Artificial Neural Networks as an Architectural Design Tool-Generating New Detail Forms Based On the Roman Corinthian Order Capital

World Multidisciplinary Civil Engineering-Architecture-Urban Planning Symposium - WMCAUS

土木建筑工程

Radziszewski, Kacper^1

Gdansk University of Technology, Faculty of Architecture, Narutowicza 11/12, Gdansk

80-233, Poland^1

关键词: Artificial networks;    Backward propagation;    Cartesian coordinate system;    Computational experiment;    Scope of application;    Spherical coordinate systems;    Supervised learning methods;    Training data sets;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/245/6/062030/pdf
DOI  :  10.1088/1757-899X/245/6/062030

学科分类：土木及结构工程学

来源: IOP

【 摘 要 】

The following paper presents the results of the research in the field of the machine learning, investigating the scope of application of the artificial neural networks algorithms as a tool in architectural design. The computational experiment was held using the backward propagation of errors method of training the artificial neural network, which was trained based on the geometry of the details of the Roman Corinthian order capital. During the experiment, as an input training data set, five local geometry parameters combined has given the best results: Theta, Pi, Rho in spherical coordinate system based on the capital volume centroid, followed by Z value of the Cartesian coordinate system and a distance from vertical planes created based on the capital symmetry. Additionally during the experiment, artificial neural network hidden layers optimal count and structure was found, giving results of the error below 0.2% for the mentioned before input parameters. Once successfully trained artificial network, was able to mimic the details composition on any other geometry type given. Despite of calculating the transformed geometry locally and separately for each of the thousands of surface points, system could create visually attractive and diverse, complex patterns. Designed tool, based on the supervised learning method of machine learning, gives possibility of generating new architectural forms- free of the designer's imagination bounds. Implementing the infinitely broad computational methods of machine learning, or Artificial Intelligence in general, not only could accelerate and simplify the design process, but give an opportunity to explore never seen before, unpredictable forms or everyday architectural practice solutions.