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Highways are one of the most critical infrastructure projects with strategic impact on the countries’ development. Asphalt has been historically the main pavement material used in all highway projects, especially in Egypt. However, with the booming in concrete technology in the past two decades, concrete has become a strong rival to asphalt, especially in highway applications. Several factors impact the decision-making criteria for any highway, which differ according to the priorities and requirements of each decision-maker and the nature of the project.

This research focuses on studying and analyzing the different factors that impact the decision for selecting the material type for highways in Egypt’s pavement construction industry. The outputs of the analysis are then incorporated into a multi-decision-making tool to assess the optimum solution as per the priorities of the decision-maker. A holistic framework is developed to compare asphalt and concrete pavements solutions considering; initial cost, maintenance cost on the life cycle, construction duration, embodied energy and fuel consumption. The data collection on local highways was performed through interviewing and surveying experts in the consulting, contracting and building materials fields (total of 15 respondents).

A multi-decision-making tool developed using the superiority and inferiority ranking method for selecting the best alternate. To illustrate the practicality of the proposed framework, a case study for assessment and validation has been done on Sokhna–Quarries highway in Egypt. The framework results reveal that despite a lower initial cost and faster construction of asphalt, concrete pavement is more cost-efficient on the lifecycle time. The multi-decision-making model indicates that concrete is a better alternate for highway applications given the cost, time and energy factors considered.

The proposed model takes into consideration the important parameters in selecting the type of pavement to be constructed considering two alternates asphalt and concrete.

This paper aims to apply a methodology that is capable to classify and localize mechanical, electrical and plumbing (MEP) elements to assist facility managers. Furthermore, it assists in decreasing the technical complexity and sophistication of different systems to the facility management (FM) team.

This research exploits artificial intelligence (AI) in FM operations through proposing a new system that uses a deep learning pre-trained model for transfer learning. The model can identify new MEP elements through image classification with a deep convolutional neural network using a support vector machine (SVM) technique under supervised learning. Also, an expert system is developed and integrated with an Android application to the proposed system to identify the required maintenance for the identified elements. FM team can reach the identified assets with bluetooth tracker devices to perform the required maintenance.

The proposed system aids facility managers in their tasks and decreases the maintenance costs of facilities by maintaining, upgrading, operating assets cost-effectively using the proposed system.

The paper considers three fire protection systems for proactive maintenance, where other structural or architectural systems can also significantly affect the level of service and cost expensive repairs and maintenance. Also, the proposed system relies on different platforms that required to be consolidated for facility technicians and managers end-users. Therefore, the authors will consider these limitations and expand the study as a case study in future work.

This paper assists in a proactive manner to decrease the lack of knowledge of the required maintenance to MEP elements that leads to a lower life cycle cost. These MEP elements have a big share in the operation and maintenance costs of building facilities.

The purpose of this paper is to facilitate the process of monitoring construction projects. Classic practice for construction progress tracking relies on paper reports, which entails a serious amount of manual data collection as well as the effort of imagining the actual progress from the paperwork.

This paper presents a new methodology for monitoring construction progress using smartphones. This is done by proposing a new system consisting of a newly-developed application named “BIM-U” and a mobile augmented reality (AR) channel named “BIM-Phase”. “BIM-U” is an Android application that allows the end-user to update the progress of activities onsite. These data are used to update the project’s 4D model enhanced with different cost parameters such as earned value, actual cost and planned value. The “BIM-Phase” application is a mobile AR channel that is used during construction phase through implementing a 4D “as-planned” phased model integrated with an augmented video showing real or planned progress.

The results from the project are then analysed and assessed to anticipate the potential of these and similar techniques for tracking time and cost on construction projects.

The proposed system through “BIM-U” and “BIM Phase” exploits the potential of mobile applications and AR in construction through the use of handheld mobile devices to offer new possibilities for measuring and monitoring work progress using building information modelling.

Decisions by construction contractors to bid (or not to bid) require the thorough assessment and evaluation of factors relevant to the decision, as well as the quantification of their combined impact, to produce successful bid/no-bid decisions. The purpose of this study is to present a fuzzy fault tree model to assist construction contractors to more efficiently bid for future projects.

The proposed model consist of two stages: first, identification of the factors that affect bidding decision using a questionnaire survey after an extensive literature review, and second, usage of the identified factors to build a fuzzy fault tree model to simulate the bidding decision.

A list of 15 factors that affect bid/no-bid decisions was identified. Analysis of factors revealed that the highest-ranking factors were related to financial aspects of the project. A case study is presented to demonstrate the capabilities of the model, and a fuzzy important analysis is performed on the basic events to demonstrate the differences between three contractors’ bid/no-bid decisions. The results reveal that there is variation between the decisions of each contractor based on their willingness to participate. Besides, the influence of evaluation factors on the final decision for each contractor is different.

The study contributes to the body of knowledge on tendering and bidding practices. The proposed model incorporated the fuzzy set theory, which suits human subjectivity. The proposed methodology overcomes the limitations of previous models as it can, using the linear pool opinion principle, combine and weigh the evaluations of multiple experts. In addition, the model is convenient for situations where historical data are not available.

The purpose of this paper is to expand the benefits of building information modeling (BIM) to include data analytics to analyze construction project performance. BIM is a great tool which improves communication and information flow between construction project parties. This research aims to integrate different types of data within the BIM environment, then, to perform descriptive data analytics. Data analytics helps in identifying hidden patterns and detecting relationships between different attributes in the database.

This research is considered to be an inductive research that starts with an observation of integrating BIM and descriptive data analytics. Thus, the project’s correspondence, daily progress reports and inspection requests are integrated within the project 5D BIM model. Subsequently, data mining comprising association analysis, clustering and trend analysis is performed. The research hypothesis is that descriptive data analytics and BIM have a great leverage to analyze construction project performance. Finally, a case study for a construction project is carried out to test the research hypothesis.

The research finds that integrating BIM and descriptive data analytics helps in improving project communication performance, in terms of integrating project data in a structured format, efficiently retrieving useful information from project raw data and visualizing analytics results within the BIM environment.

The research develops a dynamic model that helps in detecting hidden patterns and different progress attributes from construction project raw data.

The purpose of this paper is to highlight the sustainability benefits of using demolition and industrial wastes as a replacement for aggregates and cement in traditional concrete mixes.

Crushed concrete from demolition sites served as a replacement for fine and coarse aggregate in some of the mixes at various ratios. In addition, ground granulated blast furnace slag, metakaolin, silica fume, and fly ash each served as a cement replacement for cement content in the mixes tested in this research at various rates. Compression strength tests, permeability, and thermal expansion tests were performed on various mixes to compare their performance to that of traditional mixes with natural aggregate, and with no cement replacement.

The compressive strength results indicated the suitability of using such demolition wastes as replacements in producing green concrete (GC) without hindering its mechanical characteristics significantly. In addition, the results indicated an enhancement in the mechanical characteristics of GC when replacing cement with pozzolanic industrial wastes and byproducts.

The research assesses the utilization of sustainable GC using recycled waste aggregate and byproducts.

Facility management gained profound importance due to the increasing complexity of different systems and the cost of operation and maintenance. However, due to the increasing complexity of different systems, facility managers may suffer from a lack of information. The purpose of this paper is to propose a new facility management approach that links segmented assets to the vital data required for managing facilities.

Automatic point cloud segmentation is one of the most crucial processes required for modelling building facilities. In this research, laser scanning is used for point cloud acquisition. The research utilises region growing algorithm, colour-based region-growing algorithm and Euclidean cluster algorithm.

A case study is worked out to test the accuracy of the considered point cloud segmentation algorithms utilising metrics precision, recall and F-score. The results indicate that Euclidean cluster extraction and region growing algorithm revealed high accuracy for segmentation.

The research presents a comparative approach for selecting the most appropriate segmentation approach required for accurate modelling. As such, the segmented assets can be linked easily with the data required for facility management.

The housing sector in Egypt represents a considerable share of the gross domestic product (GDP) and accordingly the economy. Further, it is considered vital for any population around the world, because it provides the shelter needed by people. Egyptian housing market is facing many problems which need to be solved. The paper aims to discuss these issues.

This research reviews and analyzes the Egyptian public and private housing market’s key variables. As such, it highlights the importance of informed decision making through detailed analysis and study of the market, especially when planning for the future by any housing market stakeholder. The research proposes the use of system dynamics (SD) modeling to analyze the market by creating a stock and flow model using STELLA modeling and simulation software.

The results reveal that the expected newly established families will be nearly 800,000 families in year 2015/2016. Out of these numbers, 600,000 families require economic housing units, while the expected supply is nearly 300,000 units.

A study is made for the economic housing market, which is a very big housing market and population segment that has been suffering from negligence for years.

The level of mental alertness among students who are in need of study in the preparatory year – Imam Abdul Rahman Bin Faisal University. The level of self-competence of female students who are in need of study in the preparatory year – Imam Abdul Rahman Bin Faisal University. To learn about the relationship between mental alertness and self-competence among students who are in need of study in the preparatory year – Imam Abdul Rahman Bin Faisal University.

The researchers used the experimental method by following the experimental design with two measurements, tribal and posterior, for two groups, one experimental and the other control, due to its suitability to the nature of this research. Research sample: the research community included female students stumbling in the scientific path of Rayyan, the second semester of the academic year 2020–2021 AD, where the researchers selected the research sample in a deliberate manner from the stalled students of 25 students and a random sample of 10 female students was withdrawn to conduct the exploratory study. The number of the basic research sample is 15 female students. Reasons for selecting the search sample: all of the sample members are female students stumbling in the scientific path of Al-Rayyan for the academic year 2020–2021 and all sample members agree to apply the research.

The researchers refer to this to the virtual psychological guidance program-specific objectives, as well as the follow-up of the scientific principles and principles and the legalization of the measurement tools used in the research and the diversity of the program within the parts of the program and the training sought to develop the ability to manage pressures and emotions and training in social skills, awareness and self-awareness and the use of exercises relaxation, breathing and modern self-help, as well as the use of different methods and techniques such as the method of dialogue, discussion, awareness of feelings and problem-solving, which led to a change in behavior and personal characteristics, which led to a change in behavior and personal characteristics, leading to the development of behavior and personal characteristics.

The study aims to design a hypothetical psychological counseling program as a method of health care and its effect on the level of mental alertness and the level of self-efficacy of students who have struggled to study at the Deanship of the preparatory year – Imam Abdul Rahman bin Faisal University. The researchers used the experimental approach and applied this study on a sample of 25 students from the students who failed to study at the Deanship of the preparatory year for the second semester of the academic year 2020–2021 AD and the most important results were that the psychological counseling program as a method of health care had a positive effect on improving the level of mental alertness and the level of self-efficacy among the students who had failed to study. There is a positive correlation between the level of mental alertness and self-efficacy of struggling students as a way of health care.

The construction sector is a major contributor to the Egyptian economy and gross domestic products, plus considered as one of its fastest-growing sectors. Various deficiencies such as low productivity, delays, cost overrun, poor quality, etc. have plagued the construction sector, leading to undesirable project performance across Egypt and several other countries. One of the best methods to measure and improve performance evaluation and consequently a step toward industry improvement is key performance indicators (KPIs). The paper aims to discuss this issue.

A questionnaire survey was conducted to identify the importance index for 35 KPIs. The KPIs were identified and selected based on an extensive literature review and one on one interview with expert construction professionals. Moreover, exploratory factor analysis method has been used to analyze and determine the inter-relationships between the set of indicators.

Seven indicators that have the highest importance indicator were selected to create project overall performance indicator assessment models to consistently measure the performance of different construction projects. Seven equations are introduced reflecting the output of the research while considering both organization size and project type.

The proposed evaluation assessment models can be used to: evaluate the relative success of projects; indicate the areas of strengths and weaknesses in performance and establish company benchmarking. Three projects were selected to validate the performance evaluation models.

This paper provides mathematical assessment models for evaluating overall construction project performance in Egypt, taking into consideration organization size and project type. Previously published research works on the subject matter are quite limited, and frequently deal with only one or two selected aspects.