Assessing environmental performance in early building design stage: An integrated parametric design and machine learning method
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[1] Godfried Augenbroe,et al. Analysis of uncertainty in building design evaluations and its implications , 2002 .
[2] Sarel Lavy,et al. Identification of parameters for embodied energy measurement: A literature review , 2010 .
[3] Grace K C Ding,et al. Sustainable construction--the role of environmental assessment tools. , 2008, Journal of environmental management.
[4] S. Ranji Ranjithan,et al. Multivariate regression as an energy assessment tool in early building design , 2012 .
[5] Mohammad Mottahedi,et al. On the development of multi-linear regression analysis to assess energy consumption in the early stages of building design , 2014 .
[6] Ahmad Eltaweel,et al. Parametric design and daylighting: A literature review , 2017 .
[7] Su Ying,et al. Assessment of building energy consumption and environmental impact based on life cycle theory , 2009 .
[8] Wei Tian,et al. Relationship between built form and energy performance of office buildings in a severe cold Chinese region , 2017 .
[9] Baizhan Li,et al. Urbanisation and its impact on building energy consumption and efficiency in China , 2009 .
[10] Dominik Holzer,et al. Design exploration supported by digital tool ecologies , 2016 .
[11] Kyong Ju Kim,et al. Life cycle assessment based environmental impact estimation model for pre-stressed concrete beam bridge in the early design phase , 2017 .
[12] Durga L. Shrestha,et al. Machine learning approaches for estimation of prediction interval for the model output , 2006, Neural Networks.
[13] Shinsuke Kato,et al. A new method for reusing building information models of past projects to optimize the default configuration for performance simulations , 2014 .
[14] José R. Vázquez-Canteli,et al. Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities , 2019, Sustainable Cities and Society.
[15] Paulo Santos,et al. A macro-component approach for the assessment of building sustainability in early stages of design , 2014 .
[16] Q. M. Jonathan Wu,et al. Human face recognition based on multidimensional PCA and extreme learning machine , 2011, Pattern Recognit..
[17] C. J. Schwarz,et al. Sampling, Regression, Experimental Design and Analysis for Environmental Scientists, Biologists, and Resource Managers , 2011 .
[18] Trevor Hastie,et al. The Elements of Statistical Learning , 2001 .
[19] I. G. Capeluto,et al. Advice tool for early design stages of intelligent facades based on energy and visual comfort approach , 2009 .
[20] Massimiliano Manfren,et al. Calibration and uncertainty analysis for computer models – A meta-model based approach for integrated building energy simulation , 2013 .
[21] Chun-Hsien Chen,et al. A heuristic-based approach to conceptual design , 2009 .
[22] Wassim Jabi,et al. Parametric Design for Architecture , 2013 .
[23] M. Huijbregts,et al. Evaluating uncertainty in environmental life-cycle assessment. A case study comparing two insulation options for a Dutch one-family dwelling. , 2003, Environmental science & technology.
[24] Sarel Lavy,et al. Need for an embodied energy measurement protocol for buildings: A review paper , 2012 .
[25] Rasmus Lund Jensen,et al. Building simulations supporting decision making in early design – A review , 2016 .
[26] Shady Attia,et al. Simulation-based decision support tool for early stages of zero-energy building design , 2012 .
[27] James C. Bezdek,et al. Objective Function Clustering , 1981 .
[28] Xiao-Jun Zeng,et al. Fuzzy C-means++: Fuzzy C-means with effective seeding initialization , 2015, Expert Syst. Appl..
[29] Chee Kheong Siew,et al. Extreme learning machine: Theory and applications , 2006, Neurocomputing.
[30] Paul Strachan,et al. Practical application of uncertainty analysis , 2001 .
[31] B. Muthén,et al. How to Use a Monte Carlo Study to Decide on Sample Size and Determine Power , 2002 .
[32] Dominik T. Matt,et al. Parametric and Generative Design techniques in mass-production environments as effective enablers of Industry 4.0 approaches in the Building Industry , 2018, Automation in Construction.
[33] Lei Zhang,et al. Life cycle assessment of the air emissions during building construction process: A case study in Hong Kong , 2013 .
[34] Jason Jianjun Gu,et al. An Efficient Method for Traffic Sign Recognition Based on Extreme Learning Machine , 2017, IEEE Transactions on Cybernetics.
[35] Yannis Dimopoulos,et al. Use of some sensitivity criteria for choosing networks with good generalization ability , 1995, Neural Processing Letters.
[36] Jason Brown,et al. Assessment of uncertainty and confidence in building design exploration , 2015, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.
[37] Kamel Al-khaled,et al. Energy saving potential for residential buildings in hot climates: The case of Oman , 2019, Sustainable Cities and Society.
[38] Yimin Zhu,et al. Data-driven solutions for building environmental impact assessment , 2015, Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015).
[39] Arno Schlueter,et al. Building information model based energy/exergy performance assessment in early design stages , 2009 .
[40] J. Uma Maheswari,et al. Design iteration in construction projects Review and directions , 2017 .
[41] Dz Z. Li,et al. A methodology for estimating the life-cycle carbon efficiency of a residential building , 2013 .
[42] J. C. Dunn,et al. A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .