Informes de la Construcción, Vol 72, No 557 (2020)

Indicadores Operacionales del Performance y Análisis de Causalidad para Edificios No Residenciales


https://doi.org/10.3989/ic.67792

R. Bortolini
Department of Project and Construction Engineering (DPCE), Group of Construction Research and Innovation (GRIC), Universitat Politècnica de Catalunya (UPC), España
orcid https://orcid.org/0000-0002-6911-4423

N. Forcada
Department of Project and Construction Engineering (DPCE), Group of Construction Research and Innovation (GRIC), Universitat Politècnica de Catalunya (UPC), España
orcid https://orcid.org/0000-0003-2109-4205

Resumen


Durante la operación y el mantenimiento de los activos su performance puede disminuir. Si bien se han desarrollado una serie de herramientas y métodos para facilitar el proceso de evaluación del performance de los edificios, su complejidad y la falta de análisis de causalidad los hacen imprácticos. Este artículo pretende comprender las áreas de performance más relevantes para edificios no residenciales en general, y determinar los Key Performance Indicators (KPI) y su relación. Este estudio se basa en una revisión de la literatura, un focus group y una encuesta. Los resultados revelaron que los indicadores básicos utilizados para evaluar el performance de los edificios están relacionados con la seguridad y el correcto funcionamiento de los activos, la salud y confort, la funcionalidad de los espacios y la eficiencia energética. Los resultados también identificaron las relaciones entre los KPI y los factores externos para desarrollar un modelo causal para evaluar el performance de los edificios.

Palabras clave


Building performance; indicadores; facilities management; gestión de activos; edificios no residenciales

Texto completo:


HTML PDF XML

Referencias


(1) Heo, Y., Choudhary, R., and Augenbroe, G. A. (2012). Calibration of building energy models for retrofit analysis under uncertainty. Energy and Buildings, 47: 550-560. https://doi.org/10.1016/j.enbuild.2011.12.029

(2) Droutsa, K. G., Kontoyiannidis, S., Dascalaki, E. G., and Balaras, C. A. (2016). Mapping the energy performance of hellenic residential buildings from EPC (energy performance certificate) data. Energy, 98: 284-295. https://doi.org/10.1016/j.energy.2015.12.137

(3) Balaras, C. A., Droutsa, K., Dascalaki, E., and Kontoyiannidis, S. (2005). Service life of building elements & installations in European apartment buildings. In 10DBMC International Conférence On Durability of Building Materials and Components, Lyon (April). https://doi.org/10.1016/j.enbuild.2004.09.010

(4) Holopainen, R., Tuomaala, P., Hernandez, P., Häkkinen, T., Piira, K., and Piippo, J. (2014). Comfort assessment in the context of sustainable buildings: Comparison of simplified and detailed human thermal sensation methods. Building and Environment, 71: 60-70. https://doi.org/10.1016/j.buildenv.2013.09.009

(5) Azar, E., Nikolopoulou, C., and Papadopoulos, S. (2016). Integrating and optimizing metrics of sustainable building performance using human-focused agent-based modeling. Applied Energy, 183: 926-937. https://doi.org/10.1016/j.apenergy.2016.09.022

(6) Love, P.E.D., Ahiaga-Dagbui, D.D. and Irani, Z. (2016). Cost overruns in transportation infrastructure projects: Sowing the seeds for a probabilistic theory of causation. Transportation Research Part A: Policy and Practice, 92: 184-194. https://doi.org/10.1016/j.tra.2016.08.007

(7) Bortolini, R. (2019). Enhancing building performance: A Bayesian network model to support Facility Management. PhD thesis, Department of Project and Construction Engineering, Universitat Politècnica de Catalunya, Barcelona.

(8) Bakens, W., Foliente, G., and Jasuja, M. (2005). Engaging stakeholders in performance-based building: lessons from the Performance-Based Building (PeBBu) Network. Building Research & Information, 33(2): 149-158. https://doi.org/10.1080/0961321042000322609

(9) Ibem, E.O., Opoko, A.P., Adeboye, A.B., and Amole, D. (2013). Performance evaluation of residential buildings in public housing estates in Ogun State, Nigeria: Users' satisfaction perspective. Frontiers of Architectural Research, 2(2): 178-190. https://doi.org/10.1016/j.foar.2013.02.001

(10) Ruparathna, R., Hewage, K., and Sadiq, R. (2017). Developing a level of service (LOS) index for operational management of public buildings. Sustainable Cities and Society, 34(June): 159-173. https://doi.org/10.1016/j.scs.2017.06.015

(11) Jensen, P. A., and Maslesa, E. (2015). Value based building renovation - A tool for decision-making and evaluation. Building and Environment, 92: 1-9. https://doi.org/10.1016/j.buildenv.2015.04.008

(12) Wang, S., Yan, C., and Xiao, F. (2012). Quantitative energy performance assessment methods for existing buildings. Energy and Buildings, 55: 873-888. https://doi.org/10.1016/j.enbuild.2012.08.037

(13) Silva, A., de Brito, J., and Gaspar, P. L. (2016). Methodologies for Service Life Prediction of Buildings. In Green Energy and Technology. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-33290-1_7

(14) Serralheiro, M. I., de Brito, J., and Silva, A. (2017). Methodology for service life prediction of architectural concrete facades. Construction and Building Materials, 133: 261-274. https://doi.org/10.1016/j.conbuildmat.2016.12.079

(15) Atzeri, A. M., Cappelletti, F., Tzempelikos, A., and Andrea, G. (2016). Comfort Metrics for an Integrated Evaluation of Buildings Performance. Energy and Buildings, 127: 411-424. https://doi.org/10.1016/j.enbuild.2016.06.007

(16) Lützkendorf, T., Speer, T., Szigeti, F., Davis, G., Le Roux, P., Kato, A., and Tsunekawa, K. (2005). A comparison of international classifications for performance requirements and building performance categories used in evaluation methods. Performance based building, 61-80.

(17) Szigeti, F., Davis, G., Dempsey, J. J., Hammond, D., Davis, D., Colombard-Prout, M., and Catarina, O. (2004). Defining performance requirements to assess the suitability of constructed assets in support of the mission of the organization. In Proceedings of the CIB World Congress, Toronto, Canadá, 1-13.

(18) Prill, R., Kunkle, R., and Novosel, D. (2009). Final Report Ncembt-090417 Development of an Operation and Maintenance Rating System for Commercial. National Center for Energy Management and Building Technologies.

(19) Krueger, R., & Casey, M. (2009). Focus groups: A practical guide for applied research. Sage publications.

(20) Pärn, E.A., Edwards, D.J., and Sing, M.C.P. (2017). The building information modelling trajectory in facilities management: A review. Automation in Construction, 75: 45-55. https://doi.org/10.1016/j.autcon.2016.12.003

(21) Leaman, A., and Bordass, B. (2007). Are users more tolerant of 'green' buildings?. Building Research & Information, 35(6): 662-673. https://doi.org/10.1080/09613210701529518

(22) Zhang, L., Wu, X., Skibniewski, M. J., Zhong, J., and Lu, Y. (2014). Bayesian-network-based safety risk analysis in construction projects. Reliability Engineering and System Safety, 131: 29-39. https://doi.org/10.1016/j.ress.2014.06.006

(23) Wagner, A., Gossauer, E., Moosmann, C., Gropp, T., and Leonhart, R. (2007). Thermal comfort and workplace occupant satisfaction-Results of field studies in German low energy office buildings. Energy and Buildings, 39(7), 758-769. https://doi.org/10.1016/j.enbuild.2007.02.013

(24) Flores-Colen, I., and de Brito, J. (2010). A systematic approach for maintenance budgeting of buildings façades based on predictive and preventive strategies. Construction and Building Materials, 24(9): 1718-1729. https://doi.org/10.1016/j.conbuildmat.2010.02.017

(25) BREEAM. (2016). BRE Environmental Assessment Method, Building Research Establishment UK. Retreived from http://www.breeam.org/

(26) Fellows, R. F., and Liu, A. M. (2015). Research methods for construction. John Wiley & Sons.

(27) CEN (European Committee for Standardization). (2011). EN 15221-3: European Standard in Facility Management-Part 3: Guidance on Quality in Facility Management. Brussel: CEN (European Committee for Standardization).

(28) Sullivan, G.P., Pugh, R., Melendez, A.P., and Hunt, W. D. (2010). Operations & Maintenance Best Practices. U.S. Departament of Energy, Federal energy management program, (August), 321.

(29) Yan, D., O'Brien, W., Hong, T., Feng, X., Burak Gunay, H., Tahmasebi, F., and Mahdavi, A. (2015). Occupant behavior modeling for building performance simulation: Current state and future challenges. Energy and Buildings, 107: 264-278. https://doi.org/10.1016/j.enbuild.2015.08.032

(30) ALwaer, H., and Clements-Croome, D. J. (2010). Key performance indicators (KPIs) and priority setting in using the multi-attribute approach for assessing sustainable intelligent buildings. Building and Environment, 45(4): 799-807. https://doi.org/10.1016/j.buildenv.2009.08.019

(31) Bortolini, R., and Forcada, N. (2017). Discussion About the Use of Bayesian Networks Models for Making Predictive Maintenance Decisions. In Lean and Computing in Construction Congress - Volume 1: Proceedings of the Joint Conference on Computing in Construction, (973-980.). Edinburgh: Heriot-Watt University. https://doi.org/10.24928/JC3-2017/0145

(32) Abdul-Lateef, O. A. (2010). Quantitative Analysis of Criteria in University Building Maintenance in Malaysia. Australasian Journal of Construction Economics and Building, 10: 51-61. https://doi.org/10.5130/AJCEB.v10i3.1681

(33) Preiser, W., and Nasar, J. (2008). Assessing building performance: Its evolution from post-occupancy evaluation. International Journal of Architectural Research, 2(1): 84-99.

(34) OmniClass. (2012). OmniClass: A strategy for classifying the built environment. Retreived from http://www.omniclass.org (Jan 24, 2018).

(35) Gaspar, P.L., and Brito, J. de. (2008). Quantifying environmental effects on cement-rendered facades: A comparison between different degradation indicators. Building and Environment, 43(11): 1818-1828. https://doi.org/10.1016/j.buildenv.2007.10.022

(36) Roulet, C.-A., Johner, N., Foradini, F., Bluyssen, P., Cox, C., De Oliveira Fernandes, E., Müller, B., and Aizlewood, C. (2006). Perceived health and comfort in relation to energy use and building characteristics. Building Research & Information, 34(5): 467-474. https://doi.org/10.1080/09613210600822279

(37) Goins, J., and Moezzi, M. (2013). Linking occupant complaints to building performance. Building Research & Information, 41(3): 361-372. https://doi.org/10.1080/09613218.2013.763714

(38) Lavy, S., Garcia, J., and Dixit, M. (2014). KPIs for facility's performance assessment, Part II: identification of variables and deriving expressions for core indicators. Facilities, 32(5/6): 275-294 https://doi.org/10.1108/F-09-2012-0067

(39) Grussing, M.N. & Liu, L.Y. (2014). Knowledge-Based Optimization of Building Maintenance, Repair, and Renovation Activities to Improve Facility Life Cycle Investments. Journal of Performance of Constructed Facilities, 28(3): 539-548. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000449

(40) Fox, M., Goodhew, S. & De Wilde, P. (2016). Building defect detection: External versus internal thermography. Building and Environment, 105: 317-331. https://doi.org/10.1016/j.buildenv.2016.06.011

(41) Abisuga, A.O., Famakin, I.O. & Oshodi, O.S. (2016). Educational building conditions and the health of users. Construction Economics and Building, 16(4): 19-34. https://doi.org/10.5130/AJCEB.v16i4.4979

(42) Pearl, J. (1985). Bayesian Networks A Model of Self-Activated Memory for Evidential Reasoning. In Proceedings of the 7th Conference of the Cognitive Science Society. Irvine, California.




Copyright (c) 2020 Consejo Superior de Investigaciones Científicas (CSIC)

Licencia de Creative Commons
Esta obra está bajo una licencia de Creative Commons Reconocimiento 4.0 Internacional.


Contacte con la revista informes@ietcc.csic.es

Soporte técnico soporte.tecnico.revistas@csic.es