Abstract
Designing high-rise buildings is one of the complex tasks of architecture because it involves interdisciplinary performance aspects in the conceptual phase. The necessity for sustainable high-rise buildings has increased owing to the demand for metropolises based on population growth and urbanisation trends. Although artificial intelligence (AI) techniques support swift decision-making when addressing multiple performance aspects related to sustainable buildings, previous studies only examined single floors because modelling and optimising the entire building requires extensive computational time. However, different floor levels require various design decisions because of the performance variances between the ground and sky levels of high-rises in dense urban districts. This paper presents a multi-zone optimisation (MUZO) methodology to support decision-making for an entire high-rise building considering multiple floor levels and performance aspects. The proposed methodology includes parametric modelling and simulations of high-rise buildings, as well as machine learning and optimisation as AI methods. The specific setup focuses on the quad-grid and diagrid shading devices using two daylight metrics of LEED: spatial daylight autonomy and annual sunlight exposure. The parametric model generated samples to develop surrogate models using an artificial neural network. The results of 40 surrogate models indicated that the machine learning part of the MUZO methodology can report very high prediction accuracies for 31 models and high accuracies for six quad-grid and three diagrid models. The findings indicate that the MUZO can be an important part of designing high-rises in metropolises while predicting multiple performance aspects related to sustainable buildings during the conceptual design phase.
Highlights
High-rise buildings began to emerge at the end of the 19th century to provide extra floor space in limited urban plots (Al-Kodmany and Ali, 2013)
This paper presents a multi-zone optimisation (MUZO) methodology to support decision-making for an entire high-rise building considering multiple floor levels and performance aspects
The findings indicate that the MUZO can be an important part of designing high-rises in metropolises while predicting multiple performance aspects related to sustainable buildings during the conceptual design phase
Summary
High-rise buildings began to emerge at the end of the 19th century to provide extra floor space in limited urban plots (Al-Kodmany and Ali, 2013). According to a United Nations report (UN, 2019), 30% of the world’s population lived in urban areas in 1950 This percentage increased to 55% in 2018, and the projection by 2050 was 68%. Optimising high-rise buildings in dense urban districts is more challenging because various floor levels require different design decisions owing to perfor mance variations in ground and sky levels. These decisions are based on simulations, which require expensive computational time, and optimisation processes that need to cope with an enormous number of design parameters. This paper introduces a novel multi-zone optimisation (MUZO) methodology of optimising high-rises by considering multiple floor levels as different optimisation problems to investigate sustainable al ternatives during the conceptual phase.
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