Ahmed Mohammed Abdelalim

Bridge inspection become essential for ensuring structural safety and longevity. Recently, Artificial Intelligence (AI) has become significant in improving bridge assessment by supporting different approaches that enhance maintenance planning and minimize associated costs. Objective of this study is to investigate the more accurate and applicable AI-driven technique for assessing reinforced concrete bridges. Therefore, the presented study proposed two different techniques to estimate the current Bridge Condition Rating (BCR) of reinforced concrete (R.C.) bridges: 1) fuzzy decision-making and 2) Markov chain modelling. This paper focused on a corrosion attack as the main defect utilized to assess the bridge condition. The dual methods depend on visual inspection, applying field and laboratory tests, and reviewing the historical data of the inspected bridge to estimate its condition rating. The fuzzy decision model is used to find a correlation between corrosion degree and concrete surface condition to estimate the Bridge Condition Rating (BCR). The Markov chain model is applied to predict the current and the future Bridge Condition Rating (BCR) and when the bridge will reach the critical condition. The service life for each bridge element is evaluated due to the total time required for corrosion based on carbonation and chloride attack. The proposed models are validated through a real case study of R.C. bridge, and the results demonstrate that the fuzzy model is less accurate compared to the Markov chain. The introduced models provide valuable insights to provide proper Maintenance, Repair, and Replacement (MRR) decisions for the bridges.

Artificial Intelligence (AI) has recently played a crucial role in improving bridge assessment through diverse methodologies to optimize maintenance strategies and reduce costs. Therefore, the current study proposed two different methods to estimate the current condition rating of R.C. bridges by 1) Fuzzy Decision-Making; and 2) Markov Chain Modelling. The purpose of this study is to investigate the more applicable and accurate technique due to AI for reinforced concrete bridge assessment. The current study focused on corrosion as the main defect used to estimate the bridge condition rating. The dual methods depend on visual inspection, applying field and laboratory tests, and reviewing the historical data of the inspected bridge to estimate its condition rating. The fuzzy decision model is applied to find a correlation between corrosion degree and concrete surface condition to estimate the condition rating. The Markov chain model is used to predict the future condition rating for the whole bridge and when it will reach the critical condition. The service life for each bridge element is calculated due to carbonation and chloride attack. The Life 365 model is applied to estimate the service life due to chloride ingress. The proposed system is validated through a real case study, and the results show that the fuzzy is less accurate compared to the Markov Chain. The introduced models are expected to provide proper Maintenance, Repair, and Replacement (MRR) decisions for the bridges.

Efficient construction site layout planning is crucial for enhancing productivity and safety. This study addresses the complexities of combinatorial optimization in site layout, which involves strategically placing temporary structures while managing multiple objectives. Existing research has often focused on minimizing travel distances between two facilities, neglecting important factors like costs and safety relationships. This research develops a model with two optimization objectives: minimizing travel distance between facilities to lower transportation and construction costs traveling distance and minimizing risks related to interaction frequency between facilities, as increased interaction raises collision probabilities. A genetic algorithm (GA) is employed as the heuristic optimization method. The study includes a case study to validate the proposed model, demonstrating its effectiveness in providing practical solutions for site layout planning by incorporating cost and safety relationships considerations. Offering a more comprehensive approach, this research aims to enhance decision-making and improve overall project execution on construction site layouts.

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.

The current research investigates cost overrun factors in structural projects, focusing on the Middle East and North Africa (MENA) region using Egypt as a model. A systematic literature review was conducted, analyzing 405 research papers published between 2000 and 2024, from which 69 relevant papers were selected to identify 48 key factors contributing to cost overrun. Using K-means clustering, these factors were grouped into three clusters based on their probability and impact, which were classified for their risk levels. To ensure robust analysis, a survey was conducted to gather expert opinions, resulting in 369 valid responses from owners, contractors/subcontractors, and management firms/consultants. The fuzzy group decision-making approach (FGDMA) was conducted to rank all 48 factors, offering a detailed assessment of their relative importance. Based on these rankings, the top 20 factors were identified for analysis to examine variations in stakeholder priorities, capturing differences in perspectives among multi-stakeholders. Sensitivity analysis and Tornado charts explored the critical variations among stakeholders, with management firms/consultants and owners prioritizing design-related risks, such as inconsistencies and delays in approvals, while contractors/subcontractors focused more on material waste. This novel integration presents a structured approach for analyzing, prioritizing, and mitigating cost overrun risks, offering a comprehensive framework that provides practical insights for stakeholders to improve cost and risk management strategies.

This study examines cost overrun factors in construction projects, focusing on the MENA region using Egypt as a case study. A systematic literature review was conducted, analyzing 405 research papers published between 2000 and 2024, from which 64 relevant papers were selected to identify 48 key factors contributing to cost overruns. Using K-Means clustering, these factors were grouped into three clusters based on their probability and impact, allowing for their classification by risk level. To ensure robust analysis, a survey was conducted to gather expert input, resulting in 369 valid responses from clients, contractors, and consultants. The Fuzzy Group Decision-Making Approach (FGDMA) was used to rank all 48 factors, offering a detailed assessment of their relative importance. Based on these rankings, the top 20 factors were identified for further analysis to examine variations in stakeholder priorities, capturing differences in perspectives among clients, contractors, and consultants. Sensitivity analysis and Tornado charts revealed critical variations among stakeholder, with consultants and clients prioritizing design-related risks, such as inconsistencies and delays in approvals, while contractors focused more on material waste. The findings present a structured approach for analyzing and prioritizing cost overrun risks, providing practical insights for construction professionals to improve cost and risk management strategies.

Mega facility management has long been inefficient due to manual, reactive approaches. This study examines the transformative integration of AI and DT technologies into Building Information Modeling (BIM) frameworks using IoT sensors for real-time data collection and predictive analytics. The 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 the ability of the platform to integrate IoT and BIM technologies, shifting facilities management from reactive to proactive. This paper presents a demo platform that integrates the BIM model with DT to improve the predictive maintenance as HVAC systems, equipment, security systems, etc., by recording data from different assets which help streamline asset management, enhance energy efficiency and support decision-making for the buildings’ critical systems.

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.

Building Information Modeling (BIM) is a revolutionary method in the construction industry that enhances project coordination, minimizes mistakes, and enables efficient project completion. An essential aspect of achieving successful BIM implementation is the creation of a BIM Execution Plan (BEP). The BEP facilitates effective communication, defines roles and responsibilities, and establishes standardized processes for all stakeholders involved. Although BIM is being used more widely, the absence of standardized BEPs frequently leads to inefficiencies in projects, especially those of significant scale [3]. This paper introduces the development of a new dynamic platform designed specifically for automating and standardizing the creation of BEPs. The platform aligns with ISO 19650 standards and offers customizable workflows to suit various project needs, improving collaboration, data management, and project outcomes. In addition, the paper emphasizes the advantages of utilizing this platform, including the efficient creation of BEPs, improved collaboration through centralized data management, and the ability to tailor BEPs to specific project requirements. The platform's user-friendly interface and extensive data management capabilities not only increase efficiency but also minimize risks and enhance project results. The survey results indicate that the BEP platform is effective in resolving common issues encountered in the creation of BEPs, such as miscommunication and inconsistencies. Additionally, it helps to improve project outcomes and reduce risks.

The construction industry builds infrastructure and strengthens the global economy, but it struggles with cost overruns. This systematic study uses scientometrics and Social Network Analysis to examine the multifaceted factors causing construction project cost escalations. After reviewing 405 scholarly works, the research maps and analyzes 66 interconnected cost overrun factors in 69 high-impact studies between 200 and 2024. To uncover the patterns, the study used two main research methods. First, this study applied a scientometric analysis that reviews trends and gaps from the previous studies. Second, this study used Social Network Analysis (SNA) to examine how different factors are connected and which factors have the strongest influence on cost overruns. The methodology comprises a systematic literature search, document selection, scientometric analysis, factor standardization, and 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 SNA Degree Centrality (DC) the analysis quantifies the significance of each factor within the network. With the use of this dual analysis, a novel mapping of the main causes of cost overrun was produced, leading to the discovery of 7 core factors that significantly affected project outcomes, including planning and scheduling issues, project estimation problems, and design inefficiencies. The findings advanced the knowledge of the dynamics of cost overrun and offered practical insights for enhancing cost management practices in the construction industry.