Rapid Fire: Digital Fabrication and Data Driven Design, Innovating AEC

Presentations starting at 6:00.

Details
Name: Zhuoyang Xin
Title: Novel adaptive and sustainable FRP fabrication systems for large-scale freeform structural formwork
Summary: Mass customization with efficiency, enhanced performance and sustainability have been a consistent challenge in architecture design and construction. The design and optimization with freeform geometries especially susceptible to such challenge due to the limitation of conventional construction methods. This thesis proposes a novel adaptive fabrication technique so called Additive Lamination Manufacturing (ALM) to innovate the application of conventional Fibre Reinforces Polymer (FRP) lamination manufacturing progress and integrate with additive manufacturing to fabricate customized structural formwork in construction scale.
Contact: williamscutuq@gmail.com

Name: Mahnaz Bahremandi
Title: Self-shaping material system for large-scale ribbed shell spatial assemblies
Summary: This thesis explores the application of self-shaping techniques to transform flat sheets into curved surfaces on an architectural scale through an iterative and integrated design approach. By overcoming current limitations in self-shaping structures in scalability, controlling methods, shape complexity, and fabrication constraints, the project aims to develop a programmable 2D composite that easily transitions into 3D components. This involves identifying a viable system, developing digital control for customized shapes, and testing how these self-shaping components function in full-scale spatial structures. This approach has the potential to revolutionize architecture, allowing for prefabricated, optimized, and cost-effective large structures by integrating material properties from the very beginning.
Contact: m.bahremanditolou@uq.edu.au

Name: Lingju Wu
Title: REIMAGINING TIMBER: AN INTEGRATED APPROACH OF RESOURCE EFFICIENT FABRICATION PROCESS FOR TOPOLOGICALLY OPTIMISED CROSS-LAMINATED TIMBER SLABS
Summary: This thesis explores the application of self-shaping techniques to transform flat sheets into curved surfaces on an architectural scale through an iterative and integrated design approach. By overcoming current limitations in self-shaping structures in scalability, controlling methods, shape complexity, and fabrication constraints, the project aims to develop a programmable 2D composite that easily transitions into 3D components. This involves identifying a viable system, developing digital control for customized shapes, and testing how these self-shaping components function in full-scale spatial structures. This approach has the potential to revolutionize architecture, allowing for prefabricated, optimized, and cost-effective large structures by integrating material properties from the very beginning.
Contact: lingju.wu@uq.edu.au

Name: Tommy Nguyen
Title: AI-Enhanced Participatory Urban Design: Rethinking Brisbane's Case of Micromobility
Summary: With the rush of excitement from "Artificial Intelligence", there comes many interesting ways to incorporate it into our current practice of urban design. This thesis aims to develop a novel workflow that can rapidly generate through alternate street designs using generative artificial intelligence (GenAI) imagery and crowd-sourced feedback to target the current limitations of standard methodologies. Through the understanding of subjective preferences and objective data, this method allows designers to better understand the built environment and what is lacking. Although generative AI is prevalent in many spaces, this method hopes to explore how this technology is used ethically and sustainably.
Contact: tommy.nguyen@uq.edu.au, https://www.linkedin.com/in/tbnn98/

Name: Yassin Nooradini
Title: PRIORITISING CHILD-FRIENDLY DESIGN SOLUTIONS WITH EXPLAINABLE AI
Summary: Recently, image-based perception studies combined with deep learning have expanded across disciplines, yet their application to child-friendly urban design remains underexplored. This paper introduces a novel integration of Street View Imagery (SVI), Explainable AI (XAI), and crowdsourced Elo ranking to assess parental perceptions of safe and pleasant school neighbourhoods. By systematically prioritising visual urban attributes, the method highlights design features most critical to child-friendliness. Findings reveal suburban areas benefit from improved sidewalks and crosswalks, while inner-city streets require targeted safety measures. The approach demonstrates how AI accelerates evaluation and provides transparent guidance for inclusive, age-sensitive urban development.
Contact: y.nooradini@uq.edu.au