Aug 2026· IOP Conference Series: Earth and Environment· Vol 1656· 0 citations· 15 references
Physics
Abstract
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.
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
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.
Zarith Anisa Idris, M. Awang, N. Hamidon et al.· International journal of res...· 0 citations
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
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
The application of Building Information Modeling (BIM) to existing buildings has gained relevance as a strategy to support asset management, especially in the operation and maintenance (O&M) phases. However, in buildings already in use, its adoption is limited by the absence of reliable “as-built” documentation, fragmented technical records, and renovations not incorporated into the documentary archive. This study examines how BIM-based “as-is” modeling can support the O&M of existing university buildings when reliable “as-built” documentation is unavailable. To this end, an applied, qualitative, and descriptive case study was developed, with Blocks A and B of a student housing complex linked to a Brazilian federal public university as the unit of analysis. The methodological procedure included a critical analysis of technical documentation, field inspections, measurements, photographic records, architectural, structural, and plumbing BIM modeling, integration of the models in a federated environment, multidisciplinary coordination, and clash detection. The results revealed inconsistencies between original drawings, undocumented renovations, and the actual condition of the buildings, indicating that the fragility of the documentary archive is a barrier to BIM adoption in O&M. It was found that, without “as-built” models, “as-is” modeling requires greater technical effort, field verification, and reconciliation among different sources of information. As a contribution, the study demonstrates that BIM-based “as-is” modeling can convert legacy documentation, field surveys, and discipline-specific models into an integrated information base on the built asset, supporting interventions, building systems coordination, and lifecycle management.
K. Araújo, Ernandes Resende da Silva, Maria Carolina Carreira Barbosa et al.· Revista de Gestão e Projetos· 0 citations
Manufacturing companies often register process deviations in operational systems while managing continuous improvement (CI) actions through separate spreadsheets, templates and meeting records. This fragmentation weakens traceability between detection, prioritisation, execution and verification. This paper presents Digital for Continuous Improvement (D4CI), a configurable digital CI system developed from eight literature-derived requirements covering event traceability, detection and escalation rules, transparent assessment, workflow routing, planning, verification and interoperability. The architecture combines data input, relational storage, application logic and user interfaces within a shared information model. Deviations are recorded against process targets or expected conditions, while recurrence criteria consolidate related deviations into occurrences. Impact, Effort and Waste–Cost inputs are stored with the calculated scores and used to recommend an Action for Immediate Improvement, Quick Win or A3 pathway. Planning, execution and verification records remain linked to the originating problem. D4CI was deployed in a metalworking company with established Lean routines and evaluated through implementation records, observation of system use and consolidated feedback. The requirement–function mapping confirmed coverage of the eight design requirements. Deployment evidence indicated centralised problem records, traceable prioritisation criteria, shared visual follow-up of open actions and retrieval of completed CI records. Operational effects require longer observation and comparative performance data.
Unknown authors· Applied System Innovation· 0 citations
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