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    Generative AI, Large Language Models, and ChatGPT in Construction Education, Training, and Practice
    (MDPI (Basel, Switzerland), 2025-03-15) Jelodar MB; Senouci A
    The 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.
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    Development of a decision support tool for automation adoption and optimisation in precast concrete plants : a New Zealand case study : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Construction Project Management at Massey University, Albany, New Zealand
    (Massey University, 2022) Ansari, Reza
    In response to the growing demand in the New Zealand construction market, this study aims to develop a decision-support framework for adopting and optimising automation in precast concrete plants, which are increasingly recognised for their numerous benefits. The primary resources required by these plants include labour, equipment, and materials, and their efficient use is essential for maintaining competitiveness. Automation has been identified as a potential solution for improving productivity and profitability in precast concrete manufacturing; however, an appropriate decision-support tool is currently lacking. The current study commences with a comprehensive literature review, followed by historical data collection, face-to-face interviews, and site observations of precast concrete plants to address this research gap. These methods help identify attributes that affect profitability, leading to developing and validating of a theoretical framework named the Precast Plant Automation System Tool (PPAST) through a case study. The PPAST framework comprises two sequential phases: the strategic phase, which uses the direct rating method for preliminary feasibility evaluation of automation adoption, and the tactical phase, where the AHP method assesses the appropriate automation sequence for the plant. The study’s main findings indicate that the developed decision support system enables decision-makers to articulate their objectives and attitudes towards risk as they explore the feasibility of automation and formulate an optimal automation strategy. Specifically, the system aids in evaluating the impact of automation on cost and quality and identifying necessary process changes before implementing new technologies. The primary contribution of this research is its novel approach to systematically evaluating alternative automation scenarios in precast concrete production plants. The results demonstrate that the proposed model is a valuable and effective decision-making tool for adopting and optimising automation in precast concrete plants. This research fills a critical knowledge gap concerning the crucial measurements of precast concrete plant profitability and the absence of an automation adoption tool. The developed framework can be extended to investigate automation adoption and optimisation in other precast concrete plants across New Zealand. This study's practical implications include empowering precast plants to meet their organisation's profitability measures, thus satisfying stakeholder value propositions. A thriving precast concrete industry will lead to more satisfied clients, attract additional investment, and improve the overall construction industry's quality, productivity, and profitability at the national level. Theoretically, this research contributes a reliable benchmark for future studies by developing decision support tools that facilitate selecting optimised automation methods for precast concrete plants and contributing to theoretical knowledge by establishing an optimised automation decision support method that guides researchers in exploring other avenues for maximising profitability.
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    Cost estimation model for earthquake damage repair in New Zealand : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Construction at Massey University, Albany, New Zealand
    (Massey University, 2021) Kahandawa Appuhamillage, Ravindu Visal Dharmasena Kahandawa
    Earthquakes are natural hazards that can devastate nations, their people and the surrounding built environments. Designing a suitable strategy for rapid recovery requires an accurate damage assessment process for the built environment. Loss estimation models were developed to predict the cost of repair, but these models were not used to estimate the costs of post-earthquake repair. This could be due to the fact that these probability-based models tend to provide less accurate outputs. In fact, there is no existing literature on post-earthquake repair cost estimation models that can rapidly produce repair cost estimates. This research developed a post-earthquake cost estimation model for earthquake damage repair work (referred to as a cost of damage repair, earthquake estimation model or C-DREEM). The research used an exploratory sequential research design that used semi-structured interviews (N=19) with engineers, quantity surveyors and builders with experience in earthquake damage repair work as the primary data collection. Then a web-based survey questionnaire (N=310 distributed, N=92 received) of professionals with experience in cost estimation for earthquake damage repair work was the second data collection. The collected data was analysed using thematic analysis, descriptive statistics and non-parametric tests. Based on the findings in the literature, document review and research data analysis, a cost of damage repair earthquake estimation model (C-DREEM) was developed. The C-DREEM model was then validated through a focus group interview session with participants who had experience in the cost estimation for earthquake damage repair work in New Zealand (N=9). Key findings identified from the research were: (i) 11 factors have a critical impact on the accuracy of cost estimation of earthquake damage repair work (CEEDRW) which includes consequential damage, initially unforeseen damage, and changes to the final repair state; (ii) Use of a unit rate and lump sum amount methods were some of the most suitable ways incorporate these factors to CEEDRW; (iii) detailed damage evaluation reports are the most likely information sources post-earthquake for CEEDRW; and (iv) the standardised and automated cost estimation model, C-DREEM, developed by this research can improve both pre and post-earthquake CEEDRW process with include the benefits of sharing consequence functions and probable damage information with probability-based methods. The key contribution to knowledge from this research is identifying the factors affecting CEEDRW, evaluating the significance, selecting methods to incorporate the factors into the costing process, and creating the C-DREEM costing process that combines the pre-and post-earthquake loss estimation processes. The research also supports the professional practice by providing: a standardised and automated cost estimation process; specifying the areas that should be improved, such as the damage reporting process; and a better cost control and monitoring process through standardised rates. Through the findings of the research, government and insurance companies: can standardise and improve the accuracy and speed CEEDRW process, and makes informed decisions to manage the impact of the eleven factors affecting CEEDRW identified by this research.