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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	Jahn, G., Newnham, C. & van den Berg, N. (2022) Augmented Reality For Construction From Steam Bent Timber , Proceedings of the 27th Annual Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2022: Post carbon (pp. 191-200). The University of New South Wales, the University of Sydney and the University of Technology, Sydney

	Jahn, G., Newnham, C., & Van Den Berg, N. (2022) Augmented Reality for Construction from Steam-bent Timber , Paper presented at the CAADRIA 222, Sydney

	Jiang, Y., Han, Y., Liu, M., & Ye, Y. (2022) Street Vitality and Built Environment Features: a Data-informed Approach from Fourteen Chinese Cities , Sustainable Cities and Society, 79, 13724. http://dx.doi.org/1.116/j.scs.222.13724

	Kennicott, P., (2020) The pandemic has shown us what the future of architecture could be , The Washington Post. [cited 2022 January 15]. Available from: https://www.washingtonpost.com/magazine/2020/07/13/pandemic-has-shown-us-what-future-architecture-could-be/Mitani, J., and Igarashi, T., 2011. "Interactive design of planar curved folding by reflection". Pacific conference on computer graphics and applications-short papers, Kaohsiung, Taiwan, pp. 21-23

	Khamis A, Ibrahim S, Khateb M, et al (2022) Introducing the architecture parametric design procedure: from concept to eecution , IOP Conference Series: Earth and Environmental Science, 2022

	Kharbanda K, Papadopoulou I, Pouliou Pet al (2022) LearnCarbon-A tool for machine learning prediction of global warming potential from abstract designs , Ghent, Belgium: Conference: eCAADe 2022: Co-creating the Future - Inclusion in and through Design, 2022, p 601

	Khemlani, L (2016) City information modeling: Aecbytes , City Information Modeling; AECbytes. Retrieved March 03, 2022, from https://www.aecbytes.com/feature/2016/CityInformationModeling.html

	Kikuchi, N., Fukuda, T., And Yabuki, N. (2022) How a Flooded City Can Be Visualised from Both the Air and the Ground with the City Digital Twin Approach, System Integration of Flood Simulation and Augmented Reality with Drones , POST-CARBON, Proceedings of the 27th CAADRIA Conference, Sydney, Australia, 9-15th April 222

	Kim, J., Hwang, D. U., Son, E. J., Oh, S. H., Kim, W., Kim, Y., & Kwon, G. (2022) Emotion recognition while applying cosmetic cream using deep learning from EEG data; cross-subject analysis , Plos One, 17(11), Article e0274203. https://doi.org/10.1371/journal.pone.0274203

	Koo., Et Al. (2022) How are Neighborhood and Street-level Walkability Factors Associated with Walking Behaviors? a Big Data Approach Using Street View Images , Environment and Behavior, 54(1), 211-241, Available at: https://doi.org/1.1177/1391652111469.

	Li X, Liu S, Zhao L, et al (2022) An integrated building energy performance evaluation method: From parametric modeling to GA-NN based energy consumption prediction modeling , J Build Eng 2022; 45: 103571.

	Li, A., Tian, R., Wang, And, X. And Lu, Y. (2022) Plan Generation from Program Graph , Machine Learning and the City: Applications in Architecture and Urban Design

	Li, R., Zheng, S., Duan, C., Wang, L., & Zhang, C. (2022) Land cover classification from remote sensing images based on multi-scale fully convolutional network , Geo-Spatial Information Science, 25(2), 278-294. https://doi.org/10.1080/10095020.2021.2017237

	Li, X., Liu, S., Zhao, L., Meng, X., Fang, Y., (2022) An integrated building energy performance evaluation method: From parametric modeling to GA-NN based energy consumption prediction modeling , Journal of Building Engineering, 45, 103571 https://doi.org/10.1016/j.jobe.2021.103571

	Li, Y., Yabuki, N., & Fukuda, T. (2022) Measuring visual walkability perception using panoramic street view images, virtual reality, and deep learning , Sustainable Cities and Society, 86, 104140

	Lianjia, (2022) Housing Price Dataset, Retrieved from Https://sz , Lianjia.com/Available at: https://sz.lianjia.com/.

	Lorusso, P., Iuliis, M. D., Marasco, S., Domaneschi, M., Cimellaro, G. P., & Villa, V. (2022) Fire emergency evacuation from a school building using an evolutionary virtual reality platform , Buildings, 12(2), 223

	Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N. & Lee, S.-I. (2019) Explainable AI for Trees: From Local Explanations to Global Understanding , ArXiv:1905.04610 [Cs, Stat]. Retrieved January 10, 2022, from http://arxiv.org/abs/1905.04610Menges, A., & Reichert, S. (2012). Material Capacity: Embedded Responsiveness. Architectural Design, 82(2), 52–59. https://doi.org/10.1002/ad.1379Olsson, A., & Oscarsson, J. (2017). Strength grading on the basis of high resolution laser scanning and dynamic excitation: A full scale investigation of performance. European Journal of Wood and Wood Products, 75(1), 17–31. https://doi.org/10.1007/s00107-016-1102-6Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12(85), 2825–2830

	Mandavilli, A. (2022) New C.D.C. guidelines suggest 70 percent of Americans can stop wearing masks. , The New York Times. Retrieved March 2022, from https://www.nytimes.com/2022/02/25/health/cdc-mask-guidance.html

	Map World, Gov. (2022) Shenzhen Platform for Common Geospatial Information Services, from Https://guangdong , Tianditu.gov.cn/shenzhen/Available at: https://guangdong.tianditu.gov.cn/shenzhen/.

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