Airport terminals are one of the most complex building types, exhibiting a wide variety of design parameters and an enormous amount of possible solutions. It is because of these factors that the discovery of optimal or near optimal solutions through a traditional design approach is not always guaranteed to happen. Therefore, the application of a systematic design approach is of great importance.
This thesis proposes that the design of airport terminals be addressed by the use of computational methods. A computational design approach is proposed, which is based the combination of Parametric Modeling and Evolutionary Algorithms. Parametric modeling is used to generate a wide spectrum of airport terminal designs and evaluate them according to characteristics relevant to traveling convenience, spatial efficiency, cost and commercial revenue potential.
The parametric model is combined with an efficient Multi-Objective Evolutionary Algorithm (MOEA), the NSGA-II (Deb et al., 2000), resulting into solutions which exhibit optimality in different aspects. The benefit of using such a method is that the design space is explored in a systematic manner, yielding solutions which are almost certain to meet some optimality criteria. A the same time, the designer is freed from the need to commit a priori to the importance of the different design goals.
A component for Grasshopper has been developed as part of the thesis and in collaboration with Dr. Bittermann and the chair of Design Informatics at TU Delft, which implements the aforementioned MOEA and additionally some state of the art advancements. The component is currently in a closed beta test.
The proposed approach has been evaluated in a case study that concerns an alternative design for the New Doha International Airport in Qatar.
Chatzikonstantinou, I., "Evolutionary Computation and Parametric Pattern Generation for Airport Terminal Design", MSc Thesis, TU Delft, Delft, Netherlands, 2011
Chatzikonstantinou, I., Sariyildiz, S., Bitterman, MS., "Conceptual Airport Terminal Design using Evolutionary Computation", IEEE Congress on Evolutionary Computation, pp. 2245-2252, Sendai, Japan, 2015