Durability of current renderings: A probabilistic analysis

Abstract The durability of constructions is essential to the quality of life in urban spaces. Recently there has been a growing concern of the construction stakeholders with the durability of the materials used and construction sustainability. In this study, using a multinomial logistic regression technique, a probabilistic analysis of the degradation condition of rendered facades, as a function of age and type of mortar, is performed. The probability of the rendered facades reaching the end of their service life, i.e. the moment after which they no longer comply with the minimum performance requirements, is also evaluated. To that purpose, a sample of 100 case studies located in Lisbon, whose degradation state is determined by in situ visual inspections, is used.

[1]  O Ohlsson,et al.  Eating difficulties, need for assisted eating, nutritional status and pressure ulcers in patients admitted for stroke rehabilitation. , 2001, Journal of clinical nursing.

[2]  Ghang Lee,et al.  Decision tree for selecting retaining wall systems based on logistic regression analysis , 2010 .

[3]  Ian Witten,et al.  Data Mining , 2000 .

[4]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[5]  Jun-Mei Li,et al.  A model based on factor analysis and Support Vector Machine for Credit Risk Identification in small-and-medium enterprises , 2009, 2009 International Conference on Machine Learning and Cybernetics.

[6]  Jorge de Brito,et al.  Mapping defect sensitivity in external mortar renders , 2005 .

[7]  Jorge De Brito,et al.  Service life estimation of cement-rendered facades , 2008 .

[8]  Yun Wang,et al.  A multinomial logistic regression modeling approach for anomaly intrusion detection , 2005, Comput. Secur..

[9]  A. Silva,et al.  Statistical models applied to service life prediction of rendered façades , 2013 .

[10]  Joel C. Trexler,et al.  Nontraditional Regression Analyses , 1993 .

[11]  I. J. Myung,et al.  Tutorial on maximum likelihood estimation , 2003 .

[12]  H. Kus,et al.  Microstructural investigations of naturally and artificially weathered autoclaved aerated concrete , 2003 .

[13]  J. I. Alvarez,et al.  Ageing of Lime Mortars with Admixtures: Durability and Strength Assessment , 2010 .

[14]  A. M. Aikivuori.  CRITICAL LOSS OF PERFORMANCE - WHAT FAILS BEFORE DURABILITY , 1999 .

[15]  Hülya Kus,et al.  In-use performance assessment of rendered autoclaved aerated concrete walls by long-term moisture monitoring , 2004 .

[16]  Jorge de Brito,et al.  Expected render performance assessment based on impact resistance in situ determination , 2009 .

[17]  B. Rudolf,et al.  World Map of the Köppen-Geiger climate classification updated , 2006 .

[18]  Daniel Hallberg,et al.  System for Predictive Life Cycle Management of Buildings and Infrastructures , 2009 .

[19]  T. McMahon,et al.  Updated world map of the Köppen-Geiger climate classification , 2007 .

[20]  Björn Marteinsson,et al.  Service Life Estimations in the Design of Buildings : A Development of the Factor Method , 2005 .

[21]  Luc Courard,et al.  Durability of mortars modified with metakaolin , 2003 .

[22]  Hua Yun Chen.  A semiparametric odds ratio model for measuring association. , 2007 .

[23]  P. L. Gaspar,et al.  Limit States and Service Life of Cement Renders on Façades , 2011 .

[24]  J. de Brito,et al.  Probabilistic Analysis of the Degradation Evolution of Stone Wall Cladding Directly Adhered to the Substrate , 2013 .

[25]  Adrian Long,et al.  Why assess the properties of near-surface concrete? , 2001 .

[26]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[27]  Karuthan Chinna,et al.  Statistical analysis using SPSS , 2012 .