Massey Documents by Type
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Item Human-centric integrated change management framework for digital transformation in construction(Emerald Publishing Limited, 2025-09-08) Bidhendi A; Poshdar M; Jelodar MB; Rahimian F; González VAPurpose – This study develops a human-centric change management framework to address the gap between building information modelling (BIM) potential and its practical implementation and adoption in the construction industry by focusing on human factors influencing digital transformation success. Design/methodology/approach – A multi-phased methodology was employed, combining systematic literature reviews with advanced network analysis techniques. Two literature review rounds extracted key change management activities and human-centric principles. Social network analysis (SNA) was utilised to quantify relationships and significance within the construction industry context, identifying high-centrality nodes in the network. Findings – The analysis identified training, organisational competency assessment and resource allocation as the most critical change management activities for successful digital transformation, which emerged as central nodes. The study developed a tailored three-phase framework (Strategic initialisation, Operational transformation and Sustainable integration) that enables construction organisations to implement BIM and digital technologies while maintaining focus on human factors. Practical implications include improved employee engagement, reduced resistance to technological change, enhanced organisational readiness for digital transformation and a structured pathway for construction organisations to move beyond current BIM implementation barriers. The framework provides actionable guidance for construction leaders to balance technological advancement with human-centric values, ultimately supporting sustainable digital transformation in the industry. Originality/value – This study offers a novel data-driven approach to digital transformation in construction by quantitatively analysing relationships between change management activities and human-centric principles. The research addresses a critical gap in BIM and digital transformation implementation literature by developing an integrated framework that balances technological advancement with human considerations, helping organisations move beyond current adoption barriers in the AECO industry’s transformative journey.Item Artificial Neural Network (ANN) model for predicting blast-induced tunnel response in Steel Fiber Reinforced Concrete (SFRC) structures(Elsevier Ltd, 2025-12-01) Ali M; Chen L; Feng B; Rusho MA; Jelodar MB; Tasán Cruz DM; Samandari NThis study presents an Artificial Neural Network (ANN)-based predictive framework for evaluating the blast-induced response of Steel Fiber Reinforced Concrete (SFRC) tunnel structures. As underground infrastructure is increasingly exposed to dynamic and extreme loading conditions, particularly from accidental or intentional explosions, accurate and efficient prediction tools are essential. In this research, a comprehensive dataset comprising 299 data points was developed, including approximately 120 experimental results from published blast and structural tests, and 179 high-fidelity numerical simulations. This combined dataset ensured both physical reliability and broad coverage of loading scenarios. The model incorporates nine critical input parameters: Peak Overpressure (MPa), Impulse (kPa·ms), Tunnel Diameter (m), Wall Thickness (m), Compressive Strength (MPa), Tensile Strength (MPa), Fiber Volume Fraction (%), Soil Stiffness (MPa/m), and Standoff Distance (m). The target output variable is the tunnel's Maximum Displacement (mm) under blast loading. A three-hidden-layer ANN architecture was optimized through rigorous hyperparameter tuning. The best-performing model, with 16 neurons in each hidden layer, achieved high predictive accuracy, with R² values of 0.983 (training), 0.956 (validation), and 0.948 (testing). Error metrics including RMSE (2.12–3.14 mm), MAE (1.92–3.52 mm), and MAPE (1.95 %–3.12 %) further confirmed the model’s robustness. Validation against experimental data from literature demonstrated excellent agreement, verifying the model's practical applicability. Additionally, sensitivity analysis identified Peak Overpressure and Standoff Distance as the most influential factors affecting displacement. The proposed ANN framework offers a computationally efficient and accurate tool for assessing SFRC tunnel performance under blast loading, supporting the design of safer and more resilient underground structures.Item Real-Time tracking and analysis in construction projects: A RealCONs framework(Elsevier Ltd, 2025-09-01) Radman K; Jelodar MB; Lovreglio R; Ghazizadeh E; Wilkinson SConstruction projects increasingly rely on processing vast amounts of data from multiple sources, including consultants (BIM), cloud-based project management platforms (e.g., Aconex), planning departments, construction sites, main contractors, and subcontractors. However, inefficiencies in data acquisition and reliance on manual data entry hinder real-time project analysis, delay notifications, and decision-making. This study introduces the Real-Time Data-Driven Construction Project Analysis Framework (RealCONs) to address these challenges by streamlining data flow and enhancing project performance. A comparative analysis used eight case studies four employing the existing approach and four utilising RealCONs—to assess improvements in data integration, early delay identification, and decision-making efficiency. The results, validated through Earned Value Management (EVM) and Earned Schedule Management (ESM) metrics, demonstrate that RealCONs significantly enhance project forecasting accuracy, schedule adherence, and cost management. Additionally, statistical analyses, including the Shapiro-Wilk test and the Wilcoxon Signed-Rank analysis, confirm that RealCONs outperform the existing approach by reducing data collection and decision-making delays, enabling project managers to implement proactive mitigation strategies. These findings highlight RealCONs’ potential to improve project efficiency, reduce costs, and optimise real-time construction management.Item Exploring Off-site Construction and Building Information Modelling Integration Challenges; Enhancing Capabilities within New Zealand Construction Sector(IOP Publishing Ltd, 2022-01-01) Ghalenoei NK; Jelodar MB; Paes D; Sutrisna MOver the last few years off-site construction (OSC); which is essentially manufacturing different components in a controlled environment, has become popular in the construction industry. This method has the advantages of simplicity, speed, reducing project duration, and minimising construction waste. Therefore, a growing body of literature recognises the importance of OSC to gain better project performance. While OSC has received considerable critical attention, to enhance OSC applications, integrating advanced technologies such as building information modelling (BIM) is essential. There is a lack of research addressing the integration of BIM and OSC, particularly in New Zealand, and few studies investigated the current subject. Therefore, this study focuses on finding the existing OSC and BIM integration challenges within the New Zealand construction sector. The objective of this study has been investigated through literature review and interviews with experts. The common challenges of OSC and BIM integration were identified and classified. Human resources, documentation, managerial, and organisational are the main challenges. This paper is dedicated to exploring OSC and BIM integration in New Zealand, an essential step for the OSC application strategies within the construction sector. This study findings will lend to the construction sector expanding capabilities to improve the status quo and optimise OSC applications through advanced technologies.Item Comparing two AI methods for predicting the future trend of New Zealand building projects: Decision Tree and Artificial Neural Network(IOP Publishing Ltd, 2022-01-01) Zavvari A; Jelodar MB; Sutrisna MThe rise of Artificial Intelligence and Machine Learning in many aspects of construction management has helped this industry to further improve the management, design, and planning of construction projects. This trend happens in many construction sectors, including in New Zealand. Whilst relatively smaller compared to construction sectors in other OECD countries, the construction sector in New Zealand carries a similar degree of complexity and with its own unique characteristics. Various studies showed that AI and ML can be used for analysis of construction data to generate further insights and to predict future trends in construction sectors. However, the AI approaches have their own set of challenges such as complexity, high cost of training, failure, and change. Aiming to better understand the trends and requirements of New Zealand building projects, this study started with a review of the existing AI methods that are currently being applied. Accordingly, compare and evaluate the accuracy of two AI prediction methods. The two methods of Decision Tree and Artificial Neural Network are selected based on their predictive power and accuracy. These methods are conducted by using available historical building data which is available on StatsNZ website. A portion of the data is used for testing and evaluation purposes, and the rest of the data is used for training the AI methods. It was identified that the Decision Tree method did not show suitable accuracy for prediction building consents issued data. In comparison, Artificial Neural Network shows a reasonable range with 95% of confidence level. Therefore, this method is applied for building consents issued in New Zealand.Item A guideline for BIM and lean integrated construction practice(Emerald Publishing Limited, 2025-04-09) Likita AJ; Jelodar MB; Vishnupriya V; Rotimi JOBPurpose This study proposes a guideline for integrating Building Information Modelling (BIM) technology and lean construction practices to address the construction industry’s challenges in transitioning to environmentally friendly developments. Design/methodology/approach This study employs a qualitative research method, integrating and validating lean principles with BIM tools by extensively analysing previous studies. Subject matter expert interviews were conducted to validate the findings and create conceptual maps. Thematic and content analyses were performed to develop the proposed guidelines and recommendations. Findings The study highlights the potential of integrating BIM and lean construction practices to enhance productivity and reduce waste. The proposed guidelines provide practical recommendations for improving the implementation of BIM and lean practices, offering a structured approach for stakeholders to address critical challenges. Research limitations/implications While this study provides valuable insights, it primarily focuses on the New Zealand (NZ) context. Future research could explore the applicability of the proposed guidelines in different regions and consider quantitative validation methods to strengthen the findings. Originality/value This research contributes to the field by providing a novel guideline for integrating BIM and lean construction practices, addressing critical implementation challenges. The study offers valuable insights for global construction practices aiming to adopt advanced management approaches.Item A taxonomy of pedestrian evacuation infrastructure for urban areas; An assessment of resilience towards natural hazards(IOP Publishing Ltd, 2022-01-01) Fathianpour A; Jelodar MB; Wilkinson S; Evans BMany people in the world live in hazardous environments and are susceptible to disasters. In the time of a destructive event, a resilient community must be prepared to mitigate the event and quickly respond. An effective mitigation plan can lead to fewer fatalities and damages. One of the most critical tasks for mitigation is the evacuation process. Wherein short notice time, overcrowding, bottlenecks in infrastructure and challenging terrain and topography may worsen the situation. Amongst other things, the evacuation process encompasses transportation infrastructures referred to as corridors, signs, pedestrian footpaths, and/or shelter infrastructures for keeping people safe. Evacuation infrastructure can also become damaged after the event; therefore, it's imperative to have a robust assessment of different evacuation infrastructures. This study will investigate the characteristics of the available evacuation infrastructure and outline the general drawbacks. A systematic methodology for reviewing articles has been implemented to understand how vulnerable cities can be more prepared, especially for pedestrian evacuation. An evacuation scoring system for pedestrians will be developed to investigate evacuation infrastructure in terms of different resilience features, such as redundancy, safe to fail, readiness, capacity. The most practical evacuation system will be estimated, with a final output being to provide the features of a successful pedestrian evacuation system for future policy use.Item Real-time Employee Monitoring Technologies in the Construction Sector - Effect, Readiness and Theoretical Perspectives: The case of New Zealand(IOP Publishing Ltd, 2022-01-01) Wu RW; Yiu TW; Jelodar MBVarieties of Real-time Employee monitoring Technology (REMT) are becoming popular and have aroused significant interest in recent years from the construction sector, where the industry explores the use of advanced monitoring technologies to reduce unsafe work behaviours and improve productivity. However, studies identified some concerns about applying these monitoring technologies at construction sites. Consequently, REMT devices and applications have not been well-received for tracking frontline workers. Lack of understanding of REMT, monitoring data protection and privacy management strategy set a barrier for the monitoring technologies to implement in the construction industry. Privacy has become a critical issue for the future digital construction site. This study adopts the literature review and a questionnaire survey, examined the readiness, summarised effects of REMT applied at the New Zealand construction sites, identified the influence factors, and discovered the theories that will potentially explain the factors and address the potential impact. Communication Privacy Management theory (CPM), Equity Theory (ET) and Control Theory of Privacy (CTP) are reviewed, and a theoretical framework is built upon REMT adoption in the construction sector. In conclusion, future studies are recommended for the international construction entities to get ready to adopt the real-time monitoring tools.Item Generative AI, Large Language Models, and ChatGPT in Construction Education, Training, and Practice(MDPI (Basel, Switzerland), 2025-03-15) Jelodar MB; Senouci AThe rapid advancement of generative AI, large language models (LLMs), and ChatGPT presents transformative opportunities for the construction industry. This study investigates their integration across education, training, and professional practice to address skill gaps and inefficiencies. While AI’s potential in construction has been highlighted, limited attention has been given to synchronising academic curricula, workforce development, and industry practices. This research seeks to fill that gap by evaluating AI adoption through a mixed and multi-stage methodology, including theoretical conceptualisation, case studies, content analysis and application of strategic frameworks such as scenario planning, SWOT analysis, and PESTEL frameworks. The findings show AI tools enhance foundational learning and critical thinking in education but often fail to develop job-ready skills. Training programmes improve task-specific competencies with immersive simulations and predictive analytics but neglect strategic leadership skills. Professional practice benefits from AI-driven resource optimisation and collaboration tools but faces barriers like regulatory and interoperability challenges. By aligning theoretical education with practical training and strategic professional development, this research highlights the potential to create a future-ready workforce. The study provides actionable recommendations for integrating AI across domains. These findings contribute to understanding AI’s transformative role in construction, offering a baseline for effective and responsible adoption.Item A review of climate change impact assessment and methodologies for urban sewer networks(Elsevier B V, 2025-06) Karimi AM; Jelodar MB; Susnjak T; Sutrisna MUnderstanding how climate change affects urban sewer networks is essential for the sustainable management of these infrastructures. This research uses a systematic literature review (PRISMA) to critically review methodologies to assess the effects of climate change on these systems. A scientometric analysis traced the evolution of research patterns, while content analysis identified three primary research clusters: Climate Modelling, Flow Modelling, and Risk and Vulnerability Assessment. These clusters, although rooted in distinct disciplines, form an interconnected framework, where outputs of climate models inform flow models, and overflow data from flow models contribute to risk assessments, which are gaining increasing attention in recent studies. To enhance risk assessments, methods like Gumbel Copula, Monte Carlo simulations, and fuzzy logic help quantify uncertainties. By integrating these uncertainties with a Bayesian Network, which can incorporate expert opinion, failure probabilities are modelled based on variable interactions, improving prediction. The study also emphasises the importance of factors, such as urbanisation, asset deterioration, and adaptation programs in order to improve predictive accuracy. Additionally, the findings reveal the need to consider cascading effects from landslides and climate hazards in future risk assessments. This research provides a reference for methodology selection, promoting innovative and sustainable urban sewer management.