2026· International journal of research and innovation in social science· Vol 10, pp. 6857-6868· 0 citations
Abstract
Building Information Modelling (BIM) is increasingly positioned as a digital information environment for facilities management (FM), asset management and operation and maintenance (O&M) decision-making. Its operational value, however, remains constrained by fragmented information exchange, incomplete asset information, inconsistent requirements, limited facility manager involvement and weak integration between BIM and FM systems. This paper presents a bounded, corpus-based systematic literature review of 51 documents covering BIM-FM integration, asset information delivery, Industry 4.0 technologies, digital twins and visual analytics. The starting repository was pre-assembled rather than generated through a new bibliographic database search. To improve methodological transparency, the review applies explicit eligibility criteria, a consolidated Boolean verification string, a documented corpus-selection process and qualitative thematic synthesis. Four themes emerged: interoperability and information exchange; asset information quality, data requirements and early FM involvement; BIM convergence with Industry 4.0 technologies and digital twin-enabled FM; and visual analytics, systems-centric modelling and fault diagnosis. The synthesis also distinguishes the implementation emphasis reported in developed-country and developing-country contexts. The paper contributes a provisional, literature-derived seven-stage framework for BIM-enabled data integration and visual analytics in FM. The framework has not yet been validated through practitioner or expert assessment and is therefore presented as a testable conceptual proposition rather than an implementation-ready model. The findings are intended to support bounded theoretical synthesis and future empirical validation, not statistical generalisation across the entire BIM-FM domain.
Quality management in mega construction projects is increasingly supported by digital technologies, yet quality information often remains fragmented across inspection systems, reality-capture platforms, Building Information Modelling (BIM) environments and enterprise systems. This fragmentation limits traceability, delays corrective actions and weakens the connection between quality performance, contractual obligations and financial accountability. This study develops a closed-loop digital QA/QC framework that integrates BIM, mobile field inspection, reality capture, artificial intelligence (AI) augmentation and enterprise resource planning (ERP) within a unified governance architecture. Following a Design Science Research approach, the study proposes a seven-layer framework linking quality events to procurement, financial control, and project-management processes while supporting role-based decision-making through data democratisation mechanisms. The framework extends conventional ERP-enabled quality management by explicitly incorporating procurement (MM) and financial-control (FI/CO) functions, including supplier-quality management, cost-of-poor-quality tracking and quality-linked payment governance. An illustrative project scenario and sensitivity analysis are used to demonstrate the application of the proposed KPI and evaluation structure. By treating integration as the primary design objective, the framework provides a foundation for enterprise-wide digital quality management, lifecycle information continuity and digital-twin readiness in mega construction projects. The contribution itself is evaluated through an illustrative Design Science Research demonstration and an assumption-bounded sensitivity analysis rather than through field data, with future empirical validation specified through a controlled before-and-after case-study protocol.
The construction industry continues to encounter challenges in maintaining construction quality and controlling project costs due to fragmented inspection processes, paper-based documentation, and inefficient information management. Conventional quality inspection practices are often associated with delayed reporting, limited traceability, human error, and ineffective communication among project stakeholders, leading to rework, schedule delays, and increased project costs. This study proposes a BIM-integrated Digital Inspection and Testing System to improve construction quality management through the integration of Building Information Modelling (BIM), Novade, and business intelligence platforms (Microsoft Power BI and Google Data Studio). The study adopted the System Development Life Cycle (SDLC) methodology to analyse the existing inspection workflow, identify its limitations, and develop a conceptual digital framework that supports real-time inspection, centralized information management, BIM-based quality tracking, and analytical reporting. The proposed system establishes an end-to-end digital workflow that integrates field data collection, cloud-based information storage, BIM-enabled traceability, and interactive dashboards to enhance inspection efficiency, stakeholder collaboration, and data-driven decision-making. Unlike existing studies that examine BIM, mobile inspection, or analytics independently, this framework integrates these technologies into a unified quality management ecosystem. Although the framework has not yet been implemented or empirically validated, it provides a practical foundation for future prototype development and industry application. The proposed system has the potential to reduce documentation redundancy, improve quality traceability, minimize rework, and strengthen construction cost control, thereby supporting the digital transformation of construction quality management.
Hanafi bin Ab. Haris, Norhazren Izatie Mohd, Hamizah Liyana Tajul Ariffin et al.· International journal of res...· 0 citations
Building maintenance is essential for sustaining operational efficiency and asset longevity, yet many organizations still rely on manual, fragmented processes. This study presents an integrated methodology that combines Building Information Modelling (BIM) with a custom desktop application to automate maintenance scheduling and support structured knowledge management. The approach follows a continuous data loop comprising BIM, Dynamo, Excel, and an application that enables seamless data exchange, real-time updates, and full traceability. The methodology was validated through a case study of a Bank facility in Egypt, focusing on door assets due to their critical security and operational roles. BIM data was extracted, enriched with manufacturer maintenance instructions, and processed within the custom application, which generated maintenance schedules. The system prioritized tasks based on service life, budget constraints, and asset criticality, while also capturing technician feedback to refine future planning. Implementation results demonstrated a 75% reduction in manual scheduling time, an increase in on-time task execution from 65% to over 92%, and complete coverage of maintenance data. Additionally, the solution improved traceability, transparency in budgeting, and stakeholder confidence. This research illustrates the potential of BIM-integrated, automation-ready maintenance systems to improve decision-making, reduce operational costs, and extend asset life cycles, particularly in high-security, high-access environments.
Ahmed E. Mansour, M. Elbehery, S. A. Ibrahim et al.· IOP Conference Series: Earth...· 0 citations
Common Data Environments (CDEs) have become a cornerstone of information management in BIM-enabled construction projects and a key enabler of ISO 19650 implementation. Within the context of sustainable and Smart Construction, CDEs integrate design, construction, and operational information, supporting informed decision-making throughout the building lifecycle. However, their effectiveness depends not only on software capabilities but also on the configuration of processes, roles, permissions, workflows, and information structures. This paper proposes an ISO 19650-compliant CDE implementation framework for university construction projects. The study combines a literature review on CDEs, BIM/FM, HBIM, and information management with two university case studies: a new laboratory and educational building and a specialized facility integrated with a historic campus. Based on the findings, an eight-step implementation model was developed, covering information requirements, CDE structure, information states (WIP, Shared, Published, Archive), roles, metadata, workflows, site data integration, and information audits. A mapping matrix linking ISO 19650 requirements with CDE configuration elements and an issue management workflow are also presented. Exploratory findings from the two case studies indicate that a properly configured CDE can help reduce information-related risks, improve issue management, and enhance decision traceability; these results should be regarded as illustrative and context-dependent rather than statistically generalized. Although environmental impacts were not directly measured, improved information management may indirectly contribute to more efficient resource use and more sustainable university infrastructure management.
A. Radziejowska, U. Kwast-Kotlarek· Sustainability· 0 citations
A digital methodology is developed that integrates laboratory test results with BIM-referenced assets and delivers the integrated information via interactive 3D-enabled dashboards and provides a practical pathway for laboratories to centralise, filter, and communicate test results without embedding full datasets into the BIM environment.
Francisco Andrade, João Ventura, Cristina Ribeiro et al.· Infrastructures· 0 citations
The increasing complexity of construction projects has made traditional planning methods inadequate for managing dynamic variables. In this context, the integration of building information modelling (BIM) and artificial intelligence (AI) has been increasingly investigated as a promising approach to improve estimation accuracy, decision-making, and sustainable project execution. This systematic literature review, conducted according to the PRISMA guidelines, analysed 47 articles on BIM-AI integration for construction cost and time planning, categorising them into three clusters: time-oriented, cost-oriented, and multi-objective planning. The reviewed studies indicate that BIM-AI workflows may improve planning efficiency, estimation accuracy, and resource allocation. A limited subset of studies directly incorporated energy consumption, carbon emissions, or lifecycle performance into the optimisation objectives. By contrast, broader benefits related to material waste, rework, equipment idle time, and safety were mainly inferred from improvements in scheduling and resource management rather than directly quantified across the reviewed studies. Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) emerged as the most frequently adopted and consistently reported techniques, while Revit was the most adopted BIM platform. Despite its potential, BIM-AI integration still faces challenges related to software interoperability, data quality, interdisciplinary coordination, and the limited integration of explicit sustainability indicators within optimisation models. Future research should focus on developing standardised and adaptable frameworks that jointly address cost, time, resource efficiency, environmental impact, and lifecycle performance across different construction contexts.
Serena Vitaliano, Stefano Cascone, C. Arcidiacono· Sustainability· 0 citations
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