School of Future Environments - Huri te Ao

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https://hdl.handle.net/10292/13907
AUT is home to a number of renowned research institutes in architecture and creative technologies. The School of Future Environments - Huri te Ao strong industry partnerships and the unique combination of architecture and creative technologies within one school stimulates interdisciplinary research beyond traditional boundaries.

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    Plants of Place: Justice Through (Re)Planting Aotearoa New Zealand’s Urban Natural Heritage
    (UCL Press, 2023-05-31) Rodgers, M; Mercier, OR; Kiddle, R; Pedersen Zari, M
    Climate change has led to urgent calls for environmental action and justice, which is likely to include increased urban vegetation. The benefits of this planting could go beyond ecological and climate benefits to contribute to decolonisation and environmental and spatial justice and build on the well-documented links between ecological and human wellbeing. In Aotearoa New Zealand, past and ongoing injustices resulting from colonisation have disconnected Māori (the Indigenous people) from their land. Māori see themselves reflected in the landscape and te taiao (the natural world). The process of colonisation has mostly erased natural heritage, intrinsic to Māori identity, from urban areas. Many plants in urban areas represent colonial identity rather than this natural heritage, and many of the native plants that have been planted originate from other parts of the country. Through reviewing the literature, this article argues for research that determines the benefits of urban planting design prioritising plants that naturally occurred in the past, termed here ‘plants of place’, in public places. In settler colonial countries, where it is an accepted practice to acknowledge built and predominantly colonial heritage, making pre-colonial natural heritage visible can have many co-benefits. It has the potential to contribute to climate change mitigation and adaptation, decolonisation efforts, spatial justice and environmental justice. Celebrating natural heritage and planting ‘plants of place’ can contribute in some part to righting past injustices and preparing for a changing future.
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    Digital Twin Technology for Sustainable Urban Development: A Review of Its Potential Impact on SDG 11 in New Zealand
    (Elsevier BV, 2024-10-13) Patel, UR; Ghaffarianhoseini, A; Ghaffarian Hoseini, A; Burgess, A
    The rapid rate of urbanization and increased infrastructural complexities significantly affects achieving the targets of Sustainable Development Goal 11 (SDG 11). Digital Twin Technology (DTT) has emerged as a promising and transformative tool, yet there is a lack of comprehensive understanding of its potential impact on SDG 11 within the New Zealand (NZ) context. This research examines how DTT can advance SDG11 by analysing its application, benefits, challenges, and implications within the NZ context. The novelty of this study lies in its use of a mixed-method approach as it integrates NZ specific trend analysis, keyword analysis, and an interrelationship network diagram. This comprehensive methodology employed provides a unique understanding on how DTT advancement can be adapted to NZ's urban landscape. The findings highlight critical challenges, including data integration, cross sector collaboration, and governance barriers which hinder widespread adoption. The study underscores the importance of Knowledge Sharing and Transfer (KS&T) to translate insights into local actions effectively. In addition, the interrelationship network diagram highlights the need for a holistic approach towards DTT implementation in the context of urban sustainability. These insights can play a fundamental role for guiding policymakers and shaping urban development strategies both in NZ and globally.
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    Developing a Framework for Building Information Modelling (BIM) Adoption in New Zealand
    (Emerald, 2023-12-26) Doan, Dat Tien; Ghaffarianhoseini, Ali; Naismith, Nicola; Ghaffarianhoseini, Amirhosein; Tookey, John
    Purpose In New Zealand, building information modelling (BIM) prevalence is still in its early stages and faces many challenges. This research aims to develop a BIM adoption framework to determine the key factors affecting the success of a BIM project. Design/methodology/approach Both primary and secondary data were employed in this research, including 21 semi-structured interviews and industry guidelines from the three most well-known global building excellence models (BEMs). The data were analysed through content analysis due to its recognised benefits as a transparent and reliable approach. Findings Leadership, clients and other stakeholders, strategic planning, people, resources, process and results were identified as seven main categories along with 39 indicators in the BIM adoption framework. Based on the interviewees' perspectives, leadership is considered the most significant category, impacting all of the remaining categories. Practical implications Using the developed framework will enhance comprehension of BIM, offering directives for those embracing BIM. This will aid construction stakeholders in being better equipped for BIM projects. Having a skilled BIM manager can lead to the success of construction projects. Originality/value This research contributed to the existing body of knowledge by providing the categories with specific factors that assist BIM practitioners in assessing their BIM performance for further BIM practice improvement.
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    Examining Critical Factors Affecting the Housing Price in New Zealand: A Causal Loop Diagram Model
    (Springer Science and Business Media LLC, 2023-12-26) Albsoul, Hadeel; Doan, Dat Tien; Aigwi, Itohan Esther; Naismith, Nicola; Ghaffarianhoseini, Amirhosein; Ghaffarianhoseini, Ali
    The New Zealand housing market has become a public concern due to the significant surge in housing prices. The steep increase in housing prices has presented significant difficulties for individuals seeking homeownership, particularly for first-home buyers. Therefore, this research aims to identify the crucial factors of the New Zealand housing price system and their influence on housing prices. The system dynamics (SD) methodology was applied to organise the cause and effect variables into a causal loop diagram (CLD) illustrating the structure and interaction of the primary feedback mechanisms within the complex system of housing prices. Accordingly, population growth, macroeconomic stability, investment demand, monetary policy, and construction costs are key contributing factors to promoting affordable housing prices and increasing homeownership rates in New Zealand. The construction costs, including the land cost, are the most significant of all the factors. Hence, there is a call to prioritise optimising construction resources. This research’s developed model was validated by exploring experts’ views on the model’s components and system dynamics. The findings provide relevant stakeholders in New Zealand’s residential construction sector with solutions and guidelines for coping with supply and demand fluctuations and reducing economic cycles on material price and workforce development.
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    Data-Driven Prediction of Indoor Airflow Distribution in Naturally Ventilated Residential Buildings Using Combined CFD Simulation and Machine Learning (ML) Approach
    (SAGE Publications, 2024-01-10) Quang, Tran Van; Doan, Dat; Phuong, Nguyen Lu; Yun, Geun Young
    Predicting indoor airflow distribution in multi-storey residential buildings is essential for designing energy-efficient natural ventilation systems. The indoor environment significantly impacts human health and well-being, considering the substantial time spent indoors and the potential health and safety risks faced daily. To ensure occupants’ thermal comfort and indoor air quality, airflow simulations in the built environment must be efficient and precise. This study proposes a novel approach combining Computational Fluid Dynamics (CFD) simulations with machine learning techniques to predict indoor airflow. Specifically, we investigate the viability of employing a Deep Neural Network (DNN) model for accurately forecasting indoor airflow dispersion. The quantitative results reveal the DNN’s ability to faithfully reproduce indoor airflow patterns and temperature distributions. Furthermore, DNN approaches to investigate indoor airflow in the residential building achieved an 80% reduction in the time required to anticipate testing scenarios compared with CFD simulation, underscoring the potential for efficient indoor airflow prediction. This research underscores the feasibility and effectiveness of a data-driven approach, enabling swift and accurate indoor airflow predictions in naturally ventilated residential buildings. Such predictive models hold significant promise for optimizing indoor air quality, thermal comfort, and energy efficiency, thereby contributing to sustainable building design and operation.
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