Deep Learning-Assisted Discovery of Analogy-Inspired Designs within Peter Collins Analogical Architectural Design Classification Framework

This study focuses on analogical reasoning and deep learning models to enhance the innovative design process in architecture. By constructing multi-layered artificial neural networks, deep learning can derive analogical predictions from structured data to solve complex tasks. Deep learning models interact with analogical thinking patterns in the architectural design process, enabling designers to analyze and draw inspiration from analogical design examples. This study aims to develop a deep learning model that categorizes architectural design examples into specific analogical design classifications. For this purpose, a model based on Convolutional Neural Networks was developed and coded in the Google Colab environment using a dataset of 29,596 visual images, employing Peter Collins' classification system of biological, mechanical, gastronomic, and linguistic analogies. During the training process, the model was trained on images classified according to biological, mechanical, gastronomic, and linguistic categories, achieving an accuracy rate of 98%; however, this rate was recorded as 86% during the testing phase. It was observed that adjustments in the learning rate parameter balanced classification accuracy and training time; lower learning rates reduced accuracy while extending training time. Despite the complexity of architectural images indicated by the 86% accuracy rate on test data, the study emphasizes the model's capacity to achieve accuracy above 95% when confronted with distinct architectural features. In this case, the model allows designers to discover which analogical classification the architectural work to be tested is designed according to, allowing them to develop creative solutions to new design problems. Additionally, this research establishes an interdisciplinary dialogue between artificial intelligence and architecture, providing a foundation for future studies.