Optimizing Service Life Prediction Models of External Paint Finishes
Publication: Journal of Performance of Constructed Facilities
Volume 34, Issue 2
Abstract
In the present research effort, the topic of service life prediction is revisited, using external paint finishes on rendered facades as a case study. A statistically robust approach to develop ordinary linear regression models is proposed, covering the identification of the explanatory variables to include in the model and the identification of outliers. The application of this approach to the analyzed case study improved the determination coefficient () up to 0.84, from the 0.74 obtained in the original research in which the data set was presented. In addition to the increase in accuracy, the novel approach proposed enabled the identification of four factors having a statistically significant influence on the degradation rate of the paint finishes: (1) the urbanization density; (2) the humidity level; (3) the type of surface finishing; and (4) the facade orientation. Finally, to enable the development of a hybrid model, a novel approach is suggested whereby the model uncertainty is used to combine statistical and expert inputs. The hybrid modeling approach was proposed to allow tuning the model performance in situations in which experts can identify specific characteristics or conditions affecting the service life not taken into account by the statistical model.
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Acknowledgments
The authors would like to thank the reviewers for their contribution to improving the manuscript.
References
Barnett, V., and T. Lewis. 1994. Outliers in statistical data. 3rd ed. Hoboken, NJ: Wiley.
BPIE (Buildings Performance Institute Europe). 2011. Europe’s building under the microscope: A country-by-country review of the energy performance of buildings. Brussels, Belgium: BPIE.
Budescu, D. V. 1993. “Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression.” Psychol. Bull. 114 (3): 542–551. https://doi.org/10.1037/0033-2909.114.3.542.
CEN (European Committee for Standardization). 2006. Set of standards on facility management (7 standards). EN 15221-1/7. Brussels, Belgium: CEN.
Chai, C., J. de Brito, P. Gaspar, and A. Silva. 2015. “Statistical modelling of the service life prediction of painted surfaces.” Int. J. Strategic Property Manage. 19 (2): 173–185. https://doi.org/10.3846/1648715X.2015.1031853.
Chai, C., J. de Brito, P. L. Gaspar, and A. Silva. 2014. “Predicting the service life of exterior wall painting: Techno-economic analysis of alternative maintenance strategies” J. Constr. Eng. Manage. 140 (3): 04013057. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000812.
Cook, R. D. 1977. “Detection of influential observations in linear regression.” Technometrics 19 (1): 15–18. https://doi.org/10.1080/00401706.1977.10489493.
Costa, J., P. V. Paulo, F. A. Branco, and J. de Brito. 2014. “Modeling evolution of stains in old building facades caused by collection of dirt.” J. Perform. Constr. Facil. 28 (2): 264–271. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000417.
Darlington, R. B. 1968. “Multiple regression in psychological research and practice.” Psychol. Bull. 69 (3): 161–182. https://doi.org/10.1037/h0025471.
Dias, J. L., A. Silva, C. Chai, P. L. Gaspar, and J. de Brito. 2014. “Neural networks applied to service life prediction of exterior painted surfaces.” Build. Res. Inf. 42 (3): 371–380. https://doi.org/10.1080/09613218.2013.819551.
Donkor, E. A., T. A. Mazzuchi, R. Soyer, and J. A. Roberson. 2014. “Urban water demand forecasting: Review of methods and models.” J. Water Resour. Plann. Manage. 140 (2): 146–159. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000314.
Draper, N. R., and J. A. John. 1981. “Influential observations and outliers in regression.” Technometrics 23 (1): 21–26. https://doi.org/10.1080/00401706.1981.10486232.
EP (European Parliament). 2016. Boosting building renovation: What potential and value for Europe? Brussels, Belgium: EP.
Escriva-Bou, A., J. R. Lund, and M. Pulido-Velazquez. 2018. “Saving energy from urban water demand management.” Water Resour. Res. 54 (7): 4265–4276. https://doi.org/10.1029/2017WR021448.
Eurostat. 2015. People in the EU: Who are we and how do we live? Luxembourg, UK: Eurostat, Publications Office of the European Union.
Galbusera, M. M., J. de Brito, and A. Silva. 2014. “The importance of the quality of sampling in service life prediction.” Constr. Build. Mater. 66 (Sep): 19–29. https://doi.org/10.1016/j.conbuildmat.2014.05.045.
Garrido, M. A., P. V. Paulo, and F. A. Branco. 2012. “Service life prediction of facade paint coatings in old buildings.” Constr. Build. Mater. 29 (Apr): 394–402. https://doi.org/10.1016/j.conbuildmat.2011.10.057.
Gupta, A., P. Kumar, and R. Rastogi. 2014. “Critical review of flexible pavement performance models.” KSCE J. Civ. Eng. 18 (1): 142–148. https://doi.org/10.1007/s12205-014-0255-2.
Huang, Y., H. Shao, E. Wieland, O. Kolditz, and G. Kosakowski. 2018. “A new approach to coupled two-phase reactive transport simulation for long-term degradation of concrete.” Constr. Build. Mater. 190 (Nov): 805–829. https://doi.org/10.1016/j.conbuildmat.2018.09.114.
ISO. 2008. Set of standards on buildings and constructed assets—Service life planning. ISO 15686-1/11. Geneva: ISO.
ISO. 2014. Set of standards on asset management 3 standards. ISO 55000/55002. Geneva: ISO.
ISO. 2018. Facility management—Management systems—Requirements with guidance for use. ISO 41001. Geneva: ISO.
Johnson, J. W. 2000. “A heuristic method for estimating the relative weight of predictor variables in multiple regression.” Multivariate Behav. Res. 35 (1): 1–19. https://doi.org/10.1207/S15327906MBR3501_1.
Lacasse, M. A., and C. Sjöström. 2004. “Recent advances in methods for service life prediction of building materials and components—An overview.” In Proc., CIB (Int. Council for Building) World Building Congress. Ottawa: International Building Council.
Lacasse, M. A., D. J. Vanier, and B. R. Kyle. 1997. “Towards integration of service life and asset management tools for building envelope systems.” In Proc., 7th Conf. on Building Science and Technology: Durability of Buildings Design, Maintenance, Codes and Practice, 153–167. Ottawa: National Research Council Canada.
Loucks, D. P., and E. van Beek. 2005. Water resources systems planning and management: An introduction to methods, models and applications. Paris: United Nations Educational, Scientific and Cultural Organization.
Lua, T., X. Lü, and C. Kibert. 2015. “A hybrid numerical-neural-network model for building simulation: A case study for the simulation of unheated and uncooled indoor temperature.” Energy Build. 86 (Jan): 723–734. https://doi.org/10.1016/j.enbuild.2014.10.024.
Magos, M., J. de Brito, P. L. Gaspar, and S. Silva. 2016. “Application of the factor method to the prediction of the service life of external paint finishes on facades.” Mater. Struct. 49 (12): 5209–5225. https://doi.org/10.1617/s11527-016-0855-z.
Mahalanobis, P. C. 1936. “On the generalised distance in statistics.” Proc. Natl. Inst. Sci. India 2 (1): 49–55.
Mao, N., D. Pan, M. Song, Z. Li, Y. Xu, and S. Deng. 2017. “Operating optimization for improved energy consumption of a TAC system affected by nighttime thermal loads of building envelopes.” Energy 133 (Aug): 491–501. https://doi.org/10.1016/j.energy.2017.04.106.
Marteinsson, B. 2005. “Service life estimation in the design of buildings: A development of the factor method.” Ph.D. thesis, KTH Research School, Centre for Built Environment, Dept. of Technology and Built Environment, Univ. of Gävle.
Martin, J. W., S. C. Saunders, F. L. Floyd, and J. P. Wineburg. 1996. Methodologies for predicting the service lives of coating systems. Blue Bell, PA: Federation of Societies for Coatings Technology.
Masters, L. W., and E. Brandt. 1987. “Prediction of service life of building materials and components.” Mater. Struct. 20 (1): 55–77. https://doi.org/10.1007/BF02472728.
Masters, L. W., and E. Brandt. 1989. “Systematic methodology for service life prediction of building materials and components.” Mater. Struct. 22 (5): 385–392. https://doi.org/10.1007/BF02472509.
O’Brien, R. M. 2007. “A caution regarding rules of thumb for variance inflation factors.” Qual. Quantity 41 (5): 673–690. https://doi.org/10.1007/s11135-006-9018-6.
Paulo, P. V. 2009. “A building management system (BuildingsLife): Application of deterministic and stochastic models with genetic algorithms to building facades.” [In Portuguese.] Ph.D. thesis, Civil Engineering at Instituto Superior Técnico, Technical Univ. of Lisbon.
RJUE (Regime Jurídico da Ubanização e Edificação). 2015. Decreto-Lei n.° 555/99, de 16 de Dezembro, Portugal. [In Portuguese.] Lisbon, Portugal: Parliament.
Sharma, P. N., G. Shmueli, M. Sarstedt, and K. H. Kim. 2015. “Predictive model selection in partial least squares path modeling.” In Proc., 2nd Int. Symp. on Partial Least Squares Path Modeling. Enschede: Universiteit Twente.
Silva, A., J. de Brito, and P. Gaspar. 2016a. Methodologies for service life prediction of buildings: With a focus on facade claddings. Basel, Switzerland: Springer.
Silva, A., J. L. Dias, P. L. Gaspar, and J. de Brito. 2013. “Statistical models applied to service life prediction of rendered facades.” Autom. Constr. 30 (Mar): 151–160. https://doi.org/10.1016/j.autcon.2012.11.028.
Silva, A., P. L. Gaspar, and J. de Brito. 2014. “Durability of current renderings: A probabilistic analysis.” Autom. Constr. 44 (Aug): 92–102. https://doi.org/10.1016/j.autcon.2014.04.002.
Silva, A., P. L. Gaspar, and J. de Brito. 2016b. “Comparative analysis of service life prediction methods applied to rendered facades.” Mater. Struct. 49 (11): 4893–4910. https://doi.org/10.1617/s11527-016-0832-6.
Silva, A., L. C. Neves, P. L. Gaspar, and J. de Brito. 2016c. “Probabilistic transition of condition: Render facades.” Build. Res. Inf. 44 (3): 301–318. https://doi.org/10.1080/09613218.2015.1023645.
Sousa, V., N. Almeida, I. Meireles, and J. Brito. 2011. “Anomalies in wall renders: Overview of the main causes of degradation.” Int. J. Archit. Heritage 5 (2): 198–218. https://doi.org/10.1080/15583050903487633.
Sousa, V., C. O. Cruz, I. Meireles, and P. Moreira. 2017. “Statistical optimization of degradation curves: Application to painted rendered façades.” In Proc., International Research Conf. Salford, UK: Chartered Institute of Building.
Sousa, V., J. P. Matos, and N. Matias. 2014. “Evaluation of artificial intelligence tool performance and uncertainty for predicting sewer structural condition.” Autom. Constr. 44 (Aug): 84–91. https://doi.org/10.1016/j.autcon.2014.04.004.
Sousa, V., T. D. Pereira, and I. Meireles. 2016. “Modelling the degradation rate of the wood frame doors and windows of the National Palace of Sintra, Portugal.” J. Perform. Constr. Facil. 30 (2): 04015010. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000747.
Suganthia, L., and A. A. Samuel. 2012. “Energy models for demand forecasting—A review.” Renewable Sustainable Energy Rev. 16 (2): 1223–1240. https://doi.org/10.1016/j.rser.2011.08.014.
Tonidandel, S., and J. M. LeBreton. 2011. “Relative importance analysis: A useful supplement to regression analysis.” J. Bus. Psychol. 26 (1): 1–9. https://doi.org/10.1007/s10869-010-9204-3.
Vieira, S. M., A. Silva, J. M. C. Sousa, J. de Brito, and P. L. Gaspar. 2015. “Modelling the service life of rendered facades using fuzzy systems.” Autom. Constr. 51 (Mar): 1–7. https://doi.org/10.1016/j.autcon.2014.12.011.
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Published In
Journal of Performance of Constructed Facilities
Volume 34 • Issue 2 • April 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Mar 6, 2019
Accepted: Aug 20, 2019
Published online: Feb 11, 2020
Published in print: Apr 1, 2020
Discussion open until: Jul 11, 2020
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