Prediction on the freeze-thaw resistance of a one-part geopolymer stabilized soil by using deep learning method
Source abstract
Abstract
The mechanical properties of soil located at cold areas may be deteriorated under freeze-thaw cycle condition. One-part geopolymer (OPG) is a kind of alkaline-activated material by using industrial by-products and solid alkali. Obviously, OPG can replace ordinary portland cement (OPC) as a soil stabilizer in ground improvement, which presents environmental and low-carbon benefits. The assessment of unconfined compressive strength (UCS) is vital for evaluating OPG-stabilized soil durability under freeze-thaw conditions, typically demanding extensive resources. Leveraging artificial intelligence, a predictive model can be developed for this purpose. This study collected a small sample size of 216 data points of the OPG-stabilized soil's freeze-thaw behaviour. Three deep learning (DL) models, Backpropagation Neural Network [BPNN], Convolutional Neural Network [CNN], Gated Recurrent Unit [GRU], were trained on the small dataset to predict freeze-thaw performance efficiently, offering a promising approach to streamline assessment processes. In the DL models, the ratio of fly ash (FA) and ground granulated blast furnace slag (GGBFS), freezing temperature and freeze-thaw cycle were taken as the input variables, and the target output was the UCS of the OPG-stabilized soil. Among all the models, the CNN achieved the highest prediction accuracy with R2 of 0.9966, and followed by the BPNN (R2=0.9893) and the GRU (R2=0.9872). After that, the interpretable machine learning methods (i.e., Shapley Additive Explanation [SHAP] and Partial Dependence Plot [PDP]) were utilized for the developed CNN model to further understand the impact of input variables on the outcome predictions. In addition, the morphological analysis was used to verify the freeze-thaw mechanism of the OPG-stabilized soil derived from the interpretable CNN model. It is revealed that the inclusion of FA in the OPG crucially enhanced the freeze-thaw resistance of the OPG-stabilized soil. However, beyond a certain threshold, the addition of FA negatively impacted the freeze-thaw resistance of OPG-stabilized soil. Freezing temperature was pinpointed as the key factor affecting the properties of the stabilized soil.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Concrete and Cement Materials Research · Soil and Unsaturated Flow · Rock Mechanics and Modeling
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (64)
Show source-provided references
1. A comprehensive overview of geopolymer composites: a bibliometric analysis and literature review
2. Machine learning for high-fidelity prediction of cement hydration kinetics in blended systems10.1016/j.matdes.2021.109920 ↗
3. Toward waste glass upcycling: Preparation and characterization of high-volume waste glass geopolymer composites
4. Conventional and ensemble machine learning models to predict the compressive strength of fly ash based geopolymer concrete
5. Incorporation of clay brick wastes and calcium hydroxide into geopolymers: compressive strength, microstructure, and efflorescence
6. W. L. David, Machine learning approaches to predict compressive strength of fly ash-based geopolymer concrete: a comprehensive review10.1016/j.conbuildmat.2024.135519 ↗
7. Ternary cementless composite based on red mud, ultra-fine fly ash, and GGBS: synergistic utilization and geopolymerization mechanism
8. Alkali-activated materials made of construction and demolition waste as precursors: a review
9. Enhancing sustainability in pavement Engineering: a-state-of-the-art review of cement asphalt emulsion mixtures
10. Experimental feasibility study of geopolymer as the next-generation soil stabilizer10.1016/j.conbuildmat.2013.06.017 ↗
11. Deep soft soil improvement by alkaline activation10.1680/grim.900032 ↗
12. Advances in geopolymer materials: a comprehensive review
13. Biomass ash waste from agricultural residues: characterisation, reactivity and potential to develop one-part geopolymer cement10.1016/j.conbuildmat.2024.136544 ↗
14. A laboratory investigation of steel to fly ash-based geopolymer paste bonding behavior after exposure to elevated temperatures10.1016/j.conbuildmat.2020.119267 ↗
15. Advancing carbon nanomaterials-engineered self-sensing cement composites for structural health monitoring: a state-of-the-art review
16. Strength development in soft marine clay stabilized by fly ash and calcium carbide residue based geopolymer10.1016/j.clay.2016.04.005 ↗
17. Consolidation behavior of fly ash-based geopolymer-stabilized dredged mud10.1061/(ASCE)WW.1943-5460.0000455 ↗
18. Early strength development of soft clay stabilized by one-part ground granulated blast furnace slag and fly ash-based geopolymer10.3389/fmats.2021.616430 ↗
19. Effects of fly ash content on the strength development of soft clay stabilized by one-part geopolymer under curing stress10.1061/(ASCE)MT.1943-5533.0003887 ↗
20. Microwave absorption function on a novel one-part binary geopolymer: influence of frequency, ageing and mix design10.1016/j.conbuildmat.2024.136264 ↗
21. On the use of one-part geopolymer activated by solid sodium silicate in soft clay stabilization10.1016/j.conbuildmat.2023.132957 ↗
22. A state-of-the-art review of crushed urban waste glass used in OPC and AAMs (geopolymer): progress and challenges
23. Inverting the rock mass P-wave velocity field ahead of deep buried tunnel face while borehole drilling
24. Systematic studies on behaviors of ultra-high performance concrete subject to freezing and thawing cycles combining DIC technology10.1016/j.conbuildmat.2023.131580 ↗
25. Freeze-thaw resistance of Ultra-High performance concrete: dependence on concrete composition10.1016/j.conbuildmat.2021.123523 ↗
26. Workability, physical & mechanical properties of the cement mortars strengthened with metakaolin and steel/basalt fibers exposed to freezing-thawing periods10.1016/j.conbuildmat.2023.132100 ↗
27. Mesoscopic damage characteristics of hydrophobicity-modified geopolymer composites under freezing-thawing cycles based on CT scanning10.1016/j.compstruct.2023.117637 ↗
28. Prediction of compressive strength of glass powder concrete based on artificial intelligence
29. Chemistry-informed machine learning prediction of compressive strength for alkali-activated materials10.1016/j.conbuildmat.2021.126103 ↗
30. Prediction on compressive strength of recycled aggregate self-compacting concrete by machine learning method
31. Compressive strength analysis of fly ash-based geopolymer concrete using machine learning approaches
32. A review on material mix proportion and strength influence parameters of geopolymer concrete: application of ANN model for GPC strength prediction10.1016/j.conbuildmat.2022.129253 ↗
33. A machine learning-assisted numerical predictor for compressive strength of geopolymer concrete based on experimental data and sensitivity analysis10.3390/app10217726 ↗
34. Hybrid machine learning approach to prediction of the compressive and flexural strengths of UHPC and parametric analysis with shapley additive explanations
35. Predictive modelling of compressive strength of fly ash and ground granulated blast furnace slag based geopolymer concrete using machine learning techniques
36. Compressive strength prediction of high-strength concrete using long short-term memory and machine learning algorithms10.3390/buildings12030302 ↗
37. Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment10.1007/s11356-021-12877-y ↗
38. Sensitivity and robustness analysis of adaptive neuro-fuzzy inference system (ANFIS) for shear strength prediction of stud connectors in concrete
39. Machine learning interpretable-prediction models to evaluate the slump and strength of fly ash-based geopolymer10.1016/j.jmrt.2023.02.180 ↗
40. Compressive strength prediction of recycled concrete based on deep learning10.1016/j.conbuildmat.2018.04.169 ↗
41. Analyzing the mechanical performance of fly ash-based geopolymer concrete with different machine learning techniques10.1016/j.conbuildmat.2021.125785 ↗
42. A novel framework for strength prediction of geopolymer mortar: renovative precursor effect
43. Learning representations by back-propagating errors10.1038/323533a0 ↗
44. Landslide susceptibility prediction using particle-swarm-optimized multilayer perceptron: comparisons with multilayer-perceptron-only, BP neural network, and information value models10.3390/app9183664 ↗
45. BPNN-based optimal strategy for dynamic energy optimization with providing proper thermal comfort under the different outdoor air temperatures10.1016/j.apenergy.2022.118899 ↗
46. Using multifactor inputs BP neural network to make power consumption prediction
47. Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit10.1038/35016072 ↗
48. D. Kingma, J. Ba, Adam: A Method for Stochastic Optimization, arXiv: Learning. (2014).
49. Improving neural networks by preventing co-adaptation of feature detectors
50. Gradient-based learning applied to document recognition10.1109/5.726791 ↗
51. Phoneme recognition using time-delay neural networks10.1109/29.21701 ↗
52. 1D Convolutional neural networks and applications: a survey10.1016/j.ymssp.2020.107398 ↗
53. Deep learning10.1038/nature14539 ↗
54. LSTM: a search space odyssey10.1109/TNNLS.2016.2582924 ↗
55. Long short-term memory10.1162/neco.1997.9.8.1735 ↗
56. Learning phrase representations using RNN encoder-decoder for statistical machine translation
57. Machine learning in concrete science: applications, challenges, and best practices10.1038/s41524-022-00810-x ↗
58. C. Molnar, G. Casalicchio, B. Bischl, Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges, 2020, 417-431.10.1007/978-3-030-65965-3_28 ↗
59. A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP)
60. C. Molnar, Interpretable Machine Learning: A Guide For Making Black Box Models Explainable. 2 ed. 2022.
61. Kriegler · 2007 · 144
62. T.J. Hastie, R. Tibshirani, J.H. Friedman, The Elements of Statistical Learning. 2001.10.1007/978-0-387-21606-5 ↗
63. Stiffness and strength development of the soft clay stabilized by the one-part geopolymer under one-dimensional compressive loading10.1016/j.sandf.2021.06.001 ↗
64. ASTM C666/C666M-15, Standard test method for resistance of concrete to rapid freezing and thawing, ASTM International (2015).