Real-Time Data in Architectural Practice; Embracing A.I. in the design for energy efficiency

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AHRC
AHRC
AHRC
AHRC
AHRC

No funds listed.

Sept. 30, 2017

Dec. 31, 2021

The project aims to address methods for creating a computational system for design efficiency of the Higher Education (HE) campuses in the North-West. With new developments in technology, like generative design processes, autonomous systems and self-aware bots, the AEC industry is bound to be affected at a global scale. But how would Artificial Intelligence (AI) augment the architectural design and what are the possibilities for architects working with these technological advancements?

The case study will run as a collaboration with an experienced industrial partner that has been involved in designing and building school campuses in the UK. More specifically, the project's main focus is to identify improvement strategies for energy consumption in university campus buildings.
A university campus usually occupies large areas in a city's fabric. The urge to create additional campuses or to further develop the existing ones continues as contracts that reach £2 billion are planned between 2017 and 2020, according to Barbour ABI, the construction analysts. Similarly, the Financial Times report that, in 2016, British universities have increased their budget on new buildings by 43 per cent in six months. However a campus is a complex structure that hosts various activities. These activities change continuously over time. Thus, the development or expansion of a campus is characterized by complexity. This complexity requires an approach different from the terraced houses construction style that was applied during the UK's rapid urbanization. The mere repetitive construction approach wouldn't respond efficiently since the overall behavior of a campus changes and it happens on a procedural level. Therefore, the approach should consider the flexibility of an adaptive system in the early stages of their design. During this project, the benefits of combining Human and Artificial Intelligence will be explored. Through the use of data from the campus, the goal is to realize which factor is the biggest contributor for the energy consumption. The data will relate to the design of the campus, as well as its' energy performance, and post-occupancy information & insights. By embracing this type of data with computation in the early stages of the design process, the project will investigate improvements in the workflow and output of the architectural practice for HE campuses. Based on The Clean Growth Strategy paper, the majority of buildings including campuses, heating creates around 32 per cent of total UK emissions. However the design of an efficient HE campus in energy consumption may not only reduce the emissions but help to drive growth. According to Terence Fox, a director in the finance department at Edinburgh University, new buildings can prove decisive. "If a student [comes] from the US, Australia or China, they need to come here because we've got not just the best students, but the best facilities. If the choice is Manchester or Edinburgh, and Manchester has new buildings, they'll probably go to Manchester."

From a research aspect, the applications of the respective methodology could extend beyond the energy planning of higher education campuses. While the energy factor is becoming ever more critical within the AEC practice, further equally important design variables could be revisited and explored. People's flow in a building or the exterior view maximization are examples of such variables. In this manner, the architect's professional boundaries could be transcended. This application of Artificial Intelligence for design decision-making however is its' infancy even for large international firms. As more and more practices create specialist groups to embrace this technological development the benefits will become more tangible. And the AEC industry will be enhanced by embedding new ways of tackling design challenges; ways that would be impossible without the implementation of computational modelling systems.