Journal of the Chinese Ceramic Society, Volume. 53, Issue 5, 1098(2025)
Early Shrinkage Performance and Neural Network Prediction Model of Ultra-High Performance Concrete
[1] [1] LIU K N, YIN T Y, FAN D Q, et al. Multiple effects of particle size distribution modulus (q) and maximum aggregate size (Dmax) on the characteristics of ultra-high performance concrete (UHPC): Experiments and modeling[J]. Cem Concr Compos, 2022, 133: 104709.
[2] [2] YU R, SPIESZ P, BROUWERS H J H. Mix design and properties assessment of ultra-high performance fibre reinforced concrete (UHPFRC)[J]. Cem Concr Res, 2014, 56: 29-39.
[3] [3] YANG K, LONG G C, TANG Z, et al. Enhancement in strength and toughness of ultra-high performance concrete (UHPC) from micron- and nano-scale[J]. J Build Eng, 2023, 69: 106308.
[4] [4] RAVICHANDRAN D, PREM P R, KALIYAVARADHAN S K, et al. Influence of fibers on fresh and hardened properties of ultra high performance concrete (UHPC)—A review[J]. J Build Eng, 2022, 57: 104922.
[11] [11] LI Y Q, SHEN J L, LI Y, et al. The data-driven research on the autogenous shrinkage of ultra-high performance concrete (UHPC) based on machine learning[J]. J Build Eng, 2024, 82: 108373.
[12] [12] MOHTASHAM MOEIN M, SARADAR A, RAHMATI K, et al. Predictive models for concrete properties using machine learning and deep learning approaches: A review[J]. J Build Eng, 2023, 63: 105444.
[13] [13] WANG J Q, LIU H, SUN J B, et al. Research on concrete early shrinkage characteristics based on machine learning algorithms for multi-objective optimization[J]. J Build Eng, 2024, 89: 109415.
[14] [14] SHEN J L, LI Y, LIN H, et al. Development of autogenous shrinkage prediction model of alkali-activated slag-fly ash geopolymer based on machine learning[J]. J Build Eng, 2023, 71: 106538.
[15] [15] HILLOULIN B, TRAN V Q. Interpretable machine learning model for autogenous shrinkage prediction of low-carbon cementitious materials[J]. Constr Build Mater, 2023, 396: 132343.
[23] [23] LI F P, CHEN D F, YANG Z M, et al. Effect of mixed fibers on fly ash-based geopolymer resistance against carbonation[J]. Constr Build Mater, 2022, 322: 126394.
[26] [26] LIU Q F, HU Z, WANG X E, et al. Numerical study on cracking and its effect on chloride transport in concrete subjected to external load[J]. Constr Build Mater, 2022, 325: 126797.
[30] [30] WU Y Q, GAO R L, YANG J Z. Prediction of coal and gas outburst: A method based on the BP neural network optimized by GASA[J]. Process Saf Environ Prot, 2020, 133: 64-72.
[31] [31] MIRJALILI S, LEWIS A. The whale optimization algorithm[J]. Adv Eng Softw, 2016, 95: 51-67.
[32] [32] XI B, HUANG Z W, AL-OBAIDI S, et al. Predicting ultra high-performance concrete self-healing performance using hybrid models based on metaheuristic optimization techniques[J]. Constr Build Mater, 2023, 381: 131261.
[33] [33] JIANG J Q, XU G B, WANG H Z, et al. High-accuracy road surface condition detection through multi-sensor information fusion based on WOA-BP neural network[J]. Sens Actuat A Phys, 2024, 378: 115829.
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JIANG Zhipeng, GAO Chang, TANG Jinhui, HU Zhangli, WANG Lei. Early Shrinkage Performance and Neural Network Prediction Model of Ultra-High Performance Concrete[J]. Journal of the Chinese Ceramic Society, 2025, 53(5): 1098
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Received: Aug. 26, 2024
Accepted: May. 29, 2025
Published Online: May. 29, 2025
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