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Adaptive kinetic structural behavior through machine learning: Optimizing the process of kinematic transformation using artificial neural networks

Published online by Cambridge University Press:  07 October 2015

Odysseas Kontovourkis ,
Marios C. Phocas  and
Ifigenia Lamprou
Odysseas Kontovourkis*
Affiliation:
Department of Architecture, University of Cyprus, Nicosia, Cyprus
Marios C. Phocas
Affiliation:
Department of Architecture, University of Cyprus, Nicosia, Cyprus
Ifigenia Lamprou
Affiliation:
Department of Architecture, University of Cyprus, Nicosia, Cyprus
*
Reprint requests to: Odysseas Kontovourkis, Department of Architecture, Faculty of Engineering, University of Cyprus, PO Box 20537, Nicosia 1678, Cyprus. E-mail: kontovourkis.odysseas@ucy.ac.cy
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Abstract

Nowadays, on the basis of significant work carried out, architectural adaption structures are considered to be intelligent entities, able to react to various internal or external influences. Their adaptive behavior can be examined in a digital or physical environment, generating a variety of alternative solutions or structural transformations. These are controlled through different computational approaches, ranging from interactive exploration ones, producing alternative emergent results, to automate optimization ones, resulting in acceptable fitting solutions. This paper examines the adaptive behavior of a kinetic structure, aiming to explore suitable solutions resulting in final appropriate shapes during the transformation process. A machine learning methodology that implements an artificial neural networks algorithm is integrated to the suggested structure. The latter is formed by units articulated together in a sequential composition consisting of primary soft mechanisms and secondary rigid components that are responsible for its reconfiguration and stiffness. A number of case studies that respond to unstructured environments are set as examples, to test the effectiveness of the proposed methodology to be used for handling a large number of input data and to optimize the complex and nonlinear transformation behavior of the kinetic system at the global level, as a result of the units’ local activation that influences nearby units in a chaotic and unpredictable manner.

Keywords

AdaptationArtificial Neural NetworksKinetic StructureMachine LearningOptimization
Type
Special Issue Articles
Information
AI EDAM , Volume 29 , Special Issue 4: Generative and evolutionary design exploration , November 2015 , pp. 371 - 391
Copyright
Copyright © Cambridge University Press 2015 

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