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.
