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A Study on Performance Prediction in Early Design Stage of Apartment Housing Using Machine Learning

Authors: Seongjun Kim, Sanghoon Shim, Jinwooung Kim, Jaehwan Jung, Sung-Ah Kim


As the development of information and communication technology, the convergence of machine learning of the ICT area and design is attempted. In this way, it is possible to grasp the correlation between various design elements, which was difficult to grasp, and to reflect this in the design result. In architecture, there is an attempt to predict the performance, which is difficult to grasp in the past, by finding the correlation among multiple factors mainly through machine learning. In architectural design area, some attempts to predict the performance affected by various factors have been tried. With machine learning, it is possible to quickly predict performance. The aim of this study is to propose a model that predicts performance according to the block arrangement of apartment housing through machine learning and the design alternative which satisfies the performance such as the daylight hours in the most similar form to the alternative proposed by the designer. Through this study, a designer can proceed with the design considering various design alternatives and accurate performances quickly from the early design stage.

Keywords: Apartment housing, machine learning, multi-objective optimization, performance prediction.

Digital Object Identifier (DOI):

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[1] P. Merrell, E. Schkufza, V. Koltun, “Computer-generated residential building layouts,” In: ACM Transactions on Graphics (TOG). ACM, pp. 181, 2010.
[2] HS. Kim, SB. Cho, “Genetic algorithm with knowledge-based encoding for interactive fashion design,” PRICAI 2000 Topics in Artificial Intelligence, pp. 404-414, 2000.
[3] IS. Kang, JW. Moon, HC. Park, “Recent Research Trends of Artificial Intelligent Machine Learning in Architectural Field - Review of Domestic and International Journal Papers –,” Journal Of The Architectural Institute Of Korea Structure & Construction, vol. 33, no. 4, pp. 63-68, 2017.
[4] KB. Pratt, DE Bosworth, “A method for the design and analysis of parametric building energy models,” In: Proceedings of Building Simulation 2011: 12th Conference of International Building Performance Association, Sydney, Australia, 2011.
[5] SS. Glian, B. Dilkina, “Sustainable Building Design: A Challenge at the Intersection of Machine Learning and Design Optimization,” In: AAAI Workshop: Computational Sustainability. 2015.
[6] B. Liu, Y. Wei, Y. Zhang, Q. Yang, “Deep Neural Networks for High Dimension, Low Sample Size Data,” Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017, pp. 2287–2293.
[7] G. R PATERSON, “Real-time Energy Use Predictions at the Early Architectural Design Stages with Machine Learning,” 2017, PhD Thesis, UCL (University College London).
[8] K, BUYS, et al., “Virtual data generation based on a human model for machine learning applications,” In: Proceedings of the international Digital Human Modeling Symposium, 2013, pp. 1-9.
[9] G. Dutta, P. Jha, et al., “Artificial neural network models for forecasting stock price index in the Bombay stock exchange,” Journal of Emerging Market Finance, vol. 5, no. 3, pp. 283-295, 2006.
[10] MK. Kim, HS. Park, KD. Song, “Evaluation and Analysis of Building Energy Rating System According to Insulation Performance of Building Envelope in Regional and Building Form of Apartment House,” Korean Journal of Air-Conditioning and Refrigeration Engineering, vol. 25, no. 2, pp. 49-54, 2013.
[11] JH. Lee, DS. Jang, SS. Park, “Deep Learning-Based Corporate Performance Prediction Model Considering Technical Capability,” Sustainability, vol. 9, no. 6, pp. 899, 2017.
[12] BT. Zhang, “Next-Generation Machine Learning Technologies,” Communications of the Korean Institute of Information Scientists and Engineers, pp. 96-107, 2007.
[13] KY. Bae, HS. Jang, DK. Sung, “Solar Power Prediction Based on Machine Learning Scheme and Its Error Analysis,” Proceedings of Symposium of the Korean Institute of communications and Information Sciences, 2017, pp.13-14.
[14] SH. YUN, BH. Ha, “Real-time Estimation on Service Completion Time of Logistics Process for Container Vessels,” Journal of Society for e-Business Studies, vol. 17, no. 2, 2014.
[15] SJ. Lee, HS. Lee, “A Study on the Estimation Method of Concrete Compressive Strength Based on Machine Learning Algorithm Considering Mixture Factor,” Proceedings of Symposium of The Korean Institute of Building Construction, 2017, pp.152-153.
[16] E. Asadi, et al., “Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application,” Energy and Buildings, vol. 81, pp. 444-456, 2014.
[17] SJ. Kim, JW. Kim, JH. Jung, SH. Shim, SA. Kim, “A Study on the Prediction of Consumer’s Preference through the Analysis Apartment Unit Plan,” Proceedings of Symposium of the Korean Institute of communications and Information Sciences, 2017, pp. 25-27.
[18] JG. Eom, “Comparison of customer classification performance using machine learning,” 2010, M.S. Thesis, Konkuk University.
[19] KT. Kim BM. Lee, JW. Kim, "Feasibility of Deep Learning Algorithms for Binary Classification Problems," Journal of Intelligence and Information Systems, pp. 95-108, 2017.
[20] M. Tom, Machine Learning. McGraw-Jill, 1997, pp. 162.
[21] V. Chandranouli, S. Lingireddy, GM. Brion, “Robust training termination criterion for back-propagation ANNs applicable to small data sets,” Journal of computing in civil engineering, vol. 21, no. 1, pp. 39-46, 2007.
[22] N. Srivastava, et al., “Dropout: a simple way to prevent neural networks from overfitting,” Journal of machine learning research, vol. 15, no. 1, pp. 1929-1958, 2014.