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Research Article

Intelligent optimisation of an ultra-high-performance concrete (UHPC) multi-objective mixture ratio based on particle swarm optimisation

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Article: 2130919 | Received 05 Jul 2022, Accepted 26 Sep 2022, Published online: 14 Oct 2022

References

  • Akkurt, S., et al., 2003. The use of GA-ANNs in the modelling of compressive strength of cement mortar. Cement and Concrete Research, 33 (7), 973–979.
  • Arora, A., et al., 2019. Material design of economical ultra-high performance concrete (UHPC) and evaluation of their properties. Cement and Concrete Composites. https://doi.org/10.1016/j.cemconcomp.2019.103346
  • Behnood, A., and Golafshani, E.M, 2018. Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves. Journal of Cleaner Production, 202, 54–64.
  • Brouwers, H., and Radix, H, 2005. Self-compacting concrete: theoretical and experimental study. Cement and Concrete Research, 35 (11), 2116–2136.
  • Chang, W., and Zheng, W, 2020. Effects of key parameters on fluidity and compressive strength of ultra-high performance concrete. Structural Concrete, 21 (2), 747–760.
  • Chen, W., et al., 2020. Effects of cenosphere on the mechanical properties of cement-based composites. Construction and Building Materials, 261. https://doi.org/10.1016/j.conbuildmat.2020.120527
  • Chen, S., and Liu, X, 2019. An investigation of PSO algorithm-based back analysis on the three-dimensional seepage characteristics of an earth Dam. Indian Geotechnical Journal, 49 (2), 1–9.
  • Deng, F., et al., 2018. Compressive strength prediction of recycled concrete based on deep learning. Construction and Building Materials, 175, 562–569.
  • Eberhart, R., and Kennedy, J, 1995. A new optimizer using particle swarm theory. Proc 6th Int Symposium on Micro Machine and Human Science. Nagoya, 39–43.
  • Fan, D., et al., 2020a. A new design approach of steel fibre reinforced ultra-high performance concrete composites: experiments and modeling. Cement and Concrete Composites. https://doi.org/10.1016/j.cemconcomp.2020.103597
  • Fan, D., et al., 2020b. A novel approach for developing a green ultra-high performance concrete (UHPC) with advanced particles packing meso-structure. Construction and Building Materials, 265. https://doi.org/10.1016/j.conbuildmat.2020.120339
  • Fan, D., et al., 2021. Precise design and characteristics prediction of ultra-high performance concrete (UHPC) based on artificial intelligence techniques. Cement and Concrete Composites, 122. https://doi.org/10.1016/J.CEMCONCOMP.2021.104171
  • Gupta, H, 2018. Development and property assessment of high performance hybrid fiber reinforced concrete.
  • Ghafari, E., et al., 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), 1–11.
  • Gao, Y., Sun, S., and Chen, K, 2014. Application of micro bead fly ash in ultra high performance concrete. Building Technology, 45 (1), 26–29. (In Chinese).
  • Hammoudi, A., et al., 2019. Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates. Construction and Building Materials, 209, 425–436.
  • He, J., et al., 2022. Effect of silica fume on the rheological properties of cement paste with ultra-low water binder ratio. Materials, 15 (2), 554–554.
  • Hu, X., and Eberhart, R., 2002. Solving constrained nonlinear optimization problems with particle swarm optimization. 6th World Multiconference on Systemics, Cybernetics and Informatics, Orlando, USA.
  • Kennedy, J., and Eberhart, R, 1995. Particle swarm optimization. Proc IEEE Int Conf on Neural Networks. Perth, 1942–1948.
  • Kumar, S., and Rai, B, 2022. Synergetic effect of fly ash and silica fume on the performance of high volume fly ash self-compacting concrete. Journal of Structural Integrity and Maintenance, 7 (1), 61–74.
  • Liu, X., et al., 2015. Precise simulation analysis of the thermal field in mass concrete with a pipe water cooling system. Applied Thermal Engineering, 78, 449–459.
  • Li, L.G., and Kwan, A.K.H, 2014. Packing density of concrete mix under dry and wet conditions. Powder Technology, 253, 514–521.
  • Li, L., and Kwan, A, 2015. Effects of superplasticizer type on packing density, water film thickness and flowability of cementitious paste. Construction and Building Materials, 86, 113–119.
  • Lu, Z., et al., 2020. Analysis on influencing factors of workability and strength of ultra-high performance concrete. Material Guide, 34 (S01), 203–208. (In Chinese).
  • Lu, X., and Wang, H, 2021. Research on the current situation and development strategy of genetic algorithm and particle swarm optimization algorithm. Wireless Internet Technology, 18 (21), 108–109. (In Chinese).
  • Majhi, R., Padhy, A., and Nayak, A, 2021. Performance of structural lightweight concrete produced by utilizing high volume of fly ash cenosphere and sintered fly ash aggregate with silica fume. Cleaner Engineering and Technology, 3. https://doi.org/10.1016/j.clet.2021.100121
  • Meng, W., Valipour, M., and Khayat, K.H, 2017. Optimization and performance of costeffective ultra-high performance concrete. Materials and Structures, 50 (1). https://doi.org/10.1617/s11527-016-0896-3
  • Patil, S., et al., 2022. Durability and micro-structure studies on fly ash and silica fume based composite fiber reinforced high-performance concrete. Materials Today: Proceedings, 49, 1511–1520.
  • Parsopoulos, K., and Vrahatis, M, 2002. Particle swarm optimization method for constrained optimization problems. Intelligent Technologies Theory and Applications: New Trends in Intelligent Technologies. CiteSeer.
  • Qu, D., Cai, X., and Chang, W, 2018. Evaluating the effects of steel fibers on mechanical properties of ultra-high performance concrete using artificial neural networks. Applied Sciences, 8 (7), 1–21.
  • Sadrossadat, E., et al., 2021. Multi-objective mixture design and optimisation of steel fiber reinforced UHPC using machine learning algorithms and metaheuristics. Engineering with Computers, 38, 2569–2582.
  • Shi, C., et al., 2015. A review on ultra-high performance concrete: part I. Raw materials and mixture design. Construction and Building Materials, 101, 741–751.
  • Snehal, K., and Das, B.B, 2021. Application of Andreassen and modified Andreassen model on cementitious mixture design: a review. Rec. Develop. Sustain. Infrastruct, 729–750.
  • Sobhani, J., et al., 2010. Prediction of the compressive strength of no-slump concrete: a comparative study of regression, neural network and ANFIS models. Construction and Building Materials, 24 (5), 709–718.
  • Slonski, M, 2010. A comparison of model selection methods for compressive strength prediction of high-performance concrete using neural networks. Computers and Structures, 88 (21–22), 1248–1253.
  • Suleiman, A.R., and Nehdi, M, 2017. Modeling self-healing of concrete using hybrid genetic algorithm–artificial neural network. Materials, 10 (2), 135. https://doi.org/10.3390/ma10020135
  • Wang, F., et al., 2019c. Research on inversion of thermal parameters of concrete dam based on improved particle swarm optimization algorithm. Vibration and Shock, 38 (12), 168–174. (In Chinese).
  • Wang, Q.H., et al., 2022. Effects of silica fume on the abrasion resistance of low-heat Portland cement concrete. Construction and Building Materials, 329. https://doi.org/10.1016/J.CONBUILDMAT.2022.127165
  • Wang, F., et al., 2021. Inverse analysis of thermal parameters of different materials of ultra-high arch dam based on hybrid particle swarm optimization algorithm. Journal of Tsinghua University, 61 (07), 747–755. (In Chinese).
  • Wang, X., et al., 2019a. Optimized design of ultra-high performance concrete (UHPC) with a high wet packing density. Cement and Concrete Research, 126. https://doi.org/10.1016/j.cemconres.2019.105921
  • Wang, X., et al., 2019b. Optimized treatment of recycled construction and demolition waste in developing sustainable ultra-high performance concrete. Journal of Cleaner Production, 221, 805–816.
  • Wang, X., et al., 2018. Development of a novel cleaner construction product: ultra-high performance concrete incorporating lead-zinc tailings. Journal of Cleaner Production, 196, 172–182.
  • Wang, X., et al., 2017. Mix design and characteristics evaluation of an ecofriendly ultra-high performance concrete incorporating recycled coral based materials. Journal of Cleaner Production, 165, 70–80.
  • Wu, Q., Zheng, J., and Song, W, 2007. Improved particle swarm optimization algorithm for solving nonlinear constrained optimization problems. Computer Engineering and Application, 43 (24), 61–64. (In Chinese).
  • Wong, H., and Kwan, A, 2008. Packing density of cementitious materials: part 1–measurement using a wet packing method. Materials and Structures, 41 (4), 689–701.
  • Xiong, F., and Guo, C, 2016. Deformation prediction of concrete gravity dam based on particle swarm optimization algorithm. Scientific and Technological Economic Market, 12, 34–36. (In Chinese).
  • Yang, R., et al., 2019. The physical and chemical impact of manufactured sand as a partial replacement material in ultra-high performance concrete (UHPC). Cement and Concrete Composites, 99, 203–213.
  • Yang, T., Hu, L., and Guo, L, 2010. Inversion of characteristic parameters of concrete humidity field based on particle swarm optimization. Hydropower Energy Science, 28 (03), 115–117. (In Chinese).
  • Yu, R., Spiesz, P., and Brouwers, H.J.H, 2015a. Development of an eco-friendly ultra-high performance concrete (UHPC) with efficient cement and mineral admixtures uses. Cement and Concrete Composites, 55, 383–394.
  • Yu, R., Spiesz, P., and Brouwers, H.J.H, 2015b. Development of ultra-high performance fibre reinforced concrete (UHPC): towards an efficient utilization of binders and fibres. Construction and Building Materials, 79, 273–282.
  • Yu, R., Spiesz, P., and Brouwers, H.J.H, 2014. Mix design and properties assessment of ultrahigh performance fibre reinforced concrete (UHPFRC). Cement and Concrete Research, 56, 29–39.
  • Yu, R., Spiesz, P., and Brouwers, H, 2016. Energy absorption capacity of a sustainable ultra-high performance fibre reinforced concrete (UHPFRC) in quasi-static mode and under high velocity projectile impact. Cement and Concrete Composites, 68, 109–122.
  • Yuan, Z., Wang, L.N., and Ji, X, 2014a. Prediction of concrete compressive strength: research on hybrid models genetic based algorithms and ANFIS. Advances in Engineering Software, 67, 156–163.
  • Yuan, Q., et al., 2014b. Application of micro bead fly ash in C50 and C55 commercial concrete. Concrete and Cement Products, 7, 21–24. (In Chinese).

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