Study on the design method of multi-component industrial solid waste low carbon cementitious material with cement as the activator
Source abstract
Abstract
The relationship between microstructure and mechanical properties of multi-component solid waste low-carbon cementitious materials has been widely pay attention to. However, industrial solid waste is a complex multi-component system with many variable factors, which makes it difficult to design the formulation of cementitious materials. This paper pioneered the application of machine learning (ML) models, algorithms and error rates to analyze the compressive and flexural strength of fly ash-based pastes. Coefficient of determination (R2), mean squared error (MSE), root mean square error (RMSE), mean absolute error (MAE) and a20-index were used to evaluate robustness. X-ray diffraction (XRD), scanning electron microscope (SEM) and Brunauer-Emmett-Taylor (BET) were carried out to analyze evolution of cementitious materials. The evaluation results of ML models exhibited that the Gradient boosting regression (GBR) model had the best determination parameters and a steep normal distribution fitting curve with an a20-index of 0.861. GBR model exhibited the best robustness. The key factors of fly ash-based cementitious materials were identified by Pearson's coefficient, which was benefit to determine the formulation of multi-component solid waste low-carbon cementitious materials. Furthermore, experiments also demonstrated that the optimum ratio of multi-component solid waste low carbon cementitious material was 10 % gypsum, 10 % metakaolin, 45 % fly ash, 15 % slag and 20 % cement, respectively. It was worth noting that the compressive strength of this kind of multi-component solid waste low-carbon cementitious materials reached 35 MPa, which was superior to the mechanical properties of P·O 32.5 cement. The results of phase, SEM images and pore structure distribution showed that the synergistic effect of the multi-component solid waste materials effectively filled the material voids and also facilitated the formation of a variety of gelatinous materials through gelling reactions in the late stage (14–28 d). This work will promote the resource utilization of industrial solid waste, contribute to carbon reduction, and can accelerate the green revolution of concrete.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Concrete and Cement Materials Research · Innovative concrete reinforcement materials · Magnesium Oxide Properties and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (84)
Show source-provided references
1. Mitigation of ASR expansion in concrete using ultra-fine coal bottom ash10.1016/j.conbuildmat.2019.01.013 ↗
2. Utilization of mill tailings, fly ash and slag as mine paste backfill material: review and future perspective10.1016/j.conbuildmat.2021.125120 ↗
3. Effects of non-ground slag and bottom ash as fine aggregate on concrete permeability properties10.1016/j.conbuildmat.2011.06.080 ↗
4. Use of power plant bottom ash as fine and coarse aggregates in high-strength concrete10.1016/j.conbuildmat.2010.06.065 ↗
5. Durability of concrete incorporating non-ground blast furnace slag and bottom ash as fine aggregate10.1016/j.buildenv.2006.07.003 ↗
6. Utilization of gypsum from construction and demolition waste in Portland cement mortar10.1590/0366-6913201965s12588 ↗
7. Effect of fly ash and metakaolin on pervious concrete properties10.1016/j.conbuildmat.2019.06.185 ↗
8. H. Wang, R. Wu, Research on desulfurization gypsum-activated aluminum mineral material-cement composite cementitious system. Comprehensive utilization of fly ash. no. 03, 2016, pp. 36–39+44 .
9. Estimation of bond strength between UHPC and reinforcing bars using machine learning approaches10.1016/j.engstruct.2022.114311 ↗
10. Prediction of mechanical and durability characteristics of concrete including slag and recycled aggregate concrete with artificial neural networks (ANNs)10.1016/j.conbuildmat.2022.126839 ↗
11. Evaluating the mechanical strength prediction performances of fly ash-based MPC mortar with artificial intelligence approaches10.1016/j.jclepro.2022.131815 ↗
12. Interpretable ensemble-machine-learning models for predicting creep behavior of concrete10.1016/j.cemconcomp.2021.104295 ↗
13. Machine learning prediction of structural response of steel fiber-reinforced concrete beams subjected to far-field blast loading10.1016/j.cemconcomp.2021.104378 ↗
14. Latest concrete materials dataset and ensemble prediction model for concrete compressive strength containing RCA and GGBFS materials10.1016/j.conbuildmat.2022.126525 ↗
15. Predictive models for concrete properties using machine learning and deep learning approaches: a review
16. Hybrid MARS-, MEP-, and ANN-based prediction for modeling the compressive strength of cement mortar with various sand size and clay mineral metakaolin content10.1007/s43452-022-00519-0 ↗
17. Feed-Forward neural network prediction of the mechanical properties of sandcrete materials
18. Self-compacting concrete strength prediction using surrogate models10.1007/s00521-017-3007-7 ↗
19. Analyzing the mechanical performance of fly ash-based geopolymer concrete with different machine learning techniques10.1016/j.conbuildmat.2021.125785 ↗
20. Mathematical modeling techniques to predict the compressive strength of high-strength concrete incorporated metakaolin with multiple mix proportions
21. Predicting the compressive strength of steelmaking slag concrete with machine learning – considerations on developing a mix design tool10.1016/j.conbuildmat.2022.127896 ↗
22. Multi-objective optimization of concrete mixture proportions using machine learning and metaheuristic algorithms10.1016/j.conbuildmat.2020.119208 ↗
23. Automating the mixture design of lightweight foamed concrete using multi-objective firefly algorithm and support vector regression10.1016/j.cemconcomp.2021.104103 ↗
24. Interpreting the experimental results of compressive strength of hand-mixed cement-grouted sands using various mathematical approaches10.1007/s43452-021-00341-0 ↗
25. Multi-objective mixture design and optimisation of steel fiber reinforced UHPC using machine learning algorithms and metaheuristics10.1007/s00366-021-01403-w ↗
26. Mixture optimization of mechanical, economical, and environmental objectives for sustainable recycled aggregate concrete based on machine learning and metaheuristic algorithms
27. Knowledge discovery of concrete material using genetic operation trees10.1016/j.eswa.2008.07.004 ↗
28. Heuristic algorithm-based semi-empirical formulas for estimating the compressive strength of the normal and high performance concrete10.1016/j.conbuildmat.2021.124467 ↗
29. Prediction of concrete and FRC properties at high temperature using machine and deep learning: a review of recent advances and future
30. Intelligence decision mechanism for prediction of compressive strength of self-compaction green concrete via neural network10.1016/j.jclepro.2022.130580 ↗
31. Metamodel techniques to estimate the compressive strength of UHPFRC using various mix proportions and a high range of curing temperatures10.1016/j.conbuildmat.2022.128737 ↗
32. Prediction of concrete materials compressive strength using surrogate models10.1016/j.istruc.2022.11.002 ↗
33. Microscopic experimental study on fly ash-desulfurization gypsum-cementitious composite cementitious materials
34. Modification of high-volume fly ash cement with metakaolin for its utilization in cemented paste backfill: the effects of metakaolin content and particle size10.1016/j.powtec.2021.07.067 ↗
35. Damage prediction for regular reinforced concrete buildings using the decision tree algorithm10.1016/j.compstruc.2013.10.006 ↗
36. Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction10.1016/j.engappai.2013.03.014 ↗
37. Machine learning prediction of mechanical properties of concrete: critical review10.1016/j.conbuildmat.2020.119889 ↗
38. T. Hastie, R. Tibshirani, J.H. Friedman, J.H. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2009.10.1007/978-0-387-84858-7 ↗
39. J. Friedman, Greedy function approximation: a gradient boosting machine, 2001, pp. 1189–232.10.1214/aos/1013203451 ↗
40. Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2, Morgan Kaufmann Publishers Inc., Stockholm, Sweden, 1999.
41. Han · 2022
42. Breiman · 1984
43. Optimization of radioactive particle tracking methodology in a single-phase flow using MCNP6 code and artificial intelligence methods
44. Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption10.1016/j.enpol.2007.04.020 ↗
45. Shear resistance prediction of concrete beams reinforced by FRP bars using artificial neural networks10.1016/j.measurement.2018.05.051 ↗
46. S. Zhou, Experimental study on the effect of large dosage of fly ash on mortar and concrete properties, 2020.
47. S. Zhou, Research on isostrength mechanism and early cracking resistance of large-dosed fly ash mortar, 2019.
48. Effect of fly ash admixture on early age mechanical properties of hydraulic concrete
49. W. Wang, Experimental study on the activity excitation of fly ash and large dosage of fly ash concrete, 2005.
50. H. Wang, Study on cement replacement with large dosage of fly ash and its activity excitation, 2012.
51. J. Huang, Study on the mechanical properties and hydration process of heavily doped fly ash-cementitious materials, 2020.
52. Experimental study on the effect of the dosage of fly ash on the performance of dry mortar
53. Effects of the physicochemical properties of fly ash on the compressive strength of high-volume fly ash mortar10.1016/j.conbuildmat.2016.08.148 ↗
54. Laboratory-produced high-volume fly ash blended cements: compressive strength and resistance to the chloride-ion penetration of concrete10.1016/S0008-8846(00)00299-4 ↗
55. Compressive strength, flexural strength, and durability of high-volume fly ash concrete10.1007/s41062-023-01120-x ↗
56. Effects of Si/Al ratio on the efflorescence and properties of fly ash based geopolymer10.1016/j.jclepro.2019.118852 ↗
57. Phase evolution and mechanical performance of red mud-gypsum waste derived activator composite cementitious materials exposed to various Ca/Si and Al/S ratios10.1016/j.conbuildmat.2023.134807 ↗
58. Mapping and holistic design of natural hydraulic lime mortars10.1016/j.cemconres.2020.106167 ↗
59. Application of artificial neural networks for the prediction of interface mechanics: a study on grain boundary constitutive behavior10.1186/s40323-019-0138-7 ↗
60. Assessing dynamic conditions of the retaining wall: developing two hybrid intelligent models10.3390/app9061042 ↗
61. Prediction of cement-based mortars compressive strength using machine learning techniques10.1007/s00521-021-06004-8 ↗
62. Compressive strength of natural hydraulic lime mortars using soft computing techniques10.1016/j.prostr.2019.08.122 ↗
63. Concrete compressive strength using artificial neural networks10.1007/s00521-019-04663-2 ↗
64. H. Wang, Preparation and characterization of fly ash-based porous thermal insulation materials, 2016.
65. In situ monitoring of the hydration of calcium silicate minerals in cement with a remote fiber-optic Raman probe10.1016/j.cemconcomp.2023.105214 ↗
66. Fly ash volcanic ash reactivity and its reaction kinetics
67. T. Zhao, Research on the performance of large dosage of fly ash mortar, 2015.
68. Mechanism of the role of fly ash in the hydration process of composite cementitious materials
69. Use of coal chemical industry by-product coal gasification fine ash as supplementary cementitious materials in cement: chemical excitation, hydration and hardening characteristics10.1016/j.conbuildmat.2024.136147 ↗
70. Y. Zhou, Mechanical properties and microstructural characterization of concrete for Ultra-Deep well walls in coastal environments, 2021.
71. Q. Tang, Ageing of fly ash in cementitious composites and its mechanism of action, 2018.
72. Q. Yu, Molecular dynamics of moisture and ion adsorption and transport in cementitious materials, 2021.
73. Characterization of hydration products of slag under chemically unstimulated conditions
74. Mechanism of the effect of CFB slag instead of mechanism sand on the strength of concrete
75. Effect of calcium–silicon ratio on microstructure and nanostructure of calcium silicate hydrate synthesized by reaction of fumed silica and calcium oxide at room temperature
76. S. Wang, Research on hydration and application of fly ash-desulfurization gypsum double-mixed cementitious materials, 2012.
77. a. chemistry, Reporting physisorption data for gas/solid systems with special reference to the determination of surface area and porosity10.1351/pac198557040603 ↗
78. An embodied carbon and energy analysis of modern methods of construction in housing: a case study using a lifecycle assessment framework10.1016/j.enbuild.2010.09.005 ↗
79. Life cycle inventory of buildings: a calculation method10.1016/j.buildenv.2009.10.012 ↗
80. Spatial evolutionary characteristics and influencing factors of urban industrial carbon emission in china
81. Comparison and analysis of carbon emissions of traditional, prefabricated, and green material buildings in materialization stage10.1016/j.jclepro.2023.137152 ↗
82. Carbon emission factors identification and measurement model construction for railway construction projects
83. Carbon and air pollutant emissions forecast of China's cement industry from 2021 to 203510.1016/j.resconrec.2024.107498 ↗
84. A study on the carbon neutrality pathways of China's cement industry, Energy Found., 2023.