A critical approach to improving the maintenance management of hospital buildings is to engage the fourth industrial revolution (4IR) tools, which involve a framework that supports cyber-physical integration. Hence, this study aims to model the key adoption success factors of digital twin technology for the maintenance management of healthcare facilities in Nigeria. Following Delphi expert validation of the variables, the post-positivist philosophical perspective adopted in this study informed a quantitative research approach, which utilized a questionnaire survey. Using a purposive sampling technique, 442 respondents were selected from the Nigerian healthcare sector. Maintenance personnel, top management staff, and heads of departments in Nigerian hospitals were the respondents. Confirmatory factor analysis (CFA) using the SEM technique was used for data analysis to validate the expert findings. The CFA showed that the residual covariance estimates were within a reasonable range. Additionally, all parameter estimates for the eight (8) indicator variables were statistically significant and practicable, and the robust fit indices all met the cut-off index criteria. Considering these criteria, it was determined that the measurement model for DTMM adoption success factors adequately matched the sample data. The eight indicator variables for the success factors construct were: Provide training workshops for personnel, engage professionals in maintenance, provide support for maintenance staffers, Functional maintenance units, adopt good maintenance management practices, adopt maintenance software, plan to relocate resources effectively, and provide sufficient funds. The study enlightens healthcare stakeholders on the key strategies for efficient management of their constructed facilities.

Construction projects rely on extensive contract documents to govern payment, scheduling, change management, risk allocation, and dispute resolution. The scale and heterogeneity of these documents make contract administration largely manual, increasing the likelihood of misinterpretation, delayed actions, and governance inefficiencies. While recent advances in large language models have enabled automated clause extraction and classification, existing approaches remain disconnected from project performance metrics, standard-form contract requirements, and executable contract logic. This paper presents a retrieval-augmented contract intelligence system for end-to-end construction contract analysis and structured smart contract synthesis. The framework integrates clause extraction, semantic classification, KPI-aware importance ranking, standards alignment, discrepancy diagnostics, and constrained logic synthesis. Clause importance is quantified by mapping contract language to four key project performance indicators: cost overrun impact, schedule delay impact, cash-flow adequacy, and dispute frequency. The system was evaluated using five executed contracts from a large public owner representing general contractor, construction manager, architect-engineer, commissioning, and design-build delivery methods, comprising 588 clauses. Classification performance on a manually labeled validation subset achieved a macro-averaged F1 score of 0.55, with inter-annotator agreement of 69.17% (Cohen’s κ = 0.52). Clause prioritization rankings remained highly stable across alternative KPI weighting scenarios (Spearman ρ > 0.98). A contract-context audit further refined standards-based missing-provision findings by distinguishing confirmed omissions from relocated or uncertain obligations. For automation outputs, constrained template-based synthesis improved smart contract quality scores from 0.8/4.0 to 3.6/4.0 relative to unconstrained generation, while execution-level validation achieved a 100% pass rate across representative trigger scenarios. The findings demonstrate that retrieval-augmented language models, when combined with structured performance reasoning and standards-aware controls, provide a scalable and explainable foundation for smart contract-enabled contract governance in construction projects.

Abstract Effectively managing construction projects requires governance processes that can respond to rapid changes in macroeconomic conditions. Inflation, supply chain stress, and interest rate shocks introduce external pressures that traditional contract structures often fail to absorb. Sequential approvals, delayed payments, and slow dispute pathways create contractual frictions that accumulate over time and intensify cost escalation, schedule delay, and cash-flow strain. This study examines how automation-enabled governance mechanisms, modeled as smart contract functionalities, can reduce these frictions during volatile periods. A system dynamics model representing a generalized construction project integrates macroeconomic drivers with feedback processes associated with payment timing, administrative responsiveness, information delay, and dispute latency to assess how automation influences the propagation of shocks across project systems. Scenario simulations reflecting conditions from 2019 to 2023 show that targeted reductions in contractual delays can lower execution-stage cost escalation by approximately 15%–20% and reduce total project-level cost overruns by 5%–6%, while also moderating schedule delay and dispute escalation. The degree of stabilization depends on the severity of external shocks, yet the results demonstrate that automation-oriented governance mechanisms enhance system responsiveness by modifying the timing of corrective action within core feedback loops. By quantifying how digital automation reshapes governance delays under volatility, this study extends prior system dynamics and smart contract research and provides a quantitative basis for adaptive contract governance strategies aimed at enhancing project resilience.

Game-Based Learning (GBL) is a well-recognized experiential learning practice for Project Management (PM) training. However, GBL suffers from a stagnant adoption rate in higher education despite its positive reputation in research. Hence, this systematic review presents key findings, trends and gaps in Project Management-Game-Based Learning (PM-GBL) research to facilitate and encourage widespread adoption. Using the PRISMA framework, 50 GBL case studies on Project Management Serious Games (PM-SGs) were selected. This review also presents a timeline of serious games in construction management, software project management, and business project management across undergraduate and graduate engineering courses. Then, the content analysis identified five key themes-pilot studies, case studies, comparative studies, student-made games, and game design trends. In this section, a synthesis of key takeaways is presented regarding knowledge gain, game feedback, and learning behavior. In addition, guidelines for GBL developers and adopters are discussed. Next, the bibliometric analysis sheds light on the chronological and geographical distribution of the selected literature. Lastly, this paper suggests pivotal directions for future research in serious game design, development, implementation, and evaluation.

This study explores the automation of smart contract generation in construction, leveraging Large Language Models ( LLMs) like OpenAI's ChatGPT. Traditional construction contracts often suffer from delays and disputes, particularly in payment processes. The paper investigates automating the transformation of these traditional contracts into smart contracts, which promise enhanced efficiency and transparency. By conducting an extensive literature review and analyzing various contract types, the study identifies critical elements that are translatable into smart contracts. The experimental phase demonstrates the effective use of the ChatGPT API in extracting necessary contract information for conversion. This approach aims to streamline contract management, reduce disputes, and improve overall project execution. The potential of LLMs in this context is significant, indicating a shift towards more automated, reliable, and transparent contract management in the construction industry.

Fragmentation and poor collaboration in contract-heavy industries hinder innovation. While smart contracts offer promising automation for digital documents, the transformation process presents significant challenges. Current approaches are promising but are often constrained by technical limitations, domain-specific requirements, and limited flexibility, restricting widespread adoption. This paper systematically reviews the development of smart contracts using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to examine methodologies, challenges, and solutions through a thematic analysis of 30 key studies. The findings are grouped into three categories: Natural Language Processing (NLP)-based, template-based and ontology-based, and model-driven approaches. After analyzing the cross-industrial challenges of each category, this paper proposes a Large Language Model (LLM)-based smart contract generation solution to address the identified challenges validated through real-world use cases. This comprehensive analysis contributes to the ongoing dialogue on smart contracting, offering directions for future research and practical implementation in the digital infrastructure.

Building information modeling (BIM) has become prevalent in construction engineering management. However, the efficiency of traditional file-based BIM exchange between multidisciplinary teams remains low due to the transmission of redundant data from mostly unchanged BIM objects. Additionally, the traceability of changes between BIM files is limited. This paper proposes a traceable semantic differential transaction (tSDT) approach for open BIM exchange, which minimizes data redundancy and enables semantic-level traceability of BIM changes. Furthermore, openBIMdisk implements the tSDT and provides a Blockchain 3.0 virtual disk to support efficient, traceable, and secure BIM exchanges across multiple blockchain services. A pilot study of a modular construction project demonstrated the effectiveness of tSDT and openBIMdisk. Experimental results indicated that tSDT achieved minimal BIM redundancy for storing and restoring all BIM changes, using a sheer 0.007% of disk space on average. openBIMdisk facilitated BIM version management and object-level semantic traceability with a response time of 5.3 ms. The contributions of this paper are twofold. First, tSDT offers a novel and efficient approach for semantic BIM change traceability. Second, openBIMdisk provides BIM practitioners with a Blockchain 3.0 application featuring intuitive and user-friendly interfaces for BIM exchange.

The concept of smart cities has emerged to battle the problems facing cities from rapid urbanization and mismanagement of resources. In order to monitor and control these large-scale environments such as cities, digital twins were proposed. These digital twins represent a large-scale environment virtually and connect it to the physical world. However, complexity arises when studying these cities. A city is a complex system of interconnected systems, or a system-of-systems (SoS), according to definitions by systems engineering. To understand how SoS can be applied to a smart city digital twin, a literature review is first conducted on SoS applications in construction in order to understand how to apply it to the different parts of a smart city. This paper the proposes a unique view on smart city digital twins by applying the system-of-systems engineering approach to view the cities and thereby understand and model them.

PurposeThe healthcare sector in developing countries needs a more vigorous technological process to support maintenance activities. The ease of adopting the digital twin (DT) technology will depend on stakeholders' exposure to the advantages. Therefore, this study aims to determine the key drivers of digital twin maintenance management (DTMM) for healthcare facilities in Nigeria.Design/methodology/approachThe post-positivist philosophical perspective adopted in this study informed a mixed-method research approach using a Delphi study and a questionnaire survey. In total, 11 digital technology experts domiciled in building maintenance participated in the Delphi study, and 442 maintenance personnel, top management staff and heads of departments in Nigerian hospitals formulated the respondents for the questionnaire survey. Descriptive statistics and confirmatory factor analysis (CFA) using the structural equation modelling technique were used for data analysis.FindingsThe experts' responses indicated that the 17 listed variables had a very high impact on determining the DTMM of healthcare facilities in Nigeria. Consequently, the CFA showed that all parameter estimates for the derived ten indicator variables were statistically significant, and the robust fit indexes all met the cut-off index criteria. The measurement model for the DTMM drivers adequately matched the sample data. The foremost drivers are increased reliability of building components, maintenance cost reduction due to prediction before failures and smart management of site activities.Practical implicationsThe study enlightens the authorities and management of healthcare organisations on the benefits of DT technology in maintaining their facilities. It provides the motivation and guidance for decision-making, training of stakeholders and the full implementation of DT technology in the management of hospital facilities.Originality/valueThe innovativeness and emergence of DT technology, especially within the Nigerian healthcare sector, portend the need to determine the benefits of adopting DT in managing constructed facilities.

Accurate construction cost estimation at early stages is critical to enable project stakeholders to make financial decisions (e.g., set up the project budget). However, the heavy reliance on cost engineers' subjective experience and manual effort in practice makes the estimation an error-prone and time-consuming process. To this end, this study proposes a novel hypergraph deep learning-based framework to predict the actual costs of construction projects accurately and efficiently at early stages. It starts with a systematic hypergraph formulation incorporating construction cost factors and their interrelationships. A hypergraph deep learning model is then developed based on the formulated hypergraph for end-to-end construction cost prediction. Afterwards, model interpretation is undertaken to reveal the cost factor importance from the model training results in a quantitative manner. The framework is validated using an actual construction cost dataset of school projects. The results show high accuracy in cost prediction without human intervention and meaningful interpretations of cost factor importance for better understanding of construction cost patterns.

New digital technologies instigated by Industry 4.0 are driving digital transformation across various industries. One of the most recent concepts attracting attention in the research and industrial community is digital twins (DT). Remarkable advancements as a result of DT adoption have been seen in manufacturing and automotive industries; however, much less adoption has been seen in the construction industry due to challenges such as industry key players' technology-averse attitudes and a lack of evidence of the costs and benefits of DT adoption. This paper presents an exploratory investigation into the cost and benefits of DT implementation in the construction industry from the perspective of early adopters. Interviews with these adopters revealed that beyond the initial software and hardware cost, there are significant costs related to staff training, implementation time, and employer/employee adoption resistance. We propose a set of considerations and guidance for researchers and industry practitioners interested in DT implementation.

Recently, intelligent realities (IRs)-integrated systems that combine advanced analytics technologies such as digital twins (DTs) and artificial or virtual reality technologies-have emerged as a value-adding enhancement of DTs for facilities management (FM). However, DT-based IRs for maintenance are yet to be studied to determine their fitness for purpose from a practice perspective. The lack of practitioners' perspectives on the development of DT-based IR poses a potential disconnection between desired end user preferences for interaction with a DT for maximum application benefits and the theoretical development of this emerging knowledge field. This paper presents FM professionals' ex-ante evaluation of a conceptual prototype for a DT-based IR system. Using an electronic survey among selected FM professionals from educational and transportation facilities, it was found that the proposed solution can improve the visibility of managed assets, enhance predictive maintenance (PdM) practice, and provide cognition and judgment aid for FM professionals when they are mobile. The study provides relevant insights for developers of DT solutions and researchers on the preferred end user interaction modalities.

Buildings create a huge amount of information throughout their life cycle. The major information loss occurs during the building handover from the construction team to the owner. Communication of vast amount of information generated through the building life cycle is complex, error prone, and is insufficient, especially during commissioning stage, leading to unavailability of necessary information to the building owner and occupants. This research reviewed strengths and weaknesses of the current approaches to information management through the building life cycle. The research found that limited research has been focused on information management at the commissioning stage of construction and beyond. In addition, the research explored the requirements for adequate commissioning and digital technologies which can be used as potential solutions for efficient commissioning. It was found that digital technologies such as BIM, DT, AR, smart interactive system, and tools can lead to better communication and visualization of real-time building information.

The motivation of this paper is to examine and review the current study, gaps and trends in commissioning process and documentation in the architectural, engineering, construction, operation, and facility management (AECO-FM) industry and to propose future directions for imminent research area to revolutionized commissioning documentation in AECO-FM industry. In this paper, 66 research papers are studied to narrate the evolution of the commissioning process and documentation. This study discussed the current approach and studied adopted by the various retrieved research papers. Followed by identification of research gaps associated with on-going commissioning process and documentation. It has been found that limited studies focus on effective and automated building information sharing during commissioning and closeout stage for efficient building handover and O&M through its lifecycle. The automated digital practices to perform commissioning and handover process are still largely unknown. This study proposes novel approach for building information sharing through digital commissioning process. A scenario is presented to illustrate the application of digital commissioning process for building information sharing and verification of building assets. The proposed approach shows automatic and real-time information sharing with a holistic approach to perform building commissioning.

PurposeCyber-physical systems (CPS) offer improved delivery of facilities management (FM) mandates through their advanced computational capabilities. Using second-order multivariate analysis, this study explores the drivers of the espousal of this digital technology for FM.Design/methodology/approachThe study employed a deductive approach underpinned by a post-positivist philosophical stance using a quantitative technique aided by a well-structured questionnaire. Data retrieved from the study’s respondents were analysed with descriptive statistics, Kruskal–Wallis h-test, exploratory factor analysis and confirmatory factor analysis.FindingsThe result of the analysis conducted portrayed evidence of convergence and good measures while the estimated model parameters all attained prescribed fit indexes. Also, it was revealed that the most influential drivers for the uptake of CPS for FM mandates are resource allocation for system procurement, top management willingness, system stability and compatibility with the previous system.Practical implicationsThe study’s findings unravel the necessitated parameters that would instigate the adoption of CPS for the delivery of FM activities by organisations while also propelling the digital transformation of construction project delivery at the post-occupancy phase.Originality/valueThis is the first study to empirically assess the propelling measures for incorporating CPS for FM using second-order multivariate analysis. Consequently, the study's outcome helps close this knowledge gap.