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Articles

Prediction of self-compacting concrete strength using artificial neural networks

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References

  • Abd El-Aleem, S. (2015). Activation of granulated blast-furnace slag using lime rich sludge in presence and absence of rice husk ash. International Journal of Innovative Technology and Exploring Engineering, 5, 43–51.
  • Adeli, H. (2001). Neural networks in civil engineering: 1989–2000. Computer-Aided Civil and Infrastructure Engineering, 16, 126–142.10.1111/mice.2001.16.issue-2
  • Aiad, I., Abd El-Aleem, S., & El-Didamony, H. (2002). Effect of delaying addition of some concrete admixtures on the rheological properties of cement pastes. Cement and Concrete Research, 32, 1839–1843.10.1016/S0008-8846(02)00886-4
  • Akkurt, S., Tayfur, G., & Can, S. (2004). Fuzzy logic model for the prediction of cement compressive strength. Cement and Concrete Research, 34, 1429–1433.10.1016/j.cemconres.2004.01.020
  • Alyamaç, K. E., & Ince, R. (2009). A preliminary concrete mix design for SCC with marble powders. Construction and Building Materials, 23, 1201–1210.10.1016/j.conbuildmat.2008.08.012
  • Asteris, P. G., & Plevris, V. (2013). Neural network approximation of the masonry failure under biaxial compressive stress. Proceeding of ECCOMAS Special Interest Conference – SEECCM 2013: 3rd South-East European Conference on Computational Mechanics – An IACM Special Interest Conference, 584–598.
  • Asteris, P. G., & Plevris, V. (in press). Anisotropic masonry failure criterion using artificial neural networks. Neural Computing and Applications (NCAA),. doi:10.1007/s00521-016-2181-3
  • Asteris, P. G., Tsaris, A. K., Cavaleri, L., Repapis, C. C., Papalou, A., Di Trapani, F., & Karypidis, D. F. (2016). Prediction of the fundamental period of infilled rc frame structures using artificial neural networks. Computational Intelligence and Neuroscience, 2016, 5104907.
  • Atici, U. (2011). Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network. Expert Systems with Applications, 38, 9609–9618. doi:10.1016/j.eswa.2011.01.156
  • Bartlett, P. L. (1998). The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Transactions on Information Theory, 44, 525–536.10.1109/18.661502
  • Baskar, I., Ramanathan, P., & Venkatasubramani, R. (2012). Influence of silica fume on properties of self-compacting concrete. Int. J. Emerg. Trends Eng. Dev., 4, 757–767.
  • Baykal, G., & Döven, A. G. (2000). Utilization of fly ash by pelletization process; theory, application areas and research results. Resources, Conservation and Recycling, 30, 59–77.10.1016/S0921-3449(00)00042-2
  • Baykasoğlu, A., Dereli, T. U., & Tanış, S. (2004). Prediction of cement strength using soft computing techniques. Cement and Concrete Research, 34, 2083–2090.10.1016/j.cemconres.2004.03.028
  • Berry, M. J. A., & Linoff, G. (1997). Data mining techniques. New York, NY: Wiley.
  • Blum, A. (1992). Neural networks in C++. New York, NY: Wiley.
  • Boger, Z., & Guterman, H. (1997). Knowledge extraction from artificial neural network models. Orlando, FL: IEEE Systems, Man, and Cybernetics Conference.10.1109/ICSMC.1997.633051
  • Boukendakdji, O., Kadri, E. H., & Kenai, S. (2012). Effects of granulated blast furnace slag and superplasticizer type on the fresh properties and compressive strength of self-compacting concrete. Construction and Building Materials, 34, 583–590.
  • Boukendakdji, O., Kenai, S., Kadri, E. H., & Rouis, F. (2009). Effect of slag on the rheology of fresh self-compacted concrete. Construction and Building Materials, 23, 2593–2598.10.1016/j.conbuildmat.2009.02.029
  • Brouwers, H. J. H., & Radix, H. J. (2005). Self-compacting concrete: Theoretical and experimental study. Cement and Concrete Research, 35, 2116–2136.10.1016/j.cemconres.2005.06.002
  • Chen, Z. (2013). An overview of bayesian methods for neural spike train analysis. Computational Intelligence and Neuroscience, 2013, 251905.
  • Cladera, A., & Marí, A. R. (2004). Shear design procedure for reinforced normal and high-strength concrete beams using artificial neural networks – Part I: Beams without stirrups. Engineering Structures, 26, 917–926. doi:10.1016/j.engstruct.2004.02.010
  • Delen, D., Sharda, R., & Bessonov, M. (2006). Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks. Accident Analysis and Prevention, 38, 434–444.10.1016/j.aap.2005.06.024
  • Dias, W. P. S., & Pooliyadda, S. P. (2001). Neural networks for predicting properties of concretes with admixtures. Construction and Building Materials, 15, 371–379.10.1016/S0950-0618(01)00006-X
  • Dinakar, P., Sethy, K. P., & Sahoo, U. C. (2013). Design of self-compacting concrete with ground granulated blast furnace slag. Materials and Design, 43, 161–169.10.1016/j.matdes.2012.06.049
  • El-Alfi, E. A., Radwan, A. M., & Abd El-Aleem, S. (2004). Effect of limestone fillers and silica fume pozzolana on the characteristics of sulfate resistant cement pastes. Ceramics Silikaty, 48, 29–33.
  • Fathi, A., Shafiq, N., Nuruddin, M. F., & Elheber, A. (2013). Study the effectiveness of the different pozzolanic material on self-compacting concrete. ARPN Journal of Engineering and Applied Sciences, 8, 229–305.
  • Felekoğlu, B., Türkel, S., & Baradan, B. (2007). Effect of water/cement ratio on the fresh and hardened properties of self-compacting concrete. Building and Environment, 42, 1795–1802.10.1016/j.buildenv.2006.01.012
  • Gandage, A. S., Ram, V. V., Sivakumar, M. V. N., Vasan, A., Venu, M., & Yaswanth, A. B. (2013). Optimization of class C flyash dosage in self-compacting concrete for pavement applications. Proceedings of the International Conference on Innovations in Concrete for Meeting Infrastructure Challenge, Hyderabad, Andhra Pradesh, India (pp. 213–226).
  • Gesoglu, M., Guneyisi, E., & Ozbay, E. (2009). Properties of self-compacting concretes made with binary, ternary and quarternary cementitious blends of fly ash, blast furnace slag and silica fume. Construction and Building Materials, 23, 1847–1854.10.1016/j.conbuildmat.2008.09.015
  • Gesoglu, M., & Ozbay, E. (2007). Effects of mineral admixtures on fresh and hardened properties of self-compacting concretes: binary, ternary and quaternary systems. Materials and Structures, 40, 923–937.10.1617/s11527-007-9242-0
  • Gettu, R., Izquierdo, J., Gomes, P. C. C., & Josa, A. (2002). Development of high-strength self-compacting concrete with fly ash: A four-step experimental methodology (pp. 217–224). Proceedings of the 27th Conference on Our World in Concrete and Structures, Singapore.
  • Giovanis, D. G., & Papadopoulos, V. (2015). Spectral representation-based neural network assisted stochastic structural mechanics. Engineering Structures, 84, 382–394.10.1016/j.engstruct.2014.11.044
  • González-Taboada, I., González-Fonteboa, B., Martínez-Abella, F., & Pérez-Ordóñez, J. L. (2016). Prediction of the mechanical properties of structural recycled concrete using multivariable regression and genetic programming. Construction and Building Materials, 106, 480–499. doi:10.1016/j.conbuildmat.2015.12.136
  • Grdic, Z., Despotovic, I., & Curcic, G. T. (2008). Properties of self-compacting concrete with different types of additives. Facta Universitatis-Series: Architecture and Civil Engineering, 6, 173–177.
  • Güneyisi, E., Gesoglu, M., Ali Azez, O., & Öznur Öz, H. (2016). Effect of nano silica on the workability of self-compacting concretes having untreated and surface treated lightweight aggregates. Construction and Building Materials, 115, 371–380.10.1016/j.conbuildmat.2016.04.055
  • Heikal, M., Abd El-Aziz, M., Abd El-Aleem, S., & El-Didamony, H. (2004). Effect of polycarboxylate on rice husk ash Pozzolanic cement. Silicates Industrials, 69, 73–84.
  • Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2, 359–366.10.1016/0893-6080(89)90020-8
  • Iruansi, O., Guadagnini, M., Pilakoutas, K., & Neocleous, K. (2010). Predicting the shear strength of RC beams without stirrups using Bayesian neural network. 4th International Workshop on Reliable Engineering Computing (REC 2010), Singapore.
  • Joseph, G., & Ramamurthy, K. (2009). Influence of fly ash on strength and sorption characteristics of cold-bonded fly ash aggregate concrete. Construction and Building Materials, 23, 1862–1870.10.1016/j.conbuildmat.2008.09.018
  • Karlik, B., & Olgac, A. V. (2011). Performance analysis of various activation functions in generalized MLP architectures of neural networks. International Journal of Artificial Intelligence And Expert Systems (IJAE), 1, 111–122.
  • Kayali, O. (2008). Fly ash lightweight aggregates in high performance concrete. Construction and Building Materials, 22, 2393–2399.10.1016/j.conbuildmat.2007.09.001
  • Kostić, S., & Vasović, D. (2015). Prediction model for compressive strength of basic concrete mixture using artificial neural networks. Neural Computing and Applications, 26, 1005–1024. doi:10.1007/s00521-014-1763-1
  • Lamanna, J., Malgaroli, A., Cerutti, S., & Signorini, M. G. (2012). Detection of fractal behavior in temporal series of synaptic quantal release events: A feasibility study. Computational Intelligence and Neuroscience, 2012, 704673.
  • Lee, S. C. (2003). Prediction of concrete strength using artificial neural networks. Engineering Structures, 25, 849–857.10.1016/S0141-0296(03)00004-X
  • Lourakis, M. I. A. (2005). A brief description of the Levenberg-Marquardt algorithm Implemened by levmar. Hellas (FORTH): Institute of Computer Science Foundation for Research and Technology. Retrieved from http://www.ics.forth.gr/~lourakis/levmar/levmar
  • Malagavelli, V., & Manalel, P. A. (2014). Modeling of compressive strength of admixture-based self compacting concrete using fuzzy logic and artificial neural networks. Asian Journal of Applied Sciences, 7, 536–551.10.3923/ajaps.2014.536.551
  • Mansouri, I., & Kisi, O. (2015). Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches. Composites Part B: Engineering, 70, 247–255.10.1016/j.compositesb.2014.11.023
  • Mansouri, I., Ozbakkaloglu, T., Kisi, O., & Xie, T. (2016). Predicting behavior of FRP-confined concrete using neuro fuzzy, neural network, multivariate adaptive regression splines and M5 model tree techniques. Materials and Structures, 49, 4319–4334.10.1617/s11527-015-0790-4
  • Memon, S. A., Shaikh, M. A., & Akbar, H. (2011). Utilization of rice husk ash as viscosity modifying agent in self compacting concrete. Construction and Building Materials, 25, 1044–1048.10.1016/j.conbuildmat.2010.06.074
  • Özcan, F., Atiş, C. D., Karahan, O., Uncuoǧlu, 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, 40, 856–863.
  • Papadopoulos, V., Giovanis, D. G., Lagaros, N. D., & Papadrakakis, M. (2012). Accelerated subset simulation with neural networks for reliability analysis. Computer Methods in Applied Mechanics and Engineering, 223–224, 70–80.10.1016/j.cma.2012.02.013
  • Pérez, J. L., Cladera, A., Rabuñal, J. R., & Martínez-Abella, F. (2012). Optimization of existing equations using a new Genetic Programming algorithm: Application to the shear strength of reinforced concrete beams. Advances in Engineering Software, 50, 82–96. doi:10.1016/j.advengsoft.2012.02.008
  • Phani, S. S., Sekhar, S. T., Rao, S., & Sravana, P. (2013). High strength self-compacting concrete using mineral admixtures. Indian Concrete Journal March 2013PP, 87, 42–47.
  • Plevris, V., & Asteris, P. G. (2014a). Modeling of masonry compressive failure using Neural Networks. Proceedings of OPT-i 2014 – 1st International Conference on Engineering and Applied Sciences Optimization, 2843–2861.
  • Plevris, V., & Asteris, P. G. (2014b). Modeling of masonry failure surface under biaxial compressive stress using Neural Networks. Construction and Building Materials, 55, 447–461.10.1016/j.conbuildmat.2014.01.041
  • Plevris, V., & Asteris, P. G. (2015). Anisotropic failure criterion for brittle materials using Artificial Neural Networks. Proceedings of COMPDYN 2015 - 5th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, 2259–2272.
  • Rahman, M. E., Muntohar, A. S., Pakrashi, V., Nagaratnam, B. H., & Sujan, D. (2014). Self compacting concrete from uncontrolled burning of rice husk and blended fine aggregate. Materials & Design, 55, 410–415.10.1016/j.matdes.2013.10.007
  • Rao, N. V. R., Rao, P. S., Sravana, P., & Sekhar, T. S. (2009). Studies on relationship of water-powder ratio and compressive strength of self compacted concrete. Proceedings of the 34th Conference on Our World in Concrete and Structures, Singapore (pp. 1–8).
  • Rouis, F. (2009). Effect of slag on the rheology of fresh self-compacted concrete. Construction and Building Materials, 23, 2593–2598.
  • Şahmaran, M., Yaman, I. O., & Tokyay, M. (2009). Transport and mechanical properties of self consolidating concrete with high volume fly ash. Cement and Concrete Composites, 31, 99–106.10.1016/j.cemconcomp.2008.12.003
  • Sfikas, I. P., & Trezos, K. G. (2013). Effect of composition variations on bond properties of self-compacting concrete specimens. Construction and Building Materials, 41, 252–262.10.1016/j.conbuildmat.2012.11.094
  • Siddique, R. (2011). Properties of self-compacting concrete containing class F fly ash. Materials & Design, 32, 1501–1507.10.1016/j.matdes.2010.08.043
  • Sonebi, M. (2004). Medium strength self-compacting concrete containing fly ash: Modelling using factorial experimental plans. Cement and Concrete Research, 34, 1199–1208.10.1016/j.cemconres.2003.12.022
  • Sonebi, M., & Cevik, A. (2009). Genetic programming based formulation for fresh and hardened properties of self-compacting concrete containing pulverised fuel ash. Construction and Building Materials, 23, 2614–2622. doi:10.1016/j.conbuildmat.2009.02.012
  • Sukumar, B., Nagamani, K., & Raghavan, R. S. (2008). Evaluation of strength at early ages of self-compacting concrete with high volume fly ash. Construction and Building Materials, 22, 1394–1401.10.1016/j.conbuildmat.2007.04.005
  • Topçu, I. B., & Saridemir, M. (2008). Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Computational Materials Science, 41, 305–311.10.1016/j.commatsci.2007.04.009
  • Trtnik, G., Kavčič, F., & Turk, G. (2009). Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks. Ultrasonics, 49, 53–60.10.1016/j.ultras.2008.05.001
  • Valcuende, M., Marco, E., Parra, C., & Serna, P. (2012). Influence of limestone filler and viscosity-modifying admixture on the shrinkage of self-compacting concrete. Cement and Concrete Research, 42, 583–592.10.1016/j.cemconres.2012.01.001
  • Waszczyszyn, Z., & Ziemiański, L. (2001). Neural networks in mechanics of structures and materials – New results and prospects of applications. Computers and Structures, 79, 2261–2276.10.1016/S0045-7949(01)00083-9
  • Zhao, H., Sun, W., Wu, X., & Gao, B. (2015). The properties of the self-compacting concrete with fly ash and ground granulated blast furnace slag mineral admixtures. Journal of Cleaner Production, 95, 66–74.10.1016/j.jclepro.2015.02.050

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