The Reliability of the ACI 209R-92 Method in Predicting Column Shortening in High-Rise Concrete Buildings

  • Alaa Habrah
  • Abid I. Abu-Tair
Conference paper
Part of the Lecture Notes in Civil Engineering book series (LNCE, volume 7)


The early prediction of column shortening during the design of high-rise concrete buildings is of high importance to allow for shortening compensation measures to be proposed and taken in the construction. However, a column shortening that consists of elastic, creep and shrinkage shortening can be very complex to predict influenced by many uncertainties, such as inelasticity and non-homogeneity of the concrete structures and highly affected by the chosen method for time-dependent effects. In this paper, the ACI 209R-92 method (ACI Committee 209 in Prediction of creep, shrinkage, and temperature effects in concrete structures (ACI 209R-92). American Concrete Institute, Farmington Hills [1]) for predicting creep and shrinkage effects is evaluated by comparing its results to actual site readings taken during the construction of a high-rise building presented as a case study in this research. For that purpose, an Excel (Microsoft Corporation in Excel (computer program),, [2]) spreadsheet including all the factors of this method is developed to provide a simple interface to predict the elastic and time-dependent column shortening and settlement. Furthermore, the presented case study building was analyzed using a finite element software, Etabs (Computers and Structures in Etabs 2015 ultimate, version 15.0.0 (computer program),, [3]) where column shortening and settlement were predicted based on the CEB-FIB 90 method (Comite Euro-International Du Beton in CEB-FIB model code, Thomas Telford, London, [4]) for time-dependent effects. By comparing the two methods’ results to the actual readings, it was found that both methods overestimated column settlement in all floors. Whilst the average overestimation of the developed Excel sheet based on the ACI 209R-92 model was 630%, Etabs analysis based on the CEB-FIB 90 model had more accurate results with average overestimation of 258%.


Column shortening Creep Shrinkage Modulus of elasticity 


  1. 1.
    ACI Committee 209 (1992) Prediction of creep, shrinkage, and temperature effects in concrete structures (ACI 209R-92). American Concrete Institute, Farmington HillsGoogle Scholar
  2. 2.
    Microsoft Corporation (2013) Excel (computer program).
  3. 3.
    Computers and Structures (2015) Etabs 2015 ultimate, version 15.0.0 (computer program).
  4. 4.
    Comite Euro-International Du Beton (1993) CEB-FIB model code. Thomas Telford, LondonGoogle Scholar
  5. 5.
    Glanville W, Thomas F (1933) Creep of concrete under load. Struct Eng 11(2):54–68Google Scholar
  6. 6.
    Fintel M, Ghosh S, Iyengar H (1987) Column shortening in tall structures-prediction and compensation. Portland Cement Association, SkokieGoogle Scholar
  7. 7.
    Swamy R, Arumugasaamy P (1978) Structural behaviour of reinforced concrete columns in service. Struct Eng 56(11):319–329Google Scholar
  8. 8.
    Fintel M, Khan F (1969) Effects of column creep and shrinkage in tall structures-prediction of inelastic column shortening. ACI J Proc 66(12):957–967Google Scholar
  9. 9.
    Ghosh S (1996) Estimation and accommodation of column length changes in tall buildings. In: Rangan B, Warner R (eds) Large concrete buildings. Longman, HarlowGoogle Scholar
  10. 10.
    Jayasinghe M, Jayasena W (2005) Effect of relative humidity on absolute and differential shortening of columns and walls in multistory reinforced concrete buildings. Pract Period Struct Des Constr 10(2):88–97CrossRefGoogle Scholar
  11. 11.
    Hamed E, Lai C (2016) Geometrically and materially nonlinear creep behaviour of reinforced concrete columns. Structures 5:1–12CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Development, Research and Urban Planning ConsultancyFujairahUAE
  2. 2.The British University in DubaiDubaiUAE

Personalised recommendations