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

PDF papers
References

Hits 61 to 80 of 27198

Reformat results as: short short into frame detailed detailed into frame

Details	Citation	Select
	Austroads (2021) Guide to Road Design , ISBN 978-1-922382-48-, Austroads Ltd

	Author et al (2019) Understanding the Occupant Behaviours in Islamic Homes to Close the Gap in Building Performance Simulation: A Case Study of Houses in Riyadh , IBPSA2019

	Avent, R. (2011) One Path to better Jobs: More Density in Cities , The Gated City

	Avgerinou and Winter (1999) Considering BEATL’s evaluation design: A multi-dimensional case study , Appendix II of Paper submitted for publication to SEDA, Proc. SEDA Conference 14 th -16 th April 1999

	Avineri E. (2003) A Cumulative Prospect Theory Approach To Travel Behaviour Modelling: Waiting Time Paradox Revisited , Paper presented at the EC-Workshop Behavioural responses to ITS, Eindhoven, The Netherlands, 2003

	Aviv, D and Teitelbaum, E (2017) Thermally Informed Bending: Relating Curvature to Heat Generation Through Infrared Sensing , Humanizing Digital Reality, Singapore, pp. 362-371

	Ayagen, Z. and Fleming, U. (1998) Classification of Precedents: A Hybrid Approach to Indexing and Retreiving Design Cases in SEED , CAADRIA’98 Proceedings of the Third Conference on Computer Aided Architectural Design Research in Asia, pp. 435-444

	Ayan, Ö (2009) Cardboard in Architectural Technology and Structural Engineering: A Conceptual Approach to Cardboard Buildings in Architecture , Ph.D. Thesis, ETH

	Stiny, G (1991) How to calculate with shapes , E. Antonsson and J. Cagan (eds), Formal Engineering Design Synthesis, Cambridge University Press, New York, pp. 20–64

	Ayata T, Çam E and Yildiz O (2007) Adaptive neuro-fuzzy inference systems (ANFIS) application to investigate potential use of natural ventilation in new building designs in Turkey , Energy Convers Management 2007; 48(5): 1472–1479.

	Stiny, G (2001) How to calculate with shapes , Antonsson, E and Cagan, J (eds), Formal Engineering Design Synthesis, Cambridge University Press, pp. 20-64

	Stiny, G (2001) How to calculate with shapes , Antonsson, E and Cagan (eds), J (eds), Formal Engineering Design Synthesis, Cambridge University Press, p. 20-64

	Aygen, Z. and Flemming, U. (1998) Classification of Precedents - A Hybrid Approach to Indexing and Retrieving Design Cases in SEED (a Software Environment for the Early Phases of Building Design) , CAADRIA ‘98, Proceedings of The Third Conference on Computer Aided Architectural Design Research in Asia, Osaka (Japan) 22-24 April 1998, pp. 435-444

	Aygen, Z. and Flemming, U. (1998) Classification of Precedents - A Hybrid Approach to Indexing and Retrieving Design Cases in SEED (a Software Environment for the Early Phases of Building Design) , CAADRIA ‘98, Proceedings of The Third Conference on Computer Aided Architectural Design Research in Asia, Osaka (Japan) 22-24 April 1998, pp. 435-444

	Ayoosu, M.I. et al. (2021) Daylighting Evaluation and Optimisation of Window to Wall Ratio for Lecture Theatre in the Tropical Climate , Journal of Daylighting, 8(1), pp. 20-35. Available at: https://doi.org/10.15627/jd.2021.2

	Ayoub M (2019) A multivariate regression to predict daylighting and energy consumption of residential buildings within hybrid settlements in hot-desert climates , Indoor Built Environ 2019; 28: 848–866

	Ayoub M (2020) A review on machine learning algorithms to predict daylighting inside buildings , Sol Energy 2020; 202:249–275

	Ayoub M (2021) Self-organizing profiles to characterize representative temporal settings for daylight simulations , SolEnergy 2021; 214: 248–267

	Ayoub, M. (2019) A multivariate regression to predict daylighting and energy consumption of residential buildings within hybrid settlements in hot-desert climates , Indoor and built environment, 28(6), 848-866. https://doi.org/10.1177/1420326X18798164

	Ayoub, M. (2020) A review on machine learning algorithms to predict daylighting inside buildings , Solar Energy, 202, 249-275. https://doi.org/10.1016/j.solener.2020.03.104Bitmap+ (2022). Available at: https://www.food4rhino.com/en/app/bitmap?lang=en (Accessed: 1 Aril 2022)

For more results click below: