Deteriorated water distribution networks require significant investments to maximize their functionality. The problem is that limited financial resources are allocated for rehabilitation strategies. This deficiency highlights the importance of developing a tool that helps decision makers develop maintenance and replacement management plans. The optimization tool is employed using two evolutionary algorithms: genetic algorithms and particle swarm optimization. The efficacy of the developed model is demonstrated through its application in a case study of Shaker Al-Bahery, Egypt. Furthermore, evaluation metrics are considered to compare the performance of the aforementioned algorithms. The results reveal that the particle swarm optimization exhibited superior results when compared with the genetic algorithms. Moreover, the following two multicriteria decision-making techniques are used to provide a ranking for the near-optimum solutions: multiobjective optimization on the basis of ratio analysis and technique for order preference by similarity to ideal solution. Finally, the Spearman correlation coefficient is utilized to assess the correlation between rankings obtained from different decision-making methods. The results indicate a very strong relation among the aforementioned techniques.

The objective of this research is to create a comprehensive risk assessment framework for the restoration stage of heritage building rehabilitation in Egypt. Forty-two primary risk factors were gathered from various sources including literature, site visits, and stakeholder interviews conducted during the restoration stage of the Amr ibn al-Aas mosque and Kom el-Shoqafa Cemetery in Alexandria. An online questionnaire was created, and the data was analyzed utilizing IBM SPSS version 20. The top risk factors identified were collapse of the heritage building due to design/construction errors, falling the labors from heights, falling materials due to the building's structural weaknesses and old artistic components, causing irreparable damage while trying to restore the Heritage building, collapse of scaffolding used, fire accidents of timber buildings, assigning design tasks to unqualified designers, insufficient awareness and lack of qualifications, skills, and experience of the contractor, using inappropriate methods, selecting unsuitable materials, inadequate qualification and supervision of the owner's engineer, lack of skilled labor, poor communication between design team & construction team, poor quality control, lake of installing safety measures, lack of availability of the proper materials, using low quality materials, excavation and using heavy equipment near the rehabilitated building, lack of understanding the project scope and objectives by the contractor, and inability to determine the accurate scope of work

The estimation of costs is a crucial aspect of construction planning that should be carried out during the initial phases of a project to establish its budget. The term "project overheads" refers to the indirect expenses associated with a project, such as providing general services at the site or plant, including insurance, site accommodation, and other similar costs. The goal of this research is to investigate the variables that affect the precision of site overheads estimation. In this study, the effectiveness of artificial neural networks (ANNs) was examined in addressing the challenge of accurately estimating project overhead costs during the initial stages of building design. The research involved developing a comprehensive prediction model to estimate the percentage of site overhead costs. The primary objective of this paper is to examine the methods used for estimating project overheads in Egypt. This involves identifying factors that affect site overhead costs and developing a detailed prediction model for construction site overhead using artificial neural network in Egypt.For the purpose of achieving the goal of this research, A questionnaire was conducted among construction companies in Egypt. It was found that the most important factors (according to past studies and questionnaire results) were project’s duration, Inflation and Interest rate in the Country and project’s size. ANN model has been developed to estimate the overhead percentage depending on its characteristics. It was found that the use of the ANN model is an effective tool for minimizing the amount of work required to estimate the percentage of on-site overhead costs with greater accuracy.