School of Future Environments - Huri te Ao

Permanent link for this collection

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.

Browse

Browsing School of Future Environments - Huri te Ao by Subject "0204 Condensed Matter Physics"

Now showing 1 - 2 of 2
  • Item
    A Highly Stretchable Strain-based Sensing Sheet for the Integrated Structural Health Monitoring
    (IOP Publishing, 2024-06-28) Zhang;, Hui; Beskhyroun, Sherif
    In this study, a flexible strain sensing system that can be applied to full-scale reinforced concrete frame structures is presented. In order to fulfil the criteria for strain detection that are posed by various structural components, the flexible strain gauge is offered in two distinct configurations: one full bridge and one double half bridge. A strain configuration selector is built on the basis of this information. The selector is designed to enable the system to flexibly switch strain modes for measuring axial or bending strain without adjusting the installation location of strain sensors. The first section of this study focuses mostly on elaborating on the methodology behind the development of a flexible strain system. This method was primarily designed with the aim of detecting the abnormalities in the strain field that are brought on by structural damage in order to accomplish the goal of local detection. The creation of a strain configuration selector also enables the conversion between two different strain measures whenever it is necessary without requiring the sensor installation to be moved to a new position, which helps to significantly reduce the amount of cost associated with sensor deployment. The performance of the flexible strain sensing system as well as its sensitivity were evaluated by doing the cyclic load testing on a full-scale RC frame. Both half-bridge and full-bridge strain gauges are installed in the critical components, such as beams and columns. In addition, 14 linear variable displacement transducers (LVDTS) were placed on the RC frame in order to monitor variations in displacement and deformation. The findings of the experiments indicate that the flexible strain sensor exhibits a high degree of sensitivity, and it is therefore suitable for integration into a structural health monitoring (SHM) system for the purpose of tracing the strain caused by localised structural damage. Additionally, it is able to monitor the strain trend on the complete scale of the frame model. In future work, the flexible strain system will be modified and enhanced by using wireless technology for data transmission in order to build a wirelessly integrated structural health monitoring (SHM) system.
  • Item
    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.
Items in these collections are protected by the Copyright Act 1994 (New Zealand). These works may be consulted by you, provided you comply with the provisions of the Act and the following conditions of use:
  • Any use you make of these works must be for research or private study purposes only, and you may not make them available to any other person.
  • Authors control the copyright of their works. You will recognise the author’s right to be identified as the author of the work, and due acknowledgement will be made to the author where appropriate.
  • You will obtain the author’s permission before publishing any material from the work.