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.

This study delves into the dynamics of 'Variations' and 'Claims' within construction projects, specifically under the FIDIC-Red Book 1999 framework. The study aims to identify, categorize, and devise mitigation strategies for key types of variations and claims aligning with the FIDIC conditions of contract. The research is drawing on inputs from construction industry professionals including contract administrators and project managers, focusing on the MENA region. This choice is driven by the region's extensive adoption of FIDIC standards and its rapidly growing construction sector. Data collection encompassed a questionnaire distributed to 80 industry experts predominantly through interviews that focused on countries like Saudi Arabia, UAE, Kuwait, and Egypt. These locations were chosen to reflect diverse construction practices and the involvement of international firms. Utilizing SPSS-V.25 for statistical analysis, the 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 for a quantitative finding. A significant addition to the methodology is the implementation of a k-means clustering analysis. The survey had high internal consistency with a Cronbach's alpha of 0.97, and respondents reported frequent and significant claims like 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. Spearman's correlation showed strong positive relationships between certain claim types and causes, emphasizing the importance of dealing with these factors to reduce claims. Most respondents said the survey could predict and reduce claims.

This study explores the increasing adoption of virtual alternative dispute resolution (VADR) in the construction industry, enhancing efficiency and accessibility in dispute resolution. VADR is crucial for streamlining processes and reducing participation barriers. The study aims to investigate the critical success factors (CSFs) influencing the adoption of VADR in the construction sector. Given the rising importance of VADR technologies, understanding the key factors driving their acceptance is crucial. The background highlights the growing reliance on innovative technologies to boost operational efficiency and decision-making processes. The data for the study were collected using a cross-sectional design with online structured survey questionnaire (N = 97) from diversified construction industries. Using Smart PLS 4, structural equation modeling (SEM) was employed to test the validity, reliability, and proposed hypotheses of the study. The results showed that cost factors had the greatest impact on VADR acceptance, followed by user competence and training, procedural adaptability, and technological infrastructure. Logistical assistance and legal frameworks also had a considerable favorable impact. However, stakeholder buy-in had no significant influence on VADR implementation. The implications indicate that economic feasibility, logistical readiness, flexible procedures, supportive legal contexts, and user skills are significant factors for successful VADR integration. Governments and organizations should deploy VADR technologies to encourage innovation and operational improvement in the construction industry.

Construction equipment monitoring has shifted from manual logbooks and early telematics toward intelligent digital systems, driven by the need to reduce delays in obtaining equipment data and improve decision-making on construction sites. This study examines research on construction equipment monitoring and tracking published between 2000 and 2026 to identify the evolution, major themes, and emerging directions in the field. A scientometric and bibliometric analysis was conducted on 1093 bibliographic records retrieved from Scopus and Web of Science on 20 May 2026. The annual publication trend was evaluated through 2025, the last complete publication year, while records indexed in 2026 were retained for the remaining corpus-level analyses. The datasets were preprocessed and analyzed using Bibliometrix, VOSviewer, and CiteSpace to examine publication trends, keyword networks, collaboration patterns, citation structures, and research clusters. The keyword network was interpreted through six major thematic clusters, which were synthesized into four broader knowledge streams covering operations and sensing, AI-based perception, safety monitoring, and BIM/digital-twin integration. The results show a substantial increase in the representation of AI- and perception-related research across the later publication periods, reflecting a transition from basic sensor-based approaches toward more intelligent and connected site systems. The study identifies leading contributors and comparatively underdeveloped research priorities, particularly real-time idle-state detection, multimodal data fusion, multi-site validation, and the integration of monitoring outputs with BIM and digital-twin decision-support environments.

Effective construction-equipment monitoring requires persistent equipment-level information linking visual observations to utilization and planning decisions. This study develops and evaluates an identity-centered framework integrating ten-class YOLOv8m detection, ByteTrack tracking, AprilTag-assisted Global ID reconciliation, scale-normalized motion-state estimation, utilization accounting, and rule-based reallocation support. The selected detector achieved 90.80% micro-F1 on the held-out ConstructionXC7C benchmark (757 images, eight annotated categories). In a matched comparison, selected-checkpoint micro-F1 reached 92.52%, exceeding the original checkpoint by 16.11 percentage points. Across 6000 experimental video frames, 5757 of 5992 annotated excavator observations were matched, with 99.81% adjacent camera–local ID consistency. A controlled 192-pair simulated-transition study associated all 144 same-object pairs using ideal deterministic identity tokens, compared with 99 using a tuned appearance-only baseline; regression tests verified preservation of accumulated equipment histories. In a separate matched analysis of 1040 observations, temporal activity modeling improved idle-state discrimination and reduced video-level sampled-utilization mean absolute error by 41.92 percentage points, achieving an error of 15.11 percentage points. Controlled verification further demonstrated state-service functionality and reviewable zone-reallocation advisories. These findings provide quantitative evidence of component-level performance and traceable monitoring-to-decision functionality, establishing a technical foundation for subsequent construction-site evaluation.