Resource Circularity & Ecosystem optimization
Bloxhub,
Copenhagen, 2023.
In this talk, I aim to delve into the future of urban living, specifically highlighting the pivotal role of resource circularity and ecosystem optimization. Drawing from a transformative research project in Canada, we will explore how urban farming and waste-to-resource closed-loop ecosystems can revolutionize urban architecture.
​
I aim to demonstrate how modular design frameworks and digital tools can drive the seamless integration of farming into the urban environment, optimizing resource utilization and fostering community engagement. This novel approach helps address pressing issues such as food security and climate change and aligns seamlessly with government and global priorities.
​
This talk will share insights on how this resource circularity model could be adapted and scaled up across various urban contexts, positioning cities as leaders in sustainable and resilient urban living.
​
By intertwining cutting-edge research, innovative design strategies, and effective collaboration, we can create a future where urban farming and resource circularity are integral parts of our cities' fabric.
Join us as we journey to redefine urban living & sustainability.
Integrating Climate Change Projections in Building Performance Simulations.
Publication: Journal of Architectural Engineering.
Volume 29, Issue 3, Jul 13, 2023
Building simulation tools can empower architects to evaluate and optimize the performance of their designs. Therefore, it is essential to integrate building performance simulation modeling from an early design stage. A critical part of the simulated environment is the weather file. It is a compilation of 20–30 years of historical weather and hourly data on temperature, solar radiation, wind speed, and wind direction. The accuracy and contextual relevance of said data are of utmost importance to the integrity of the analysis. Current energy modeling weather files are unlikely to include any indication of risk from potential climate change. Hence, there is a need to incorporate climate change projections in energy simulations and prepare architects to understand better the evolution of design strategies echoing such climate change in different climate zones.
​
This paper uses the CCWorldWeatherGen tool to morph Energy Plus Weather files of 17 North American cities in key climate zones into three simulated scenarios representing 2020, 2050, and 2080, corresponding to a 1.5°C global temperature rise. By simulating the impact of the morphed weather files on five commercial building models, this paper outlines a database echoing the projected climate change impacts within the selected locations. It highlights the critical effect of passive design strategies in facilitating indoor thermal comfort while limiting energy consumption.
Optimizing into the probable future,
Royal Architecture Institute of Canada (RAIC), Calgary, AB, 2023.
Computational analysis is an emergent data-driven approach to performance-based building design, yet few practitioners understand effective methods of evaluating the generated computational data. Furthermore, even fewer understand predictive models and how to estimate uncertainties. Designers who equip themselves with these methods can derive innovative insight into adaptive data-driven decision-making processes, ensuring an optimized design approach.
This session aims to lead a hands-on discussion examining how to leverage computational predictive models to empower designers with insights on the impact of climate change on the built environment. First, participants will learn how to simulate performance models based on morphed weather files representing different climate change scenarios. Subsequently, participants will learn how to generate design alternatives based on multi-objective design criteria, analyze the performance of large design spaces, augment simulated data using machine learning predictive methods, visualize data for qualitative data exploration, perform sensitivity analysis on simulated data and manage uncertainty using analysis of variance methods for quantitative assessment. Finally, the session will conclude with a comparative study of design performance in different climate zones across the various morphed climate scenarios.
Machine Learning in Architectural Design
BuildEX Conference, 2022
Current building performance analysis methods, while detailed, require significant computational time, often causing delays in professional practice. This research endeavours to optimize this process using Machine Learning (ML) predictive algorithms. The primary objectives are to ascertain the efficacy of ML in predicting performance metrics like energy use intensity, operational carbon, and daylight and to establish a robust framework for accurate predictions.
The study encompasses selecting representative building types, identifying relevant climate zones, expansive design simulations, and rigorous testing of various ML algorithms. The anticipated outcome is a more efficient predictive method for building performance, leading to swifter feedback for design teams. This advancement will bolster both the speed and confidence in design decisions, offering profound value to clients.
Rapid urbanization, resource depletion, and limited land are further increasing the need for skyscrapers in city centers; therefore, it is imperative to enhance tall building performance efficiency and generative capability. Potential performance improvements can be explored using parametric multi-objective optimization aided by evaluation tools, such as computational fluid dynamics and energy analysis software, to visualize and explore skyscrapers’ multi-resource, multi-system generative potential. An optimization-centred, software-based design platform can potentially enable the simultaneous exploration of multiple strategies for the decreased consumption and large-scale production of multiple resources. Resource Generative Skyscrapers (RGS) are proposed as a possible solution to further explore and optimize the generative potentials of skyscrapers. RGS can be optimized with waste energy harvesting capabilities by capitalizing on passive features of integrated renewable systems. This paper describes various resource generation technologies suitable for a synergetic integration within RGS typology and the software tools that can facilitate exploration of their optimal use.
KEYWORDS: Energy efficiency, Multi-objective optimization, Performance Based Design, Renewable Energy, Resource generation.
Integrated Process of Design to Construction for Robotic FabricationArchitectural Engineering Institute (AEI) Conference, Denver, April 2023.
This research project highlights an innovative approach to applying mixed-reality and robotic fabrication to a swing space art installation for a confidential technology client. Both the design and research teams’ vision is the conceptualization and application of said approach as a proof of concept of a flexible, location-independent fabrication using a platform called MRAAD (Mobile Robotic Assistant for Architectural Design). This platform and its accompanying scripts and customized six-axis robot arm empower architects to engage with robots in the design process and utilize methods of automation inaccessible by other construction methods.
This innovative design-to-construction approach enables the generated computational models to be communicated directly to a site-deployed robot arm. Moreover, the integrated mixed-reality platform enables further on-site optimization of the robot’s placement, surface processing, and tool paths. This process has the potential to reduce the time of construction, waste, and general costs. Furthermore, it can improve sustainability, the precision of construction, rapid prototyping, and re-establishes the architect’s role as the master builder with direct involvement throughout the construction process.
