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    Construction projects status tracking : a real-time data-driven framework for delay management and analysis : a thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy in Building and Construction, School of Built Environment, College of Science, Massey University, New Zealand
    (Massey University, 2025-10-16) Radman, Kambiz
    Construction delays remain one of the most critical challenges in project delivery, often resulting in cost overruns, schedule slippages, and weakened stakeholder confidence. Traditional delay management methods are largely reactive, relying on periodic reporting and fragmented communication across project teams. In contrast, the increasing availability of digital tools offers the opportunity to adopt more proactive, data-driven approaches. This study introduces a framework that centralises and analyses real-time project data from multiple stakeholders, including head contractors, subcontractors, consultants (via Building Information Modelling—BIM), and on-site teams. By integrating these diverse inputs into a unified Power BI dashboard, the framework enhances early detection of delays, improves coordination, and supports timely decision-making. Earned Value (EV) metrics are embedded as key control points, providing early signals of deviations and potential risks. Despite these advances, several research gaps remain. Existing systems are often costly and complex, highlighting the need for simple, inexpensive, and user-friendly solutions. Real-time data acquisition and centralisation are still underdeveloped, limiting the speed and reliability of insights. Current practice focuses heavily on retrospective reporting, with limited capability for real-time analytics or predictive forecasting. Stakeholder communication and coordination remain fragmented, while systematic early notification systems for emerging delays are rarely implemented. Ultimately, it is necessary to integrate historical and real-time data to facilitate predictive delay analytics. Addressing these gaps would help shift construction delay management from reactive intervention towards proactive risk mitigation. Guided by these gaps, the research is shaped around three central questions: (1) What causes delays in major construction projects, and how do these delays affect stakeholder collaboration? (2) How are digital technologies currently being deployed to improve project performance in relation to delays and risks? (3) How can a new framework be designed and evaluated to strengthen early delay detection and enhance project outcomes? To answer these questions, five objectives are established. First, to identify and analyse the key project stakeholders and the principal causes of delay. Second, to review and assess the role of digital technologies in construction projects. Third, to develop a framework that integrates real-time data for enhanced monitoring, reporting, and early detection of delays. Finally, to evaluate this framework in practice, assess its effectiveness in enhancing transparency, facilitating stakeholder coordination, and improving overall project performance. In doing so, this research contributes to the advancement of digital construction management by embedding real-time analytics into live project environments. The proposed framework not only enhances transparency and resource allocation but also lays the groundwork for predictive delay management, thereby aligning construction practices with the broader objectives of Industry 4.0.
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    Real-time fusion of wireless sensor network data for wellness determination of the elderly in a smart home : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science and Engineering at Massey University, Manawatu, New Zealand
    (Massey University, 2014) Suryadevara, Nagender Kumar
    In this research, I have explored a methodology for the development of efficient electronic real time data processing system to recognize the behaviour of an elderly person. The ability to determine the wellness of an elderly person living alone in their own home using a robust, flexible and data driven artificially intelligent system has been investigated. A framework integrating temporal and spatial contextual information for determining the wellness of an elderly person has been modelled. A novel behaviour detection process based on the observed sensor data in performing essential daily activities has been designed and developed. The model can update the behaviour knowledge base and simultaneously execute the tasks to explore the intricacies of the generated behaviour pattern. An initial decline or change in regular daily activities can suggest changes to the health and functional abilities of the elderly person. The developed system is used to forecast the behaviour and quantitative wellness of the elderly by monitoring the daily usages of household appliances using smart sensors. Wellness determination models are tested at various elderly houses, and the experimental results related to the identification of daily activities and wellness determinations are encouraging. The wellness models are updated based on the time series analysis formulations. The integrated smart sensing system is capable of detecting human emotion and behaviour recognition based on the daily functional abilities simultaneously. The electronic data processing system can incorporate the Internet of Things framework for sensing different devices, understand and act according to the requirement of smart home environment.