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supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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id caadria2018_290
authors Wang, Zhenyu, Shi, Jia, Yu, Chuanfei and Gao, Guoyuan
year 2018
title Automatic Design of Main Pedestrian Entrance of Building Site Based on Machine Learning - A Case Study of Museums in China's Urban Environment
source T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 227-235
summary The main pedestrian entrance of the building site has a direct influence on the use of the buildings, so the selection of the main pedestrian entrance is very important in the process of architectural design. The correct selection of the main pedestrian entrance of building site depends on the experience of designers and environment data collected by designers, the process is time consuming and inefficient, especially when the building site located in complex urban environment. In order to improve the efficiency of design process, we used online map to collect museums information in China as training samples, and constructing artificial neural networks to predict the direction of the main pedestrian entrance. After the training, we get the prediction model with 79% prediction accuracy. Although the accuracy still need to be improved, it creates a new approach to analysis the main pedestrian entrance of the site and worth further researching.
keywords Artificial Neural Network (ANN); Main Pedestrian Entrance of Building Site; Automatic Design
series CAADRIA
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100%; open Brugnaro, G and Hanna, S (2017) Find in CUMINCAD Adaptive Robotic Training Methods for Subtractive Manufacturing , Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture, pp. 164-169

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100%; open Wilkinson, S and Hanna, S (2014) Find in CUMINCAD Approximating computational fluid dynamics for generative tall building design , International Journal of Architectural Computing, 12(2), pp. 155-177

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