Society for Computer Simulation International (SimAUD '13)
Abstract
Adaptive architecture is expected to improve the performance of buildings and create more efficient building systems. One of the major research areas under this scope is the adaptive behavior of structural elements affected by load distribution. In order to achieve this, current studies develop structures that adapt by either following a database of pre-calculated equilibrium solutions or using self-learning algorithms to acquire active control systems to structures. This paper examined a case study element, which demonstrates an adaptive behavior in real time, based on self-learning abilities. The focus of this experiment was to gain control over a structural system as a whole (not only on a singular component) according to both objective and subjective parameters, that is, both load distribution parameters and spatial parameters, which are design related. The examined structural element was a canopy, situated in a dynamic environment that brought a change in the element’s load distribution. The learning ability was given by applying a supervised learning algorithm—Artificial Neural Network (ANN)—on a physical prototype. The ANN was trained by an optimized database of finite solutions, which was created by a Genetic Algorithm. Through this method, complex calculations are conducted “offline”, and the component operates in a “decision-making” mode in real time, adapting to a versatile environment while using minimal computational resources. Results show that the case study successfully exhibited self-learning and acquired the ability to adapt to unpredictable changing forces while keeping certain design requirements. This method can be applied over different structural elements (façade elements, canopies, structural components, etc.) to achieve adaptation to various parameters with an unpredictable pattern, such as human behavior or weather conditions
