Abstract
High-rise building optimisation is becoming increasingly relevant owing to global population growth and urbanisation trends. Previous studies have demonstrated the potential of high-rise optimisation but have been focused on the use of the parameters of single floors for the entire design; thus, the differences related to the impact of the dense surroundings are not taken into consideration. Part 1 of this study presents a multi-zone optimisation (MUZO) methodology and surrogate models (SMs), which provide a swift and accurate prediction for the entire building design; hence, the SMs can be used for optimisation processes. Owing to the high number of parameters involved in the design process, the optimisation task remains challenging. This paper presents how MUZO can cope with an enormous number of parameters to optimise the entire design of high-rise buildings using three algorithms with an adaptive penalty function. Two design scenarios are considered for quad-grid and diagrid shading devices, glazing type, and building-shape parameters using the setup, and the SMs developed in part 1. The optimisation part of the MUZO methodology reported satisfactory results for spatial daylight autonomy and annual sunlight exposure by meeting the Leadership in Energy and Environmental Design standards in 19 of 20 optimisation problems. To validate the impact of the methodology, optimised designs were compared with 8748 and 5832 typical quad-grid and diagrid scenarios, respectively, using the same design parameters for all floor levels. The findings indicate that the MUZO methodology provides significant improvements in the optimisation of high-rise buildings in dense urban areas.
Highlights
The demand for high-rise buildings is increasing in metropolises owing to population growth and urbanisation trends (Ali and AlKodmany, 2012)
10,000 was set as the maximum function evaluations (FES) for covariance matrix adaptation with evolution strategy (CMA-ES) and radial basis function opti misation (RbfOpt), while 40 population sizes and 250 generations were considered for the population-based jEDE algorithm
The results proved that the performance outcomes on different floor levels of high-rise buildings may be affected in dense urban areas
Summary
The demand for high-rise buildings is increasing in metropolises owing to population growth and urbanisation trends (Ali and AlKodmany, 2012). Designing a sustain able high-rise building is a complex task because the process involves various types of design parameters that affect multiple performance aspects. In none of these studies, were the various floor levels considered as separate design problems, which is crucial for improving the overall performance of high-rise buildings (Wood, 2007). Using the same design parameters for the entire high-rise design is a limited approach because the perfor mance of the building varies between the ground and sky floor levels in dense urban areas. Optimising the design of an entire high-rise building is challenging as the simulations require expensive computational time, and the optimisation process needs to cope with an enormous number of design parameters. Part 1 of the study is focused on solving computationally expensive simulations of each zone using (http://creativecommons.org/licenses/by/4.0/)
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