This study explores integrating Building Information Modeling (BIM) and Value Engineering (VE) to enhance cost management and project value optimization in residential construction. The primary aim is to develop a comprehensive framework that synergizes 5th Dimension Building Information Modeling (5D BIM) with VE processes to identify and evaluate the most cost-effective construction alternatives. Employing a mixed-methods approach, this research includes an extensive review of existing practices, an integrated BIM-VE framework proposal, and the application of 5D BIM principles to improve visualization, cost estimation, and scheduling. The findings reveal that integrating BIM and VE significantly enhances project efficiency, quality, and cost-effectiveness. Specifically, the case study of a twin villa project demonstrates a 42% reduction in floor material costs and a 30% reduction in door material costs, resulting in an overall project cost savings of 35%. Additionally, the integrated approach contributes to a 15% reduction in project duration and a notable improvement in design quality and stakeholder collaboration. This research contributes significantly by providing a robust framework for BIM and VE integration, emphasizing its potential to revolutionize cost management practices in the construction industry. The novelty of this study lies in its detailed and practical approach to merging BIM with VE, offering a viable solution for resource optimization and sustainable building practices. This study highlights the transformative potential of BIM-VE integration, advocating for its broader adoption to achieve superior project outcomes.

This study identifies a critical knowledge gap, revealing how the deterioration of roads, compounded by extensive usage and additional factors, poses significant risks to the road networks’ functionality. Without a robust fund allocation and prioritization strategy, the extent of this risk may be overlooked, adversely affecting the performance of essential infrastructure elements. Our research introduces an integrated decision-making model for existing road infrastructures to address this gap. This innovative approach combines a Geographic Information System (GIS)-based road management model with a fund allocation prioritization strategy, enhanced by an optimization engine via a genetic algorithm. The primary aim is to precisely determine Maintenance and Repair (M&R) interventions tailored to the condition states, thereby improving the Pavement Condition Index (PCI) of the road segments. The research is structured around three key objectives: (1) develop a detailed GIS-based road management database incorporating inspection data and attributes of road infrastructure for proactive M&R decision-making; (2) efficiently allocate funds to maintain service delivery on deteriorated roads; and (3) pinpoint the optimal type and timing of M&R interventions to boost the condition and performance of the road segments. Anticipated results will provide asset managers with a comprehensive decision support system for executing effective M&R practices.

The construction industry continues to grapple with persistently high accident rates and fragmented workforce management systems, where manual record-keeping and siloed data impede effective safety compliance. While digital interventions exist, they often rely on centralized databases that are vulnerable to manipulation and opaque. This systematic literature review critically examines the application of blockchain technology as a decentralized infrastructure for enhancing safety compliance in construction. Adhering to the PRISMA 2020 guidelines, this study synthesizes findings from 115 peer-reviewed articles (2020–2025) retrieved from Scopus, Web of Science, IEEE Xplore, and Google Scholar. The analysis focuses on three core mechanisms: (1) the creation of immutable, timestamped safety logs to prevent retroactive data tampering; (2) the integration of IoT sensors for real-time, trustless hazard monitoring; and (3) the deployment of smart contracts to automate compliance verification and incentive distribution. The review juxtaposes theoretical frameworks with empirical evidence from global case studies, including pilot projects in North America and the Asia-Pacific, to quantify benefits such as reduced reporting latency and improved data integrity. Despite promising results, the analysis reveals significant barriers to widespread adoption, notably the “oracle problem,” scalability limitations of consensus protocols, and the lack of legal recognition for blockchain records. This paper concludes that while blockchain is not a panacea, it offers a necessary layer of trust and accountability absent in traditional Common Data Environments (CDEs). Future research directions are proposed to address interoperability with BIM standards (ISO 19650) and to develop energy-efficient consensus mechanisms suitable for resource-constrained construction sites.

Rapid urbanization and environmental challenges necessitate innovative construction solutions in Egypt. This study presents a 6D Green Building Information Modeling (BIM) framework designed to enhance sustainability, reduce costs, and optimize construction processes. Integrating energy consumption, carbon footprint, and lifecycle performance metrics, the framework addresses critical gaps in Egypt’s construction sector, including limited technical expertise, high costs, and regulatory challenges. A mixed-methods approach was employed, combining global case study analysis, 3D modeling using Autodesk Revit, energy simulations with Autodesk Insight, and project scheduling through Primavera P6. Expert interviews with industry professionals further refined and validated the framework, ensuring its practicality and adaptability to Egypt’s unique socio-economic context. Key findings highlight the framework’s ability to reduce project costs through accurate 3D models, improve scheduling efficiency with 4D and 5D simulations, and enhance sustainability with 6D analyses of energy, water, and photovoltaic systems. The framework not only supports Egypt Vision 2030’s sustainability goals but also provides a clear, step-by-step implementation process using widely adopted tools. This research contributes a comprehensive, scalable model for sustainable construction, offering practical solutions to industry stakeholders. Its adaptable nature makes it relevant for other developing countries facing similar challenges, positioning BIM as a transformative tool for achieving greener, more efficient construction practices.

Industrial safety question answering requires access to the exact source material that supports an answer, not merely a related document. This study evaluates the retrieval stage of a local retrieval-augmented generation (RAG) system at the source page level. Eighteen documents on lockout/tagout, machine guarding, and personal protective equipment were prepared in raw, cleaned, and metadata-enriched forms. Best Matching 25 (BM25), latent semantic analysis (LSA), and reciprocal rank fusion (RRF) hybrid retrieval were tested on 54 questions: 36 answerable, 12 safety-related but unsupported, and 6 outside the covered topics. Raw BM25 achieved the highest mean reciprocal rank (0.492) and Hit@5 (0.722), cleaned BM25 achieved the highest Hit@3 (0.556) and answerability AUROC (0.765), and metadata-enriched BM25 achieved the highest Hit@10 (0.833). For raw text, the retrieval method affected reciprocal rank (Friedman χ² = 7.13, p = 0.028). Hybrid retrieval exceeded LSA after Holm correction (p = 0.011), whereas BM25 and hybrid retrieval did not differ (p = 0.302). Text preparation had no overall effect within BM25 (p = 0.220). The results show that document-level evaluation can overstate access to exact evidence. The study provides a repeatable procedure for source tracking, page-level relevance labeling, separate retrieval and generation evaluation, and refusal analysis. These design principles are also relevant to construction safety knowledge systems that combine regulations, accident reports, project data, and BIM-linked information. Because the relevance set is a model-assisted silver standard, the findings remain exploratory rather than expert-certified absolute retrieval accuracy. A controlled same-run extension with all MiniLM L6 v2 and BM25 + MiniLM RRF is reported separately; its observed fusion gains did not reach statistical significance on the 36 answerable questions. The work is intended to support safety engineers, researchers, and developers designing evidence-grounded industrial and construction safety information systems.