Modeling the Influence of Environmental Factors on Concrete Evaporation Rate

Document Type : Regular Article


1 Ph.D. Candidate, Department of Civil Engineering, University of Patras, Patras, Greece

2 Ph.D., Department of Civil Engineering, University of Patras, Patras, Greece

3 Associate Professor, Department of Civil Engineering, University of Patras, Patras, Greece


Newly poured concrete opposing hot and windy conditions is considerably susceptible to plastic shrinkage cracking. Crack-free concrete structures are essential in ensuring high level of durability and functionality as cracks allow harmful instances or water to penetrate in the concrete resulting in structural damages, e.g. reinforcement corrosion or pressure application on the crack sides due to water freezing effect. Among other factors influencing plastic shrinkage, an important one is the concrete surface humidity evaporation rate. The evaporation rate is currently calculated in practice by using a quite complex Nomograph, a process rather tedious, time consuming and prone to inaccuracies. In response to such limitations, three analytical models for estimating the evaporation rate are developed and evaluated in this paper on the basis of the ACI 305R-10 Nomograph for “Hot Weather Concreting”. In this direction, several methods and techniques are employed including curve fitting via Genetic Algorithm optimization and Artificial Neural Networks techniques. The models are developed and tested upon datasets from two different countries and compared to the results of a previous similar study. The outcomes of this study indicate that such models can effectively re-develop the Nomograph output and estimate the concrete evaporation rate with high accuracy compared to typical curve-fitting statistical models or models from the literature. Among the proposed methods, the optimization via Genetic Algorithms, individually applied at each estimation process step, provides the best fitting result.


Google Scholar


Main Subjects

[1]       Sivakumar A, Santhanam M. A quantitative study on the plastic shrinkage cracking in high strength hybrid fibre reinforced concrete. Cem Concr Compos 2007;29:575–81. doi:10.1016/j.cemconcomp.2007.03.005.
[2]       Wittmann FH. On the action of capillary pressure in fresh concrete. Cem Concr Res 1976;6:49–56. doi:10.1016/0008-8846(76)90050-8.
[3]       El-Reedy M. Onshore Structural Design Calculations: 9.2.1 Plastic Shrinkage Cracking.Elsevier Ltd. 2017:387–430.
[4] %20ctif.pdf, last assessed: 27.02.2020. n.d.
[5]       ACI Committee 305. Hot-Weather Concreting. ACI 305R-10, American Concrete Institute 2010.
[6]       Uno PJ. Plastic shrinkage cracking and evaporation formulas. ACI Mater J 1998;95:365–75.
[7]       Sayahi F, Emborg M, Hedlund H. Plastic shrinkage cracking in concrete: State of the art. Nord Concr Res 2014;51:95–110.
[8]       Grzybowski M, Shah SP. Shrinkage cracking of fiber reinforced concrete. Mater J 1990;87:138–48.
[9]       Mouret M, Bascoul A, Escadeillas G. Strength impairment of concrete mixed in hot weather: relation to porosity of bulk fresh concrete paste and maturity. Mag Concr Res 2003;55:215–23. doi:10.1680/macr.2003.55.3.215.
[10]     Zhang J, Hou D, Han Y. Micromechanical modeling on autogenous and drying shrinkages of concrete. Constr Build Mater 2012;29:230–40. doi:10.1016/j.conbuildmat.2011.09.022.
[11]     Al-Fadhala M, Hover KC. Rapid evaporation from freshly cast concrete and the Gulf environment. Constr Build Mater 2001;15:1–7. doi:10.1016/S0950-0618(00)00064-7.
[12]     Schmidt M, Slowik V. Instrumentation for optimizing concrete curing. Concr Int 2013;35:60–4.
[13]     Boshoff WP, Combrinck R. Modelling the severity of plastic shrinkage cracking in concrete. Cem Concr Res 2013;48:34–9. doi:10.1016/j.cemconres.2013.02.003.
[14]     Douglas J, Danciu L. Nomogram to help explain probabilistic seismic hazard. J Seismol 2020;24:221–8. doi:10.1007/s10950-019-09885-4.
[15]     ACI Committee 207.2R-07. Report on Thermal and Volume Change Effects on Cracking of Mass Concrete, American Concrete Institute, 2007 n.d.
[16]     Why Stata,, last assessed: 01.08.2020 n.d.
[17]     Economou P, Batsidis A, Kounetas K. Evaluation of the OECD’s prediction algorithm for the annual GDP growth rate. Commun Stat Case Stud Data Anal Appl 2020:1–21. doi:10.1080/23737484.2020.1805818.
[18]     Sharifi Y, Hosainpoor M. A Predictive Model Based ANN for Compressive Strength Assessment of the Mortars Containing Metakaolin. J Soft Comput Civ Eng 2020;4:1–12.
[19]     Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators. Neural Networks 1989;2:359–66. doi:10.1016/0893-6080(89)90020-8.
[20]     Esmailzadeh A, Kamali A, Shahriar K, Mikaeil R. Connectivity and Flowrate Estimation of Discrete Fracture Network Using Artificial Neural Network. J Soft Comput Civ Eng 2018;2:13–26.
[21]     Luo X, Patton AD, Singh C. Real power transfer capability calculations using multi-layer feed-forward neural networks. IEEE Trans Power Syst 2000;15:903–8. doi:10.1109/59.867192.
[22]     Fractional Polynomial Regression, Chapter 382,, last assessed: 01.08.2020. n.d.
[23]     Green SB. How Many Subjects Does It Take To Do A Regression Analysis. Multivariate Behav Res 1991;26:499–510. doi:10.1207/s15327906mbr2603_7.