Publication: Properties prediction of environmentally friendly ultra-highperformance concrete using artificial neural networks
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Abbas, S., Nehdi, M. L., & Saleem, M. A. (2016). Ultra-high performance concrete: Mechanical performance, durability, sustainability and implementation challenges. International Journal of Concrete Structures and Materials, 10(3), 271–295. https://doi.org/10.1007/s40069-016-0157-4
Abdollahzadeh, A., Masoudnia, R, & Aghababaei, S. (2011). Predict strength of rubberized concrete using atrificial neural network. WSEAS Transactions on Computers, 10(2), 31–40.
Abell an-Garc ıa, J., Nu nez-L ~ opez, A., Torres-Castellanos, N., & Fern andez-Gomez, J. ( 2019). Effect of FC3R on the properties of ultra-high-performance concrete with recycled glass [Efecto Del FC3R En Las Propiedades Del Concreto de Ultra Altas Prestaciones Con Vidrio Reciclado]. Dyna, 86(211), 84–92. http://doi.org/10.15446/dyna.v86n211.79596
Abell an, J., Fern andez, J., Torres, N., & Nu nez, A. ( ~ 2020). Statistical optimization of ultra-high-performance glass concrete. ACI Materials Journal, 117(1), 243–254. https://doi.org/10.14359/51720292
Abellan, J., Torres, N., Nu nez, A., & Fern ~ andez, J. (2018a). Influencia Del Exponente de Fuller, La Relacion Agua Conglomerante y El Contenido En Policarboxilato En Concretos de Muy Altas Prestaciones [Paper presentation]. IV Congreso Internacional de Ingenieria Civil, Havana, Cuba.
Abellan, J., Torres, N., Nu nez, A., & Fern ~ andez, J. (2018b). Ultra high performance fiber reinforced concrete: State of the art, applications and possibilities into the Latin American market [Paper presentation]. XXXVIII Jornadas Sudamericanas de Ingenier ıa Estructural, Lima, Peru.
Adeli, H. (2001). Neural networks in civil engineering: 1989 2000. Computer-Aided Civil and Infrastructure Engineering, 16(2), 126–142. https://doi.org/10.1111/0885-9507.00219
Aderaw, M., Muse, S., & Abiero, Z. C. (2018). Artificial neural network based modelling approach for strength prediction of concrete incorporating agricultural and construction wastes. Construction and Building Materials, 190, 517–525.
Anderson, J. A. (1983). Cognitive and psychological computation with neural models. IEEE Transactions on Systems, Man, and Cybernetics, 13(5), 799–816. https://doi.org/10.1109/TSMC.1983.6313074
Arizzi, A., & Cultrone, G. (2018). Comparing the pozzolanic activity of aerial lime mortars made with metakaolin and fluid catalytic cracking catalyst residue: A petrographic and physical-mechanical study. Construction and Building Materials, 184, 382–390. https://doi.org/10.1016/j.conbuildmat.2018.07.002
ASTM. 2010. Standard test method for compressive strength of hydraulic cement mortars (using 2-in. or [50-Mm] cube specimens). American Society for Testing and Materials C-109/109M (C109/C109M – 11b): 1–9.
ASTM and ASTM C1437. 2016. Standard test method for flow of hydraulic cement mortar. American Society for Testing and Materials C-1437 (C1437), 1–2.
Bal, L., & Buyle-Bodin, F. (2013). Artificial neural network for predicting drying shrinkage of concrete. Construction and Building Materials, 38, 248–254. https://doi.org/10.1016/j.conbuildmat.2012.08.043
Bharathi, S. D., Manju, R., & Premalatha, J. (2017). Prediction of compressive strength for self-compacting concrete (SCC) using artificial intelligence and regression analysis. International Journal of ChemTech Research, 10(8), 263–275.
Camacho, E., Lopez, J. A., & Serna, P. ( 2012). Definition of three levels of performance for UHPFRCVHPFRC with available materials, in Proceedings of Hipermat 2012. In M. Schmidt, E. Fehling, C. Glotzbach, S. Frohlich, & S. Piotrowski (Eds.), € 3rd International Symposium on UHPC and Nanotechnology for Construction Materials (pp. 249–256). Kassel University Press.
Camacho Torregrosa, E. (2013). Dosage optimization and bolted connections for UHPFRC ties. Polytechnic University of Valencia.
Chandwani, V., Agrawal, V., & Nagar, R. (2014). Applications of artificial neural networks in modeling compressive strength of concrete: A state of the art review. Advances in Artificial Neural Systems, 2014(4), 1–56. https://doi.org/10.1155/2014/629137
Chandwani, V., Agrawal, V., & Nagar, R. (2015). Modeling slump of ready mix concrete using genetic algorithms assisted training of artificial neural networks. Expert Systems with Applications, 42(2), 885–893. https://doi.org/10.1016/j.eswa.2014.08.048
Chollet, F., & Allaire, J. J. (2018). Deep learning with R. Manning Publications Co.
Demir, F. (2008). Prediction of elastic modulus of normal and high strength concrete by artificial neural networks. Construction and Building Materials, 22, 1428–1435.
Duan, Z. H., Kou, S. C., & Poon, C. S. (2013). Prediction of compressive strength of recycled aggregate concrete using artificial neural networks. Construction and Building Materials, 40, 1200–1206. https://doi. org/10.1016/j.conbuildmat.2012.04.063
Estebon, M. D. (1997). Perceptrons: An associative learning network by. Virginia Tech (June 1960).
Franceschini, S., Gandola, E., Martinoli, M., Tancioni, L., & Scardi, M. (2018). Cascaded neural networks improving fish species prediction accuracy: The role of the biotic information. Scientific Reports, 8(1), 1–12. https://doi.org/10.1038/s41598-018-22761-4
Funk, J. E., & Dinger, D. R. (1994). Predictive process control of crowded particulate suspensions. Applied to ceramic manufacturing. Springer Science.
Ghafari, E., & Al. (2012). Optimization of UHPC by adding nanomaterials, in Proceedings of Hipermat 2012 [Paper presentation]. 3rd International Symposium on UHPC and Nanotechnology for Construction Materials (pp. 71–78), Kassel, Alemania.
Ghafari, E., Bandarabadi, M., Costa, H., & Julio, E. ( 2015). Prediction of fresh and hardened state properties of UHPC: Comparative study of statistical mixture design and an artificial neural network model. Journal of Materials in Civil Engineering, 27(11), 04015017. https://doi.org/10.1061/(ASCE)MT.1943-5533. 0001270
Ghafari, E., Costa, H., Nuno, E., & Santos, B. (2014). RSM-based model to predict the performance of selfcompacting UHPC reinforced with hybrid steel micro-fibers. Construction and Building Materials, 66, 375–383. https://doi.org/10.1016/j.conbuildmat.2014.05.064
Ghafari, E., Costa, H., Nuno, E., Santos, B., Costa, H., & Julio, E. ( 2015). Critical review on eco-efficient ultra high performance concrete enhanced with nano-materials. Construction and Building Materials Journal, 101, 201–208. https://doi.org/10.1016/j.conbuildmat.2015.10.066
Gupta, S. (2013). Using artificial neural network to predict the compressive strength of concrete containing nano-silica. Civil Engineering and Architecture, 1(3), 96–102.
Huang, Z., & Cao, F. (2012). Effects of nano-materials on the performance of UHPC. 材料导报B:研究篇, 26(9), 136–141.
Jamalaldin, S., Hakim, S., Noorzaei, J., Jaafar, M. S., & Jameel, M. (2011). Application of artificial neural networks to predict compressive strength of high strength concrete. International Journal of the Physical Sciences, 6(5), 975–981.
Kalra, G., & Joseph, E. (2016). Research review and modeling of concrete compressive strength using artificial neural networks. Construction and Building Materials, 3(2), 672–677.
Khan, S. U., & Ayub, T. (2013, January). Prediction of compressive strength of plain concrete confined with ferrocement using artificial neural network (ANN) and comparison with existing mathematical models, American Journal of Civil Engineering and Architecture, 1(1),7–14.
Khashman, A., & Akpinar, P. (2017). Science direct non-destructive prediction of concrete compressive strength using neural networks prediction of concrete compressive strength using neural networks. Procedia Computer Science, 108, 2358–2362. https://doi.org/10.1016/j.procs.2017.05.039
Kubens, S. (2010). Interaction of cement and admixtures and its influence on rheological properties. (Vol. 49). Edited by V. Culliver, & A. Inhaberin. Internationaler wissenschaftlicher Fachverlag.
De Larrard, F. (1999). Concrete mixture proportioning: A scientific approach. In Modern concrete technology series. London: E&FN SPON.
Li, W., Huang, Z., Zu, T., Shi, C., Duan, W. H., & Shah, S. P. (2016). Influence of nanolimestone on the hydration, mechanical strength, and autogenous shrinkage of ultrahigh-performance concrete. Journal of Materials in Civil Engineering, 28(1), 04015068–04015069. https://doi.org/10.1061/(ASCE)MT.1943- 5533.0001327
Meng, W., Samaranayake, V. A., & Khayat, K. H. (2018). Factorial design and optimization of UHPC with lightweight sand. ACI Materials Journal, 345(435M), 327–335.
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. American Society of Agricultural and Biological Engineers, 50(3), 885–900. https://doi.org/10.13031/2013.23153
Mushgil, H. M., Alani, H. A., & George, L. E. (2015). Comparison between resilient and standard back propagation algorithms efficiency in pattern recognition. International Journal of Scientific & Engineering Research, 6(3), 773–778.
Naderpour, H., Kheyroddin, A., & Ghodrati Amiri, G. (2010). Prediction of FRP-confined compressive strength of concrete using artificial neural networks. Composite Structures, 92(12), 2817–2829. https:// doi.org/10.1016/j.compstruct.2010.04.008
Naoum, R. S., & Al-Sultani, Z. N. (2013). Hybrid system of learning vector quantization and enhanced resilient backpropagation artificial neural. International Journal of Recent Research and Applied Studies, 14, 333–339.
Olden, J. D., Joy, M. K., & Death, R. G. (2004). An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological Modelling, 178(3-4), 389–397. https://doi.org/10.1016/j.ecolmodel.2004.03.013
Parichatprecha, R., & Nimityongskul, P. (2009). Analysis of durability of high performance concrete using artificial neural networks. Construction and Building Materials, 23(2), 910–917. https://doi.org/10.1016/j. conbuildmat.2008.04.015
Pedrajas, C., Rahhal, V., & Talero, R. (2014). Determination of characteristic rheological parameters in portland cement pastes. Construction and Building Materials, 51, 484–491. https://doi.org/10.1016/j.conbuildmat.2013.10.004
Prasad, N., Singh, R., & Lal, S. P. (2013). Comparison of back propagation and resilient propagation algorithm for spam classification. Proceedings of International Conference on Computational Intelligence, Modelling and Simulation, 29–34.
Puertas, F., Santos, H., Palacios, M., & Mart ınez-Ram ırez, S. (2005). Polycarboxylate superplasticiser admixtures: Effect on hydration, microstructure and rheological behaviour in cement pastes. Advances in Cement Research, 17(2), 77–89. https://doi.org/10.1680/adcr.2005.17.2.77
R Core Team. (2018). R: A language and environment for statistical computing. R Core Team. Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386–408. https://doi.org/10.1037/h0042519
Rumelhart, D. E., Hinton, G. E., & R. J. & Williams. (1986). Learning internal representations by error propagation. In D. Rumelhart & J. McClelland (Eds.), Parallel distributed processing: Explorations in the microstructures of cognition (pp. 318–362), MIT Press.
Sahoo, K., Sarkar, P., & Robin Davis, P. (2016). Artificial neural networks for prediction of compressive strength of recycled aggregate concrete. Int’l Journal of Research in Chemical, Metallurgical and Civil Engg, 3(1), 81–85. http://dx.doi.org/10.15242/IJRCMCE.IAE0316414
Schmidt, C., & Schmidt, M. (2012). Whitetopping of asphalt and concrete pavements with thin layers of ultra-high-performance concrete - construction and economic efficiency [Paper presentation]. Proceedings of Hipermat 2012 - 3rd International Symposium on UHPC and Nanotechnology for Construction Materials, Kassel, Germany.
Soliman, N. A., & Tagnit-Hamou, A. (2017a). Partial substitution of silica fume with fine glass powder in UHPC: Filling the micro gap. Construction and Building Materials, 139, 374–383. https://doi.org/10.1016/ j.conbuildmat.2017.02.084
Soliman, N. A., & Tagnit-Hamou, A. (2017b). Using glass sand as an alternative for quartz sand in UHPC. Construction and Building Materials, 145, 243–252. https://doi.org/10.1016/j.conbuildmat.2017.03.187
Srinivasulu, S., & Jain, A. (2006). A comparative analysis of training methods for artificial neural network rainfall – runoff models. Applied Soft Computing, 6(3), 295–306. https://doi.org/10.1016/j.asoc.2005.02. 002
Taghaddos, H., Mahmoudzadeh, F., Pourmoghaddam, A., & Shekarchizadeh, M. (2004). Prediction of compressive strength behaviour in RPC with applying an adaptive network-based fuzzy interface system [Paper presentation]. Proceedings of the International Symposium on Ultra High Performance Concrete, Kassel, Alemania.
Tagnit-Hamou, A., Soliman, N., & Omran, A. (2016a). Green ultra - high - performance glass concrete [Paper presentation]. First International Interactive Symposium on UHPC – 2016. Des Moines, Iowa, USA. https://doi.org/10.21838/uhpc.2016.35
Tagnit-Hamou, A., Soliman, N., & Omran, A. (2016b). Green ultra - high - performance glass concrete. First International Interactive Symposium on UHPC – 2016, 3(1), 1–10.
Torre, A., Garcia, F., Moromi, I., Espinoza, P., & Acuna, L. ( ~ 2015). Prediction of compression strength of high performance concrete using artificial neural networks [Paper presentation]. VII International Congress of Engineering Physics, Mexico City, Mexico (Vol. 012010). https://doi.org/10.1088/1742-6596/582/1/ 012010
Torres Castellanos, N. (2014). Estudio en estado fresco y endurecido de concretos adicionados con catalizador de craqueo catal Itico usado (fcc) nancy. Universidad Nacional de Colombia.
Torres Castellanos, N., & Torres Agredo, J. (2010). Uso Del Catalizador Gastado de Craqueo Catal ıtico (FCC) Como Adicion Puzol anica [Revision using spent fluid catalytic cracking (FCC) catalyst as pozzolanic add- ition — A review]. Ingenieria & Investigacion Journal, 30(2), 35–42.
Uysal, M., & Tanyildizi, H. (2012). Estimation of compressive strength of self compacting concrete containing polypropylene fiber and mineral additives exposed to high temperature using artificial neural network. Construction and Building Materials, 27(1), 404–414. https://doi.org/10.1016/j.conbuildmat.2011. 07.028
Yu, R., Spiesz, P., & Brouwers, H. J. H. (2014). Mix design and properties assessment of ultra-high performance fibre reinforced concrete (UHPFRC). Cement and Concrete Research, 56, 29–39. https://doi.org/10. 1016/j.cemconres.2013.11.002
Zhang, J., & Zhao, Y. (2017). Experimental investigation and prediction of compressive strength of ultrahigh performance concrete (UHPC) containing supplementary cementitious materials. Hindawi Advances in Materials Science and Engineering, 2017, 522–525.