Prediction of Compressive Strength of Concrete by Data-Driven Models

Faezehossadat Khademi*, Mahmoud Akbari**, Sayed Mohammadmehdi Jamal***
* Graduate Student, Civil, Architectural, and Environmental Engineering Department, Illinois Institute of Technology, Chicago, USA.
** Assistant Professor, Civil Engineering Department, University of Kashan, Kashan, Iran.
*** Graduate Student, Civil Engineering Department, University of Hormozgan, Hormozgan, Iran.
Periodicity:March - May'2015
DOI : https://doi.org/10.26634/jce.5.2.3350

Abstract

The aim of this study is prediction of 28-day compressive strength of concrete by data-driven models. Hence, by considering concrete constituents as input variables, two data-driven models namely Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models are constructed for the purpose of predicting the 28-days compressive strength of different concrete mix designs. Comparing the two models illustrates that MLR model is not a suitable model for predicting the compressive strength; however, ANN can be used to efficiently predict the compressive strength of concrete.

Keywords

Concrete, Compressive Strength, Multiple Linear Regression model (MLR), Artificial Neural Network (ANN).

How to Cite this Article?

Khademi,F., Akbari,M., and Jamal,S,M. (2015). Prediction of Compressive Strength of Concrete by Data-Driven Models. i-manager’s Journal on Civil Engineering, 5(2), 16-23. https://doi.org/10.26634/jce.5.2.3350

References

[1]. Alshihri, M. M., Azmy, A. M., & El-Bisy, M. S. (2009). “Neural networks for predicting compressive strength of structural light weight concrete”. Construction and Building Materials, Vol.23, No.6,pp. 2214-2219.
[2]. Atici, U. (2011). “Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network”. Expert Systems with applications, Vol. 38, No. 8, pp.9609-9618.
[3]. Bilim, C., Atis, C. D., Tanyildizi, H., & Karahan, O. (2009). “Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network”. Advances in Engineering Software, Vol.40, No.5, pp.334-340.
[4]. Deepa, C., SathiyaKumari, K., & Sudha, V. P. (2010). “Prediction of the compressive strength of high performance concrete mix using tree based modeling”. International Journal of Computer Applications, Vol.6, No.5, pp.18-24.
[5]. Duan, Z. H., Kou, S. C., & Poon, C. S. (2013). “Using artificial neural networks for predicting the elastic modulus of recycled aggregate concrete”. Construction and Building Materials, Vol.44, pp.524-532.
[6]. Hola, J., & Schabowicz, K. (2005). “Application of artificial neural networks to determine concrete compressive strength based on non-destructive tests”. Journal of Civil Engineering and Management, Vol.11, No.1, pp. 23-32.
[7]. Jang, J. S. R., & Sun, C. T. (1996). Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice-Hall, Inc.
[8]. Jeong,.D. I. and Kim, Y.O. (2005). “Rainfall- Runoff Models Using Artificial Neural Networks for Ensemble Streamflow Prediction”, Hydrol. Process. Vol.19, pp.3819–3835.
[9]. Kewalramani, M. A., & Gupta, R. (2006). “Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks”. Automation in Construction, Vol.15, No.3, pp.374-379.
[10]. Kosmatka, S. H., & Panarese, W. C. (2002). “Design and control of concrete mixtures”. Portland Cement Association.
[11]. Mansour, M. Y., Dicleli, M., Lee, J. Y., & Zhang, J. (2004). “Predicting the shear strength of reinforced concrete beams using artificial neural networks”, Engineering Structures, Vol.26, No.6, pp.781-799.
[12]. Naderpour, H., Kheyroddin, A., & Amiri, G. G. (2010). “Prediction of FRP-confined compressive strength of concrete using artificial neural networks”. Composite Structures, Vol.92, No.12, pp.2817-2829.
[13]. Ni, H. G., & Wang, J. Z. (2000). “Prediction of compressive strength of concrete by neural networks”. Cement and Concrete Research, Vol.30, No.8, pp.1245- 1250.
[14]. Özcan, F., Atis, C. D., Karahan, O., Uncuoglu, E., & Tanyildizi, H. (2009). “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.
[15]. Öztas, A., Pala, M., Özbay, E., Kanca, E., Çag?lar, N., & Bhatti, M. A. (2006). “Predicting the compressive strength and slump of high strength concrete using neural network”. Construction and Building materials, Vol.20, No.9, pp.769-775.
[16]. Pala, M., Özbay, E., Öztas, A., & Yuce, M. I. (2007). “Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks”. Construction and Building Materials, Vol.21, No.2, pp.384-394.
[17]. Popovics, S. (1998). “History of a mathematical model for strength development of Portland cement concrete”. ACI Materials Journal, Vol.95, No.5.
[18]. Sadrmomtazi, A., Sobhani, J., & Mirgozar, M. A. (2013). “Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS”. Construction and Building Materials, Vol.42, pp.205-216.
[19]. K. Samrajyam, B. Sobha, T. D. Gunneswara Rao and R.L.N. Sai Prasad (2014). “Plastic Optic Fiber (POF) Based Phase Difference Measurement Method for Estimation Of Crack Mouth Opening Displacement (CMOD) in Concrete”. i-manager's Journal on Civil Engineering, 4(2), Mar-May 2014, Print ISSN 2231- 1068, E-ISSN 2249-0779, pp. 13-19.
[20]. S. Seshaphani, Seshadri Sekhar.T, Srinivasa Rao, Sravana and Sarika. P (2013). “Studies on Strength, Acid Attack and Sulphate Attack of High Strength Self Compacting Concrete Using Mineral Admixtures”, imanager's Journal on Civil Engineering, 3(1) Dec-Feb, 2013, Print ISSN 2231- 1068, E-ISSN 2249-0779, pp.30-34 .
[21]. Sobhani, J., Najimi, M., Pourkhorshidi, A. R., & Parhizkar, T. (2010). “Prediction of the compressive strength of no-slump concrete: a comparative study of regression, neural network and ANFIS models”. Construction and Building Materials, Vol. 24, No.5, pp.709-718.
[22]. Tsivilis, S., & Parissakis, G. (1995). “A mathematical model for the prediction of cement strength”. Cement and Concrete research, Vol.25, No.1, pp.9-14.
[23]. T. Senthil Vadivel, R. Thenmozhi and M. Doddurani (2013). “Analyzing the Behaviour of Concrete with Waste Ceramic as Fine Aggregate”. i-manager's Journal on Civil Engineering, 3(1) Dec-Feb, 2013, Print ISSN 2231- 1068, EISSN 2249-0779, pp.25-29.
[24]. Yeh, I. C. (2007). “Modeling slump flow of concrete using second-order regressions and artificial neural networks”. Cement and Concrete Composites, Vol.29, No.6, pp.474-480.
[25]. Zain, M. F. M., & Abd, S. M. (2009). Multiple regression model for compressive strength prediction of high performance concrete. Journal of Applied Sciences, Vol.9, No.1, pp.155-160.

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