indicators of corrosion damage are identified.

Genetic algorithms (GA) are a helpful instrument for planning and controlling the activities of a project. It is based on the technique of survival of the fittest and natural selection. GA has been used in different sectors of construction and building however that is rarely documented. This research aimed to examine the utilisation of genetic algorithms in construction project management. For this purpose, the research focused on the benefits and challenges of genetic algorithms, and the extent to which genetic algorithms is utilised in construction project management. Results showed that GA provides an ability of generating near optimal solutions which can be adopted to reduce complexity in project management and resolve difficult problem

Construction projects are characterized as projects with multi phases and activities, complex, unique, and have many different parties and stakeholders. Risks could appear at one or more of the construction project stages and may affect the achievement of project objectives. Therefore, one of the key elements in the planning phase of any project is the risk management process (RMP). This study attempts to understand the terminology of risk in general, risk management, and response to risk in particular. This study is mainly a review of thirty-eight studies that have been published between 1997 and 2020 that demonstrate the importance of the crucial phase of risk response from the risk management process and its impact on

The management of construction projects needs to complete the basics of system management and work. Starting from the idea and how to turn it into a full study and ended at the construction project completion arriving at the purpose prepared for it, so the projects need to control on its operation and integration system in order to succeed.

It is no secret for who concerned in construction projects field that the design stage is a very important stage in construction project because it determines the final features of the project through the requirements provided by the employer for the consultant to formulate it during this phase in the form of plans, drawings, and specifications, then translated on the ground as the shape of co

The Internet of Things (IoT) has significantly transformed modern systems through extensive connectivity but has also concurrently introduced considerable cybersecurity risks. Traditional rule-based methods are becoming increasingly insufficient in the face of evolving cyber threats.  This study proposes an enhanced methodology utilizing a hybrid machine-learning framework for IoT cyber-attack detection. The framework integrates a Grey Wolf Optimizer (GWO) for optimal feature selection, a customized synthetic minority oversampling technique (SMOTE) for data balancing, and a systematic approach to hyperparameter tuning of ensemble algorithms: Random Forest (RF), XGBoost, and CatBoost. Evaluations on the RT-IoT2022 dataset demonstrat

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature