Bulletin of the Chinese Ceramic Society, Volume. 44, Issue 4, 1398(2025)
Analysis of Factors Influencing Compressive Strength and Elastic Modulus of Alkali-Activated Slag-Fly Ash Concrete Based on Machine Learning
[3] [3] LI Y, SHEN J L, LIN H, et al. Optimization design for alkali-activated slag-fly ash geopolymer concrete based on artificial intelligence considering compressive strength, cost, and carbon emission[J]. Journal of Building Engineering, 2023, 75: 106929.
[4] [4] ADESANYA E, ALADEJARE A, ADEDIRAN A, et al. Predicting shrinkage of alkali-activated blast furnace-fly ash mortars using artificial neural network (ANN)[J]. Cement and Concrete Composites, 2021, 124: 104265.
[5] [5] HUANG Y M, HUO Z H, MA G W, et al. Multi-objective optimization of fly ash-slag based geopolymer considering strength, cost and CO2 emission: a new framework based on tree-based ensemble models and NSGA-II[J]. Journal of Building Engineering, 2023, 68: 106070.
[6] [6] LIU L, WANG X C, CHEN H S, et al. Numerical modeling of drying shrinkage deformation of cement-based composites by coupling multiscale structure model with 3D lattice analyses[J]. Computers & Structures, 2017, 178: 88-104.
[7] [7] ZHU Z G, CHEN H S, XU W X, et al. Parking simulation of three-dimensional multi-sized star-shaped particles[J]. Modelling Simul Mater Sci Eng, 2014, 22(3): 035008.
[8] [8] THUNUGUNTLA C S, GUNNESWARA RAO T D. Mix design procedure for alkali-activated slag concrete using particle packing theory[J]. Journal of Materials in Civil Engineering, 2018, 30(6): 04018113.
[11] [11] BHAT R, HAN T H, AKSHAY PONDURU S, et al. Predicting compressive strength of alkali-activated systems based on the network topology and phase assemblages using tree-structure computing algorithms[J]. Construction and Building Materials, 2022, 336: 127557.
[12] [12] NAZAR S, YANG J, WANG X E, et al. Estimation of strength, rheological parameters, and impact of raw constituents of alkali-activated mortar using machine learning and SHapely Additive exPlanations (SHAP)[J]. Construction and Building Materials, 2023, 377: 131014.
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LIU Lin, SHAO Xin, PANG Kun, ZHENG Hongchen. Analysis of Factors Influencing Compressive Strength and Elastic Modulus of Alkali-Activated Slag-Fly Ash Concrete Based on Machine Learning[J]. Bulletin of the Chinese Ceramic Society, 2025, 44(4): 1398
Received: Dec. 27, 2024
Accepted: May. 26, 2025
Published Online: May. 26, 2025
The Author Email: LIU Lin (liulin@hhu.edu.cn)