Abstract
A significant number of school buildings in Italy require seismic and energy retrofits based on National laws, which contribute to the school environment's characteristics and health and safety in buildings. Moreover, government initiatives to promote ambitious national plans for the renovation and construction of new school buildings are gaining vast attention. For this purpose, the Ministry of Education, with the local authorities’ collaboration, carries out a database to register national school buildings and their level of consistency and functionality, which is the fundamental knowledge tool for planning interventions in the sector. However, it does not provide a guideline to estimate future interventions’ costs. This research aims to design a retrofitting cost estimation model for energy and seismic improvement and adaptation interventions using Artificial Neural Networks. It can serve as a beneficial tool for forecasting expenses based on the interrelated building features, which the public administration can use to optimize the management and planning of school buildings’ funds. The proposed work focuses on a small sample of over 200 school buildings and their seismic and energy retrofitting costs. The ANN model uses the parameters of the case studies as the input to train the network to estimate the retrofitting cost of other projects based on the historical data. The parameters are categorized into three groups of features: (i) building's characteristics, e.g., construction year and the number of floors, (ii) energy retrofit parameters, e.g., class heating energy consumption, and (iii) seismic retrofit parameters, e.g., seismic zone and structural type. Therefore, the goal is to facilitate the financial feasibility assessments and optimize the available resources related to the planning of interventions. The proposed model will contribute significantly to school buildings’ resilience as a single integrated space, which has the characteristics of habitability, flexibility, functionality, comfort, and well-being.
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References
Ali, U. et al. (2018). An intelligent knowledge-based energy retrofit recommendation system for residential buildings at an urban scale. In ASHRAE and IBPSA-USA Building Simulation Conference (pp. 84–91).
Amasyali, K., & El-Gohary, N. M. (2018). A review of data-driven building energy consumption prediction studies. Renewable and Sustainable Energy Reviews, 81, 1192–1205. https://doi.org/10.1016/j.rser.2017.04.095
Asadi, E., et al. (2014). Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application. Energy and Buildings, 81, 444–456. https://doi.org/10.1016/j.enbuild.2014.06.009
Asadi, E., Salman, A. M., & Li, Y. (2019). Multi-criteria decision-making for seismic resilience and sustainability assessment of diagrid buildings. Engineering Structures, 191(April), 229–246. https://doi.org/10.1016/j.engstruct.2019.04.049
Ascione, F., et al. (2017a). Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach. Energy, 118, 999–1017. https://doi.org/10.1016/j.energy.2016.10.126
Ascione, F., et al. (2017b). CASA, cost-optimal analysis by multi-objective optimisation and artificial neural networks: A new framework for the robust assessment of cost-optimal energy retrofit, feasible for any building. Energy and Buildings, 146, 200–219. https://doi.org/10.1016/j.enbuild.2017.04.069
Azadeh, A., Babazadeh, R., & Asadzadeh, S. M. (2013). Optimum estimation and forecasting of renewable energy consumption by artificial neural networks. Renewable and Sustainable Energy Reviews, 27, 605–612. https://doi.org/10.1016/j.rser.2013.07.007
Carofilis, W., et al. (2020). Seismic retrofit of existing school buildings in Italy: Performance evaluation and loss estimation. Engineering Structures, 225(August), 111243. https://doi.org/10.1016/j.engstruct.2020.111243
Caterino, N., et al. (2021). A BIM-based decision-making framework for optimal seismic retrofit of existing buildings. Engineering Structures, 242(May), 112544. https://doi.org/10.1016/j.engstruct.2021.112544
Darko, A., et al. (2020). Artificial intelligence in the AEC industry : Scientometric analysis and visualization of research activities. Automation in Construction, 112(January), 103081. https://doi.org/10.1016/j.autcon.2020.103081
De Santoli, L. et al. (2014). Energy performance assessment and a retrofit strategies in public school buildings in Rome. Energy and Buildings, 68(PARTA), 196–202. doi: https://doi.org/10.1016/j.enbuild.2013.08.028.
De Giuli, V., Da Pos, O., & De Carli, M. (2012). Indoor environmental quality and pupil perception in Italian primary schools. Building and Environment, 56, 335–345. https://doi.org/10.1016/j.buildenv.2012.03.024
Deb, C., Dai, Z., & Schlueter, A. (2021). A machine learning-based framework for cost-optimal building retrofit. Applied Energy, 294, 116990. https://doi.org/10.1016/j.apenergy.2021.116990
Edilizia scolastica—MIUR. (no date). Retreived October 1, 2021, from https://www.istruzione.it/edilizia_scolastica/anagrafe.shtml.
EU-Energy. (2018). Energy for Europe by European Commission.
Ferreira, M. & Almeida, M. (2015). Benefits from energy related building renovation beyond costs, energy and emissions. In Energy Procedia (pp. 2397–2402). Elsevier B.V.
Geyer, P., Schlüter, A., & Cisar, S. (2017). Application of clustering for the development of retrofit strategies for large building stocks. Advanced Engineering Informatics, 31, 32–47. https://doi.org/10.1016/j.aei.2016.02.001
Guo, Y., et al. (2017). A thermal response time ahead energy demand prediction strategy for building heating system using machine learning methods. Energy Procedia, 142, 1003–1008. https://doi.org/10.1016/j.egypro.2017.12.346
Håkansson, A. et al. (2013). Sustainability in energy and buildings: proceedings of the 4th international conference on sustainability in energy and buildings (SEB’12). Smart Innovation, Systems and Technologies, 22, 209–227. doi: https://doi.org/10.1007/978-3-642-36645-1.
Jafari, A. and Valentin, V. (2018). Proposing a conceptual decision support system for building energy retrofits considering sustainable triple bottom line criteria. In Construction Research Congress 2018: Sustainable Design and Construction and Education—Selected Papers from the Construction Research Congress 2018 (pp. 553–563). doi: https://doi.org/10.1061/9780784481301.055.
Legambiente. (2021). XX RAPPORTO sulla qualità dell’edilizia scolastica e dei servizi.
Lohse, R., Staller, H. and Riel, M. (2016). The economic challenges of deep energy renovation—Differences, similarities, and possible solutions in central Europe: Austria and Germany. In ASHRAE Conference-Papers (pp. 69–87). American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc. (ASHRAE).
Marasco, D. E., & Kontokosta, C. E. (2016). Applications of machine learning methods to identifying and predicting building retrofit opportunities. Energy and Buildings, 128, 431–441. https://doi.org/10.1016/j.enbuild.2016.06.092
Ministero delle Infrastrutture (2008) D.M. 14/01/2008.
Park, Y. S. and Lek, S. (2016). Artificial Neural Networks: Multilayer Perceptron for Ecological Modeling, Developments in Environmental Modelling. Elsevier. doi: https://doi.org/10.1016/B978-0-444-63623-2.00007-4.
Re Cecconi, F., Moretti, N. and Tagliabue, L. C. (2019). Application of artificial neutral network and geographic information system to evaluate retrofit potential in public school buildings. Renewable and Sustainable Energy Reviews, 110(December 2018), 266–277. doi: https://doi.org/10.1016/j.rser.2019.04.073.
Scherer, R. J., & Katranuschkov, P. (2018). BIMification: How to create and use BIM for retrofitting. Advanced Engineering Informatics, 38(May), 54–66. https://doi.org/10.1016/j.aei.2018.05.007
Seghezzi, E. and Masera, G. (2017). Identification of technological and installation-related parameters for a multi-criteria approach to building retrofit. In Procedia Engineering (pp. 1056–1064). The Author(s). doi: https://doi.org/10.1016/j.proeng.2017.04.265.
Seyedzadeh, S., et al. (2020). Machine learning modelling for predicting non-domestic buildings energy performance: A model to support deep energy retrofit decision-making. Applied Energy, 279(May), 115908. https://doi.org/10.1016/j.apenergy.2020.115908
Sherstobitoff, J., Taylor, G. and Shuttleworth, J. (2010). Seismic retrofit strategies for historical clay brick masonry school buildings; British Columbia, Canada. In 9th US National and 10th Canadian Conference on Earthquake Engineering 2010, Including Papers from the 4th International Tsunami Symposium (pp. 988–997).
Stojiljković, M. M., Vučković, G. D. and Ignjatović, M. G. (2021). Classification of retrofit measures for residential buildings according to the global cost. Thermal Science, 25(4 Part A), 2677–2689. doi: https://doi.org/10.2298/TSCI200825306S.
Thrampoulidis, E., et al. (2021). A machine learning-based surrogate model to approximate optimal building retrofit solutions. Applied Energy, 281, 116024. https://doi.org/10.1016/j.apenergy.2020.116024
Wei, Y., et al. (2018). A review of data-driven approaches for prediction and classification of building energy consumption. Renewable and Sustainable Energy Reviews, 82, 1027–1047. https://doi.org/10.1016/j.rser.2017.09.108
Woo, J. H., & Menassa, C. (2014). Virtual Retrofit Model for aging commercial buildings in a smart grid environment. Energy and Buildings, 80, 424–435. https://doi.org/10.1016/j.enbuild.2014.05.004
Xu, Y., Loftness, V., & Severnini, E. (2021). Using machine learning to predict retrofit effects for a commercial building portfolio. Energies, 14(14), 1–24. https://doi.org/10.3390/en14144334
Yaseen, Z. M. et al. (2020). Prediction of risk delay in construction projects using a hybrid artificial intelligence model (pp. 1–14).
Zhang, Y., & Barrett, P. (2010). Findings from a post-occupancy evaluation in the UK primary schools sector. Facilities, 28(13), 641–656. https://doi.org/10.1108/02632771011083685
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Khodabakhshian, A., Rampini, L., Vasapollo, C., Panarelli, G., Cecconi, F.R. (2023). Application of Machine Learning to Estimate Retrofitting Cost of School Buildings. In: Krüger, E.L., Karunathilake, H.P., Alam, T. (eds) Resilient and Responsible Smart Cities. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-031-20182-0_16
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