Energy refurbishment planning of Italian school buildings using data-driven predictive models
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[1] S. Pampanin,et al. Displacement incompatibility shape functions between masonry infill wall panels and reinforced concrete frames , 2023, Bulletin of Earthquake Engineering.
[2] S. Pampanin,et al. Seismic assessment and finite element modeling of traditional vs innovative point fixed glass facade systems (PFGFS) , 2023, Bulletin of Earthquake Engineering.
[3] R. Monteiro,et al. Optimal Combined Seismic and Energy Efficiency Retrofitting for Existing Buildings in Italy , 2023, Journal of Structural Engineering.
[4] Adhurim Haxhimusa,et al. Optimum insulation thickness design of exterior walls and overhauling cost to enhance the energy efficiency of Albanian's building stock , 2022, Journal of Cleaner Production.
[5] M. Overend,et al. A probabilistic-based framework for the integrated assessment of seismic and energy economic losses of buildings , 2022, Engineering Structures.
[6] S. Giovinazzi,et al. A Framework and Tool for Knowledge-Based Seismic Risk Assessment of School Buildings: SLaMA-School , 2022, Sustainability.
[7] K. Hewage,et al. Artificial Neural Network for Predicting Building Energy Performance: A Surrogate Energy Retrofits Decision Support Framework , 2022, Buildings.
[8] S. Pampanin,et al. Shake table tests of concrete anchors for non-structural components including innovative and alternative anchorage detailing , 2022, Bulletin of Earthquake Engineering.
[9] C. Butenweg,et al. Experimental testing of decoupled masonry infills with steel anchors for out-of-plane support under combined in-plane and out-of-plane seismic loading , 2022, Construction and Building Materials.
[10] G. Ciulla,et al. An intelligent way to predict the building thermal needs: ANNs and optimization , 2021, Expert Syst. Appl..
[11] A. Prota,et al. Review of methods for the combined assessment of seismic resilience and energy efficiency towards sustainable retrofitting of existing European buildings , 2021, Sustainable Cities and Society.
[12] Vivian Loftness,et al. Using Machine Learning to Predict Retrofit Effects for a Commercial Building Portfolio , 2021, Energies.
[13] A. C. Costa,et al. Shake‐table tests of innovative drift sensitive nonstructural elements in a low‐damage structural system , 2021, Earthquake Engineering & Structural Dynamics.
[14] A. D'Amico,et al. Multiple criteria assessment of methods for forecasting building thermal energy demand , 2020 .
[15] Andreas Sumper,et al. A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings , 2020 .
[16] Kristina Mjörnell,et al. Using Machine Learning to Enrich Building Databases—Methods for Tailored Energy Retrofits , 2020, Energies.
[17] Gil-Tae Kim,et al. Analysis of the Effects of Strengthening Building Energy Policy on Multifamily Residential Buildings in South Korea , 2020, Sustainability.
[18] Wei Feng,et al. A novel improved model for building energy consumption prediction based on model integration , 2020, Applied Energy.
[19] F. Minelli,et al. Retrofitting RC infills by a glass fiber mesh reinforced overlay and steel dowels: experimental and numerical study , 2020 .
[20] A. D'Amico,et al. Building energy performance forecasting: A multiple linear regression approach , 2019, Applied Energy.
[21] Ivan Glesk,et al. Tuning machine learning models for prediction of building energy loads , 2019, Sustainable Cities and Society.
[22] Guido Magenes,et al. Innovative solution for seismic-resistant masonry infills with sliding joints: in-plane experimental performance , 2018, Engineering Structures.
[23] Ivan Glesk,et al. Machine learning for estimation of building energy consumption and performance: a review , 2018, Visualization in Engineering.
[24] D. Bournas. Concurrent seismic and energy retrofitting of RC and masonry building envelopes using inorganic textile-based composites combined with insulation materials: A new concept , 2018, Composites Part B: Engineering.
[25] S. Pampanin,et al. Shake Table Tests of Post-Installed Anchors with Supplemental Damping , 2018 .
[26] Matthew J. Eckelman,et al. Predictive modeling for US commercial building energy use: A comparison of existing statistical and machine learning algorithms using CBECS microdata , 2018 .
[27] Yong Shi,et al. A review of data-driven approaches for prediction and classification of building energy consumption , 2018 .
[28] Valerio Lo Brano,et al. Evaluation of building heating loads with dimensional analysis: application of the Buckingham π theorem , 2017 .
[29] Jianqiang Yi,et al. Building Energy Consumption Prediction: An Extreme Deep Learning Approach , 2017 .
[30] S. Pampanin,et al. Rocking Cantilever Clay Brick Infill Wall Panels: A Novel Low Damage Infill Wall System , 2017 .
[31] Ezio Giuriani,et al. Combining seismic retrofit with energy refurbishment for the sustainable renovation of RC buildings: a proof of concept , 2017 .
[32] Zeyu Wang,et al. A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models , 2017 .
[33] Jonathan Shi,et al. Artificial Intelligent Models for Improved Prediction of Residential Space Heating , 2016 .
[34] A. Prota,et al. Experimental Investigation of Exterior RC Beam-Column Joints Retrofitted with FRP Systems , 2014 .
[35] Mohammad Yusri Hassan,et al. A review on applications of ANN and SVM for building electrical energy consumption forecasting , 2014 .
[36] A. Kialashaki,et al. Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks , 2013 .
[37] Sylvain Robert,et al. State of the art in building modelling and energy performances prediction: A review , 2013 .
[38] Frédéric Magoulès,et al. A review on the prediction of building energy consumption , 2012 .
[39] S. Pampanin,et al. Assessment and Design Procedure for the Seismic Retrofit of Reinforced Concrete Beam-Column Joints using FRP Composite Materials , 2012 .
[40] Jesper Kragh,et al. Use of Building Typologies for Energy Performance Assessment of National Building Stocks. Existent Experiences in European Countries and Common Approach , 2010 .
[41] Stefano Pampanin,et al. Experimental and Numerical Validation of Selective Weakening Retrofit for Existing Non-Ductile R.C. Frames , 2009 .
[42] V. Bianco,et al. Electricity consumption forecasting in Italy using linear regression models , 2009 .
[43] V. Ismet Ugursal,et al. Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector , 2008 .
[44] Stefano Pampanin,et al. Development and validation of a metallic haunch seismic retrofit solution for existing under‐designed RC frame buildings , 2006 .
[45] Andre Filiatrault,et al. Posttensioned Energy Dissipating Connections for Moment-Resisting Steel Frames , 2002 .
[46] Sri Sritharan,et al. Preliminary results and conclusions from the PRESSS five-story precast concrete test Building , 1999 .
[47] Chiara Passoni,et al. Redefining the concept of sustainable renovation of buildings: State of the art and an LCT-based design framework , 2021 .
[48] Donato,et al. Combined retrofit solutions for seismic resilience and energy efficiency of reinforced concrete residential buildings with infill walls. , 2019 .
[49] Stefano Pampanin,et al. Displacement-Based Retrofit of Existing Reinforced Concrete Frames Using Alternative Steel Brace Systems , 2019 .
[50] Nora El-Gohary,et al. A review of data-driven building energy consumption prediction studies , 2018 .
[51] Andrea Belleri,et al. Does seismic risk affect the environmental impact of existing buildings , 2016 .
[52] Gian Michele Calvi,et al. Energy Efficiency and Seismic Resilience: A Common Approach , 2016 .
[53] Alessandro Palermo,et al. Improving the Seismic Performance of Existing Reinforced Concrete Buildings using Advanced Rocking Wall Solutions , 2007 .
[54] Alessandro Palermo,et al. Seismic design of multi-storey buildings using Laminated Veneer Lumber (LVL) , 2005 .
[55] Stefano PAMPANIN,et al. SEISMIC REPONSE OF GRAVITY-LOAD DESIGN FRAMES WITH MASONRY INFILLS , 2002 .
[56] M. Parti,et al. The Total and Appliance-Specific Conditional Demand for Electricity in the Household Sector , 1980 .