The construction industry builds infrastructure and strengthens the global economy, but it struggles with cost overruns. This systematic study used scientometrics and Social Network Analysis to examine the multifaceted factors causing construction project cost escalations. After reviewing 405 scholarly works, the research mapped and analyzed 66 interconnected cost overrun factors in 69 high-impact studies between 2000 and 2024. To uncover the patterns, the study used two main research methods. First, this study applied a scientometric analysis that reviewed trends and gaps from the previous studies. Second, this study used Social Network Analysis (SNA) to examine how different factors were connected and which factors had the strongest influence on cost overruns. The methodology comprised a systematic literature search, document selection, scientometric analysis, factor standardization, and an SNA application. Seven critical drivers with high network centrality were identified: planning and scheduling issues, project estimation inaccuracies, design inefficiencies, negative weather conditions, scope definition challenges, contractual ambiguities, and unforeseeable site conditions. By applying the SNA degree centrality (DC), the analysis quantified the significance of each factor within the network. With the use of this dual analysis, a novel mapping of the main causes of cost overruns was produced, leading to the discovery of seven core factors that significantly affected project outcomes, including planning and scheduling issues, project estimation problems, and design inefficiencies. The findings advance the knowledge of the dynamics of cost overruns and offer practical insights for enhancing cost management practices in the construction industry.

Mega-facility management has long been inefficient due to manual, reactive approaches. Current facility management systems face challenges such as fragmented data integration, limited predictive systems, use of traditional methods, and lack of knowledge of new technologies, such as Building Information Modeling and Artificial Intelligence. This study examines the transformative integration of Artificial Intelligence and Digital Twin technologies into Building Information Modeling (BIM) frameworks using IoT sensors for real-time data collection and predictive analytics. Unlike previous research, this study uses case studies and simulation models for dynamic data integration and scenario-based analyses. Key findings show a significant reduction in maintenance costs (25%) and energy consumption (20%), as well as increased asset utilization and operational efficiency. With an F1-score of more than 90%, the system shows excellent predictive accuracy for equipment failures and energy forecasting. Practical applications in hospitals and airports demonstrate the developed ability of the platform to integrate the Internet of Things and Building Information Modeling technologies, shifting facilities management from being reactive to proactive. This paper presents a demo platform that integrates BIM with Digital Twins to improve the predictive maintenance of HVAC systems, equipment, security systems, etc., by recording data from different assets, which helps streamline asset management, enhance energy efficiency, and support decision-making for the buildings’ critical systems.

Risk identification is integral to construction management. The severe consequences of identified risk factors on time and cost performance can be addressed by improved risk management. The identification and preparation of responses to risk factors affects the risk management process directly and indirectly. Recent changes in the valuation of the Egyptian currency against foreign currencies during the last year and measures to cut down on the importation of engineering materials and equipment have had significant effects on the existing and recognized regulations concerning construction in Egypt. A pilot survey with expert engineers is a crucial step in completing research. In this research study, 15 experts were asked to discuss the collected risk factors from previous studies to verify the workability of these factors in Egypt. Thirty-five risk factors were selected during the pilot survey, which was distributed to 95 participants. To facilitate the analysis process, the collection was based on a five-point Likert scale. Therefore, redefining and arranging the risks according to the current circumstances is the main objective of this study. The proposed model identified different high-risk factors that could cumulatively affect overall performance, such as funding problems from contractors, material price fluctuations, unrealistic estimates of the duration of project activities, and shortages of construction materials in the market. Thus, to help stakeholders achieve project success, these high-risk factor components should be identified and controlled duly.

Facility Management (FM) has increasingly focused on integrating Building Information Modeling (BIM) with the Internet of Things (IoT), known as digital twins, in large-scale development projects. Effective BIM integration in FM requires improved cooperation among participants across various project stages. This digital revolution aims to enhance planning, construction, and asset management efficiency, benefiting all parties. However, BIM utilization in FM is limited by incomplete owner understanding, insufficient data accessibility, and stakeholders’ unfamiliarity with BIM procedures and standards. Despite recognizing BIM’s significance, the FM industry faces significant implementation challenges. Facility managers often lack a comprehensive understanding of BIM’s benefits in streamlining operations and enhancing cost efficiency, as well as the necessary skills for its use. Addressing these barriers requires developing an Employer’s Information Requirements (EIR) document at a project’s outset, providing a strategic plan and vision for all involved parties. BIM and IoT are pivotal technologies for transitioning to efficient building operations and crucial for reducing time, costs, and operational challenges throughout any project. This research aims to establish a digital trio workflow, integrating BIM, EIR, and IoT to maximize stakeholder benefits. It explores how preparing the EIR through stakeholder communication can improve design processes, sustainability, efficiency, cost, and time, especially for megaprojects.

In response to the critical need for enhanced resource management in the construction industry, this research develops an innovative, integrated methodology that synergistically combines Agent-Based Modeling (ABM), Building Information Modeling (BIM), and Bluetooth Low Energy (BLE) technologies. Central to our approach is a sophisticated technological framework that incorporates a Client Early Warning System (CEWS) and a Decision Support System (DSS). These systems facilitate real-time monitoring and management of construction resources, ensuring operational efficiency and optimal resource utilization. Our methodology was empirically validated through a comprehensive case study at Helwan University’s College of Engineering. The results demonstrated a significant enhancement in operational efficiency, particularly in resource allocation and progress tracking. Key practical outcomes include the development of a CEWS master dashboard that provides in-depth, real-time insights into project metrics. This dashboard was crucial for managing compliance with health protocols during the COVID-19 pandemic, showcasing the framework’s adaptability to critical health standards. Further, the integration of indoor tracking technology revolutionized attendance tracking by replacing outdated manual methods with automated processes. This capability not only underscores the practical applicability of our research but also establishes a new benchmark for future technological advancements in construction project management. Our study sets the stage for subsequent innovations, paving the way for a more connected, efficient, and data-driven approach in the construction industry.

This study delves into the dynamics of ‘Variations’ and ‘Claims’ in construction projects. This study aims to identify, categorize, and devise mitigation strategies for critical types of variations and claims that are aligned with the contract’s FIDIC conditions. The research draws on input from construction industry professionals, including contract administrators and project managers, and focuses on the MENA region. The region’s extensive adoption of FIDIC standards and the rapidly growing construction sector drive this choice. Data collection encompassed a questionnaire distributed to 80 industry experts, predominantly through interviews focused on countries such as Saudi Arabia, the UAE, Kuwait, and Egypt. Utilizing SPSS-V.25 for statistical analysis, this study uncovers the most prevalent and impactful causes of variations and claims, highlighting the critical need for managerial intervention. A key feature is the integration of scientometric analysis into a quantitative finding. Implementing a k-means clustering analysis is a significant addition to the methodology. The survey had high internal consistency with a Cronbach’s alpha of 0.97, and the respondents reported frequent and significant claims such as delayed drawings, ambiguous documents, and client changes. The results showed that effective claims management requires clear communication and balanced contracts, while poor design and contract documentation cause variations and claims. The correlation analysis showed strong positive correlations between claim types and causes. To reduce claims and address these factors, most respondents said the survey could predict and reduce claims.