Cumincad : CUMINCAD References : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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	Chen Y, Hong T, Luo X, et al (2019) Development of city buildings dataset for urban building energy modeling , Energy Build 2019; 183: 252–265

	Chen, Ch, You, Ch and Mai, X (2019) Research on Spatial Morphology of Traditional Settlements Based on Spatial Syntax - A Case Study of Xiuxi Village , Open Journal of Social Sciences, 2019, 7, pp. 159-171

	Chen, Chun-Yen, Chang, Teng-Wen, Hsiao, Chi-Fu and Huang, Hsin-Yi (2019) Developing and Interactive Fabrication Process of Maker Based on "Seeing-Moving-Seeing"Model. , DigitalFUTURES,edited Philip.F.Y ,Yi.Min(Mike).X,JiaWei Y,Chao Y .Springer., Shanghai, pp. 312-321

	Chen, D, Luo, D, Xu, W, Luo, C, Shen, L, Yan, X and Wang, T (2019) Re-perceive 3D printing with Artificial Intelligence , Proceedings of the 37th eCAADe/23nd SIGraDi Conference, Porto, Portugal, pp. 443-450

	Cheng, X, Lu, Y, Li, Y and Xiao, J (2019) Quantifying and visualizing value exchanges in building information modeling (BIM) projects , Automation in Construction, 99, pp. 91-108

	Colomina, B. (2019) X-Rays Architecture , Baden: Lars Müller Publishers

	del Campo, M, Manninger, S and Carlson, A (2020) A Question of Style , Yuan, PF, Xie, M, Leach, N, Yao, J and Wang, X (eds), Architectural Intelligence: Selected Papers from the 1st International Conference on Computational Design and Robotic Fabrication (CDRF 2019), Springer, Singapore, pp. 171-188

	Di, S, Li, Z, Tang, R, Pan, X, Liu, H and Niu, Y (2019) Urban green space classification and water consumption analysis with remote-sensing technology: a case study in Beijing, China , International Journal of Remote Sensing, 40, pp. 1909-1929

	Dong, Y., X. Cui, X. Yin, Y. Chen and H. Guo (2019) Assessment of Energy Saving Potential by Replacing Conventional Materials by Cross Laminated Timber (CLT)-A Case Study of Office Buildings in China , Applied Sciences, 9(5), 858

	Du, M., Niu, N., & Hu, X. (2019) Techniques for Interpretable Machine Learning , Communications of the ACM, 63(1), 68-77. Available at: https://doi.org/1.1145/3359786.

	Fei, M. A., Fr, A., Kfy, B., Yg, A., Cz, A., Dan, G. A. (2019) The spatial coupling effect between urban public transport and commercial complexes: A network centrality perspective - ScienceDirect , Sustainable Cities and Society, 50, 101645-101645.Fotheringham, A. S., Yang, W., Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.Harris, P., Fotheringham, A. S., Crespo, R., Charlton, M. (2018). Inference in multi-scale geographically weighted regression. WILEY, 43(3), 399.Jia, J., Zhang, X. (2021). A human-scale investigation into economic benefits of urban green and blue infrastructure based on big data and machine learning: A case study of Wuhan. Journal of Cleaner Production, 316, 128321.Oshan, T. M., Li, Z., Kang, W., Wolf, L. J., Fotheringham, A. S. (2019). MGWR: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale. International Journal of Geo-Information, 8(6), 269.Rifkin, J. (2013). The Third Industrial Revolution. International Study Reference, 6(1), 8-11.Yu H, Fotheringham A S, Li Z, et al. (2019). Inference in multi-scale geographically weighted regression. Geographical Analysis, 52(1), 87-106

	Gao, X., & Pishdad-bozorgi, P. (2019) BIM-enabled facilities operation and maintenance: A review , Advanced Engineering Informatics, 39(November), 227–247

	Guo, X., Zhang, W. S. & Zhong, W. L. (2014) Doing topology optimization explicitly and geometrically-a new moving morphable components based framework , Journal of Applied Mechanics, 81, 081009. https://doi.org/10.1115/1.4027609He, L. W., Matthew, G., & Song, X. Y. (2019). A python script for adaptive layout optimization of trusses. Structural and Multidisciplinary Optimization, 60, 835-847. https://doi.org/10.1007/s00158-019-02226-6Liu, J. K., Gaynor A. T., Chen, S. k., Kang, Z., Suresh, K., Takezawa, A., Li, L., Kato, J. J, Tang, J. Y., Wang, Charlie C. L., Cheng, L., Liang, X., & To, A. C. (2018). Current and future trends in topology optimization for additive manufacturing. Structural and Multidisciplinary Optimization, 57, 2457–2483. https://doi.org/10.1007/s00158-018-1994-3Parkes, E. W. (1975) Joints in optimization frameworks. International Journal of Solids and Structures, 11, 1017-1022

	Guo, Z, Wang, X and Yuan, F (2019) Sensing Human Behavior in the Built Environment , Proceedings of 18th International Conference, CAAD Futures 2019, Daejeon, pp. 378-388

	Guo, Z, Wang, X and Yuan, P (2019) Sensing Human Behavior in the Built Environment , Hello, Culture! Proceedings18th International Conference, CAAD Futures 2019, Daejeon, Korea, pp. 378-388

	Guo, Z., Wang, X., & Yuan, P. (2019) Sensing Human Behavior in the Built Environment , Hello, Culture! Proceedings18th International Conference, CAAD Futures 2019 (pp. 378-388)

	He, W., & Yang, X. (2019) Artificial Intelligence Design, from Research to Practice , Proceedings of the 2019 DigitalFUTURES (pp. 189-198). Springer. https://doi.org/10.1007/978-981-13-8153-9_17

	Heeren, N., & Fishman, T. (2019) A database seed for a community-driven material intensity research platform , Scientific data, 6(1), 23. https://doi.org/10.1038/s41597-019-0021-x

	Hong, T., Chen, Y., Luo, X., Luo, N. & Lee, S. H. (2020) Ten questions on urban building energy modeling , Building and Environment, 168. https://doi.org/10.1016/j.buildenv.2019.106508

	Hu X, Chong H-Y, Wang X, et al. (2019) Understanding stakeholders in off-site manufacturing: a literature review , J Constr Eng Manag; 145: 03119003

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