Workability and strength optimisation of concrete utilising artificial sand blended with natural sand for road pavement

References

• ACI 318-14, 2014. Building code requirements for structural concrete. Farmington Hills, MI, USA: ACI 318-14, American Concrete Institute.
   Google Scholar
• Ahmad, S., and Mahmood, S., 2008. Effects of Crushed And Natural Sand on The Properties of Fresh and Hardened Concrete. In Proceeding of the 33rd Conference on Our World in Concrete & Structures, Singapore, pp. 25–27.
   Google Scholar
• Ahmad, S., 2007. Optimum concrete mixture design using locally available ingredients. The Arabian Journal for Science and Engineering, 32 (1), 27–33.
   Google Scholar
• Alexander, M.G., 1993. Two experimental techniques for studying the effects of the interfacial zone between cement paste and rock. Cement and Concrete Research, 23 (3), 567–575. doi:10.1016/0008-8846(93)90006-U.
   Web of Science ®Google Scholar
• Alshihri, M.M., Azmy, A.M., and El-Bisy, M.S., 2009. Neural networks for predicting compressive strength of structural light weight concrete. Construction and Building Materials, 23 (6), 2214–2219.
   Web of Science ®Google Scholar
• ASTM C143-20, 2002. Standard test method for slump of hydraulic-cement concrete. West Conshohocken, PA, USA: ASTM 2 International.
   Google Scholar
• ASTM D6272-02, 2002. Standard test method for flexural properties of unreinforced and reinforced plastics and electrical insulating materials by four-point bending. West Conshohocken, PA, USA: ASTM International.
   Google Scholar
• Bazant, Z.P., and Planas, J., 1997. Fracture and size effect on concrete and other quasibrittle materials. United States: CRC Press LLC.
   Google Scholar
• Chithra, S., et al., 2016. A comparative study on the compressive strength prediction models for high performance concrete containing nano silica and copper slag using regression analysis and artificial neural networks. Construction and Building Materials, 114, 528–535. doi:10.1016/j.conbuildmat.2016.03.214.
   Web of Science ®Google Scholar
• Chopra, P., Sharma, R.K., and Kumar, M., 2014. Predicting compressive strength of concrete for varying workability using regression models. International Journal of Engineering & Applied Sciences, 6 (4), 10–10. doi:10.24107/ijeas.251233.
   Google Scholar
• Chopra, P., Sharma, R.K., and Kumar, M., 2016. Prediction of compressive strength of concrete using artificial neural network and genetic programming. Advances in Materials Science and Engineering, 2016, 7648467, 1–10.
   Web of Science ®Google Scholar
• Domone, P.L.J., and Soutsos, M.N., 1994. An approach to the proportioning of high-strength concrete mixes. Concrete International, 16 (10), 26–31.
   Google Scholar
• Gencel, O., et al., 2013. Modeling of thermal conductivity of concrete with vermiculite by using artificial neural networks approaches. Experimental Heat Transfer, 26 (4), 360–383.
   Web of Science ®Google Scholar
• Ghazy, M.F., 2020. Optimization of recycled concrete aggregate geopolymer bricks by taguchi technique. Revista de La Construccion, 19 (2), 244–254. doi:10.7764/RDLC.19.2.244.
   Web of Science ®Google Scholar
• Haario, H., Taavitsainen, V.M., and Jokinen, P.A., 1990. A chemometrics/statistics/neural networks toolbox for MATLA. Data Handling in Science and Technology, 6, 133–148.
   Google Scholar
• Hashemi, M., et al., 2018. The effect of coarse to fine aggregate ratio on the fresh and hardened properties of roller-compacted concrete pavement. Construction and Building Materials, 169, 553–566. doi:10.1016/j.conbuildmat.2018.02.
   Web of Science ®Google Scholar
• Jayaram, M.A., Nataraja, M.C., and Ravikumar, C.N., 2009. Elitist genetic algorithm models: optimization of high performance concrete mixes,”. Materials and Manufacturing Processes, 24 (2), 225–229.
   Web of Science ®Google Scholar
• Kalogirou, S.A., 2000. Applications of artificial neural-networks for energy systems. Applied Energy, 67 (1–2), 17–35. doi:10.1016/S0306-2619(00)00005-2.
   Web of Science ®Google Scholar
• Kasperkiewicz, J., 1994. Optimization of concrete mix using a spreadsheet package,”. ACI Materials Journal, 91 (6), 551–559.
   Web of Science ®Google Scholar
• Lam, MN.T., 2020. Using the taguchi technique to optimize the compressive strength of geopolymer mortars. International Journal of Advanced and Applied Sciences, 7 (8), 1–10. doi:10.21833/ijaas.2020.08.00.
   Web of Science ®Google Scholar
• Lam, M.N.T., Nguyen, D.L., and Le, D.H., 2020. Predicting compressive strength of roller-compacted concrete pavement containing steel slag aggregate and fly ash. International Journal of Pavement Engineering, 0 (0), 1–14. doi:10.1080/10298436.2020.1766688.
   Google Scholar
• Lee, B.Y., Kim, J.H., and Kim, J.K., 2009. Optimum concrete mixture proportion based on a database considering regional characteristics. Journal of Computing in Civil Engineering, 23 (5), 258–265.
   Web of Science ®Google Scholar
• Lee, J.H., Lee, J.J., and Cho, B.S., 2012. Effective prediction of thermal conductivity of concrete using neural network method. International Journal of Concrete Structures and Materials, 6 (3), 177–186.
   Web of Science ®Google Scholar
• Ly, H.B., et al., 2019. Development of hybrid machine learning models for predicting the critical buckling load of I-shaped cellular beams. Applied Sciences (Switzerland), 9 (24), 1–21. doi:10.3390/app9245458.
   Web of Science ®Google Scholar
• Mansour, M.Y., et al., 2004. Predicting the shear strength of reinforced concrete beams using artificial neural networks. Engineering Structures, 26 (6), 781–799. doi:10.1016/j.engstruct.2004.01.011.
   Web of Science ®Google Scholar
• Masters, T., 1993. Practical neural network recipes. McGraw-Hill.
   Google Scholar
• Mohamed, G., and Djamila, B., 2018. Physical, mechanical and thermal properties of crushed sand concrete containing rubber waste. MATEC Web of Conferences, 149, 1–5. doi:10.1051/matecconf/201714901076.
   Google Scholar
• Mundra, S., et al., 2016. Crushed rock sand – An economical and ecological alternative to natural sand to optimize concrete mix. Perspectives in Science, 8, 345–347. doi:10.1016/j.pisc.2016.04.070.
   Google Scholar
• Nguyen, D.L., and Duong, M.T., 2019. Compressive resistance of environmental concrete using Fly Ash and fine aggregate for replacing traditional sand. Applied Mechanics and Materials, 889, 283–288. doi:10.4028/www.scientific.net/AMM.889.283.
   Google Scholar
• Nguyen, D.T., Nguyen, D.L., and Lam, M.N.T., 2022. An experimental investigation on the utilization of crushed sand in improving workability and mechanical resistance of concrete. Construction and Building Materials, 326, 1–15. doi:10.1016/j.conbuildmat.2022.126766.
   Web of Science ®Google Scholar
• Naderpour, H., Rafifiean, A.H., and Fakharian, P., 2018. Compressive strength prediction of environmentally friendly concrete using artificial neural networks. Journal of Building Engineering, 16, 213–219.
   Web of Science ®Google Scholar
• Nguyen, Q.H., et al., 2021. Investigation of ANN architecture for predicting shear strength of fiber reinforcement bars concrete beams. PLoS ONE, 16 (4), e0247391, 1–22. doi:10.1371/journal.pone.0247391.
   Web of Science ®Google Scholar
• Neville, A.M., and Brooks, J.J., 2010. Concrete technology. Porto London: Pearson, 460 pages.
   Google Scholar
• Olivia, M., and Nikraz, H., 2012. Properties of fly ash geopolymer concrete designed by Taguchi technique. Materials and Design, 36, 191–198. doi:10.1016/j.matdes.2011.10.036.
   Web of Science ®Google Scholar
• Orhan, H., and Tatlıyer, A., 2016. Comparison of factorial and Taguchi designs in poultry science data. International Journal of Development Research, 06 (06), 8113–8116.
   Google Scholar
• Rajput, S.P.S., 2018. An experimental study on crushed stone dust as fine aggregate in cement concrete. Materials Today: Proceedings, 5 (9), 17540–17547. doi:10.1016/j.matpr.2018.06.070.
   Google Scholar
• Rao, G.A., and Prasad, B.KR., 2002. Influence of the roughness of aggregate surface on the interface bond strength. Cement and Concrete Research, 32 (2), 253–257.
   Web of Science ®Google Scholar
• Ross, P.J., 1996. Taguchi technique for quality engineering (2nd ed.). New York: McGraw-Hill.
   Google Scholar
• Sadrmomtazi, A., Sobhani, J., and Mirgozar, M.A., 2013. Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS. Construction and Building Materials, 42, 205–216. doi:10.1016/j.conbuildmat.2013.01.016.
   Web of Science ®Google Scholar
• Sahu, A.K., Sunil, K., and Sachan, A.K., 2003. Crushed stone waste as fine aggregate for concrete. The Indian Concrete Journal, 2003, 845–847.
   Google Scholar
• Shanmugapriya, T., and Uma, R.N., 2012. Optimization of partial replacement of M-sand By natural sand In high performance concrete With silica fume. International Journal of Engineering Sciences & Emerging Technologies, 2, 73–80.
   Google Scholar
• Singh, T.N., Sinha, S., and Singh, V.K., 2007. Prediction of thermal conductivity of rock through physico-mechanical properties. Building and Environment, 42 (1), 146–155.
   Web of Science ®Google Scholar
• Stangierski, J., Weiss, D., and Kaczmarek, A., 2019. Multiple regression models and artificial neural network (ANN) as prediction tools of changes in overall quality during the storage of spreadable processed gouda cheese. European Food Research and Technology, 245 (11), 2539–2547. doi:10.1007/s00217-019-03369-y.
   Web of Science ®Google Scholar
• Suryanita, R., Jingga, H., Yuniarto, E. (2016) The Application of Artificial Neural Networks in Predicting Structural Response of Multistory Building in The Region of Sumatra Island. In: ICoSE Conference Proceedings, 6. doi: 10.18502/keg.v1i1.526
   Google Scholar
• Tanyildizi, H., and Şahin, M., 2015. Application of Taguchi technique for optimization of concrete strengthened with polymer after high temperature. Construction and Building Materials, 79, 97–103. doi:10.1016/j.conbuildmat.2015.01.039.
   Web of Science ®Google Scholar
• Tapkire, G.V., et al., 2017. Comparative Analysis of River & Crushed Sand in Concrete. 3525–3529. doi.org/10.15680/IJIRSET.2017.0603094.
   Google Scholar
• TCVN 8218, 2009. Hydraulic concrete – Technical requirements. Vietnamese code.
   Google Scholar
• Teimortashlu, E., Dehestani, M., and Jalal, M., 2018. Application of Taguchi technique for compressive strength optimization of tertiary blended self-compacting mortar. Construction and Building Materials, 190, 1182–1191. doi:10.1016/j.conbuildmat.2018.09.165.
   Web of Science ®Google Scholar
• Tread, U., and Buyukozturk, O., 1998. Size effect and influence of aggregate roughness in interface fracture of concrete composites. ACI Materials Journal, 95 (4), 331–338.
   Web of Science ®Google Scholar
• Tsakiri, K., Marsellos, A., and Kapetanakis, S., 2018. Artificial neural network and multiple linear regression for flood prediction in mohawk river, New York. Water, 10 (9), 1158, 1–20. doi:10.3390/w10091158.
   Web of Science ®Google Scholar
• Yamei, H., and Lihua, W., 2017. Effect of particle shape of limestone manufactured sand and natural sand on concrete. Procedia Engineering, 210, 87–92. doi:10.1016/j.proeng.2017.11.052.
   Google Scholar
• Yeh, I.C., 2007. Computer-aided design for optimum concrete mixtures. Cement and Concrete Composites, 29 (3), 193–202. doi:10.1016/j.cemconcomp.2006.11.001.
   Web of Science ®Google Scholar
• Yeh, I.C., 2009. Optimization of concrete mix proportioning using a flattened simplex-centroid mixture design and neural networks. Engineering with Computers, 25 (2), 179–190.
   Web of Science ®Google Scholar
• Zain, M.F.M., and Abd, S.M., 2009. Multiple regression model for compressive strength prediction of high performance concrete. Journal Applied Sciences, 9 (1), 155–160.
   Google Scholar
• Zhou, M.K., Liu, Z.A., and Chen, X., 2016. Frost durability and strength of concrete prepared with crushed sand of different characteristics. Advances in Materials Science and Engineering, 2016, 2580542, 1–9. doi:10.1155/2016/2580542.
   Web of Science ®Google Scholar