Due to the cement hydration heat, concrete deforms during curing. These deformations may lead to cracks in the concrete. Therefore, a method which estimates the strain during curing is very valuable. In this research, two methods of multivariable regression and neural network were studied with the aim of estimating strain changes in concrete. For this purpose, laboratory cylindrical specimens were prepared under controlled situation at first and then vibration wire strain gauges equipped with thermistors were placed inside each sample to measure the deformations. Two different groups of input data were used in which variables included time, environment temperature, concrete temperature, water-to-cement ratio, aggregate content, height, and specimen diameter. CEM I, 42.5？R was utilized in set (I) and strain changes have been measured in six concrete specimens. In set (II) CEM II, 52.5？R was employed and strain changes were measured in three different specimens in which the diameter was held constant. The best multivariate regression equations calculated the determined coefficients at 0.804 and 0.82 for sets (I) and (II), whereas the artificial neural networks predicted the strain with higher of 1 and 0.996. Results show that the neural network method can be utilized as an efficient tool for estimating concrete strain during curing. 1. Introduction Due to increasing use of high-performance concrete, early-age concrete behavior is a problem of great concern. Concrete at early ages experiences thermal deformations due to the heat generation caused by the cement hydration reactions. These deformations may lead to cracking of concrete. So, one of the most important parameters in studying concrete behavior is deformation or strain [1]. Considering that the major difficulty with early-age concrete monitoring relates to measurement of strains [2, 3]. Diagnosing the strain during hydration is one of the fundamental problems [1]. Also, measuring concrete strain during curing requires precise instruments and high costs. A practical and accurate measurement of this parameter is faced with major problems [1]. Therefore, utilizing a method that estimates strain during curing is very beneficial. ASTM C827 is the only standardized test to measure early-age shrinkage of concrete. This test method is limited as only vertical movement can be measured [4]. Jensen and Hansen have devised a method for measuring linear autogenous shrinkage of cement paste [5]. This method uses sealed soft plastic tubes that are partially fixed to a rigid frame. If autogenous shrinkage
References
[1]
M. Azenha, R. Faria, and D. Ferreira, “Identification of early-age concrete temperatures and strains: monitoring and numerical simulation,” Cement and Concrete Composites, vol. 31, no. 6, pp. 369–378, 2009.
[2]
M. Azenha, Behaviour of concrete at early ages. Phenomenology and thermo- mechanical analysis [M.S. thesis], Faculty of Engineering of the University of Porto, Porto, Portugal, 2004.
[3]
R. Faria, M. Azenha, and J. A. Figueiras, “Modelling of concrete at early ages: application to an externally restrained slab,” Cement and Concrete Composites, vol. 28, no. 6, pp. 572–585, 2006.
[4]
E. E. Holt, “Early age autogenous shrinkage of concrete. Espoo 2001. Technical Research. Centre of Finland,” VTT Publications, no. 446, pp. 2–184, 2001.
[5]
O. M. Jensen and P. F. Hansen, “A dilatometer for measuring autogenous deformation in hardening portland cement paste,” Materials and Structures, vol. 28, no. 7, pp. 406–409, 1995.
[6]
H. Justnes, A. van Gemert, F. Verboven, and E. J. Sellevold, “Total and external chemical shrinkage of low w/c ratio cement pastes,” Advances in Cement Research, vol. 8, no. 31, pp. 121–126, 1996.
[7]
T. A. Hammer, “Test methods for linear measurement of autogenous shrinkage before setting,” in Autogenous Shrinkage of Concrete, Ei-ichi Tazawa, Ed., pp. 143–154, E & FN Spon, London, UK, 1999.
H. Weigler and S. Karl, Junger Beton, Beanspruchung-Festigkeit-Verformung, vol. 40, Betonwerk Fertigteil-Technik, Munich, Germany, 1974.
[10]
D. W. Mokarem, R. M. Meyerson, and R. E. Weyers, “Development of concrete shrinkage performance specifications,” Final Contract Report 04-CR1, Virginia Transportation Research Council (VTRC), Charlottesville, Va, USA, 2003.
[11]
B. Bissonnette, P. Pierre, and M. Pigeon, “Influence of key parameters on drying shrinkage of cementitious materials,” Cement and Concrete Research, vol. 29, no. 10, pp. 1655–1662, 1999.
[12]
Z. P. Bazant and W. P. Murphy, “Creep and shrinkage prediction model for analysis and design of concrete structures-model B3,” Materiaux et Constructions, vol. 28, no. 180, pp. 357–365, 1995.
[13]
ACI Committee 209, Prediction of Creep, Shrinkage and Temperature Effects in Concrete Structures, vol. SP-76, American Concrete Institute, Farmington Hills, Mich, USA, 1982.
[14]
CEB-FIP, International Recommendations for the Design and Construction of Concrete Structures, Comite Europeen du beton-Federation International de la Precontrainte, Cement and Concrete Association, London, UK, 1970.
[15]
CEB-FIP, Model Code for Concrete Structures (International System of Unified Standard Codes of Practice For Structures), Comite Euro-International du Beton, Lausanne, Switzerland, 1978.
[16]
CEB-FIP, Model Code, Comite Euro-International du Beton, Lausanne, Switzerland; Thomas Telford Services, London, UK, 1990.
[17]
N. J. Gardner and M. J. Lockman, “Design provisions for drying shrinkage and creep of normal-strength concrete,” ACI Materials Journal, vol. 98, no. 2, pp. 159–167, 2001.
[18]
K. Sakata, “Prediction of concrete creep and shrinkage. Creep and shrinkage of concrete,” in Proceedings of the 5th International RILEM Symposium, 1993.
[19]
K. Eguchi and K. Teranishi, “Prediction equation of drying shrinkage of concrete based on composite model,” Cement and Concrete Research, vol. 35, no. 3, pp. 483–493, 2005.
[20]
Z. M. Sbartai, S. Laurens, S. M. Elachachi, and C. Payan, “Concrete properties evaluation by statistical fusion of NDT techniques,” Construction and Building Materials, vol. 37, pp. 943–950, 2012.
[21]
W. Hansen, “Constitutive model for predicting ultimate drying shrinkage of concrete,” Journal of the American Ceramic Society, vol. 70, no. 5, pp. 329–332, 1987.
[22]
C. T. Leondes, Neural Network Systems Techniques and Applications: Algorithms and Architectures, Academic Press, New York, NY, USA, 1998.
[23]
R. P. Lippmann, “An introduction to computing with neural nets,” IEEE ASSP Magazine, vol. 4, no. 2, pp. 4–22, 1987.
[24]
D. Sarkar, “Methods to speed up error back-propagation learning algorithm,” ACM Computing Surveys, vol. 27, no. 4, pp. 519–542, 1995.
[25]
F. Ozcan, C. D. Atis, O. Karahan, E. Uncuoglu, and H. Tanyildizi, “Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete,” Advances in Engineering Software, vol. 40, no. 9, pp. 856–863, 2009.
[26]
H. Demuth and M. Beale, Neural Network Toolbox for Use with MATLAB, Handbook, 2002.
[27]
Z. Dahou, Z. M. Sbarta？, A. Castel, and F. Ghomari, “Artificial neural network model for steel-concrete bond prediction,” Engineering Structures, vol. 31, no. 8, pp. 1724–1733, 2009.
