Exploring the interrelationships between composition, rheology, and compressive strength of self-compacting concrete: An exploration of explainable boosting algorithms
Source abstract
Abstract
This study introduces a novel methodology for enhancing the compressive strength of self-compacting concrete (SCC) via the use of the Explainable Boosting Machine (EBM), a sophisticated and interpretable machine learning algorithm. It presents a data-driven model that aims to accurately predict the strength of SCC by considering the intricate interactions among its various elements. Additionally, the model provides insights into the variables that influence SCC's compressive strength. By using EBM in conjunction with XGBoost and CatBoost algorithms, this study conducts a comparative examination of predictive abilities using datasets related to composition and rheology. The findings reveal that CatBoost has greater predictive performance using rheology dataset, as shown by an R2 value of 0.977. Conversely, XGBoost exhibits a higher predictive capability using the composition dataset, as indicated by an R2 value of 0.947. The EBM can provide comprehensive explanations at both global and local levels. It effectively identifies the key factors that have a significant influence on compressive strength. These factors include the coarse aggregate content, cement content, water content, viscosity, and V-funnel flow time. The study findings provide more evidence to support the notion that including rheological data into the model leads to a notable improvement in its accuracy. This outcome further confirms the existence of a direct correlation between rheological properties and compressive strength. The explanatory insights provided by EBM give practical instructions for customising SCC mixes to attain desired strengths. This facilitates quality control and enables personalised concrete design in the field of construction. This study highlights the potential of interpretable machine learning algorithms in improving the predictive modelling of SCC features. This advancement may lead to the development of more durable, efficient, and customised building materials.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Infrastructure Maintenance and Monitoring · Concrete and Cement Materials Research · Innovative concrete reinforcement materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (79)
Show source-provided references
1. Mechanical properties and durability characteristics of SCC incorporating crushed limestone powder
2. Durability of Self-Compacting Concrete
3. Properties of normal concrete, self-compacting concrete and glass fibre-reinforced self-compacting concrete: an experimental study10.1016/j.proeng.2016.12.106 ↗
4. Durability of normal strength self-compacting concretes and their impact on service life of reinforced concrete structures10.1016/j.conbuildmat.2012.12.042 ↗
5. Statistical models supporting the high-performance self-compacting concrete (HPSCC) design process for high strength10.3390/ma15020690 ↗
6. Assessment of high performance self-consolidating concrete through an experimental and analytical multi-parameter approach10.3390/ma14040985 ↗
7. Statistical approach for the design of structural self-compacting concrete with fine recycled concrete aggregate10.3390/math8122190 ↗
8. Cements in the 21st century: challenges, perspectives, and opportunities10.1111/jace.14948 ↗
9. Daczko · 2012
10. Nanotechnology innovations for the construction industry10.1016/j.pmatsci.2013.04.001 ↗
11. Self-compacting concrete10.3151/jact.1.5 ↗
12. Modelling the fresh properties of self-compacting concrete using support vector machine approach10.1016/j.conbuildmat.2015.12.035 ↗
13. Performance of self-compacting concrete containing different mineral admixtures10.1007/s12205-013-1882-8 ↗
14. Assessment of fresh properties and compressive strength of self-compacting concrete made with different sand types by mixture design modelling approach10.1016/j.conbuildmat.2013.08.039 ↗
15. Effect of water/cement ratio on the fresh and hardened properties of self-compacting concrete10.1016/j.buildenv.2006.01.012 ↗
16. RSM and ANN modelling of the mechanical properties of self-compacting concrete with silica fume and plastic waste as partial constituent replacement
17. Dataset on predictive compressive strength model for self-compacting concrete10.1016/j.dib.2018.02.008 ↗
18. Application of soft computing in predicting the compressive strength of self-compacted concrete containing recyclable aggregate
19. Admixed high-performance concrete property prediction by novel regression-based models
20. S. Elhishi, A.M. Elashry, and S. El-Metwally, Assessing Concrete Strength Using Machine Learning, presented at the 2023 International Conference on Artificial Intelligence Science and Applications in Industry and Society (CAISAIS), 2023/09/03, 2023. [Online]. Available: http://dx.doi.org/10.1109/caisais59399.2023.10270023.10.1109/CAISAIS59399.2023.10270023 ↗
21. Can domain knowledge benefit machine learning for concrete property prediction?10.1111/jace.19549 ↗
22. Machine learning in concrete strength simulations: multi-nation data analytics10.1016/j.conbuildmat.2014.09.054 ↗
23. Mechanical and durability performance of sustainable structural concretes: an experimental study10.1016/j.cemconcomp.2016.05.009 ↗
24. Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks10.1016/j.advengsoft.2011.05.016 ↗
25. Deep learning in neural networks: an overview10.1016/j.neunet.2014.09.003 ↗
26. Machine learning-based compressive strength prediction for concrete: an adaptive boosting approach10.1016/j.conbuildmat.2019.117000 ↗
27. Application of soft-computing methods to evaluate the compressive strength of self-compacting concrete10.3390/ma15217800 ↗
28. Strength and acid resistance of ceramic-based self-compacting alkali-activated concrete: optimizing and predicting assessment10.3390/ma14206208 ↗
29. Using experimental statistical models for predicting strength and deformability of self-compacting concrete with ground blast-furnace slag10.3390/ma15124110 ↗
30. Prediction models for the mechanical properties of self-compacting concrete with recycled rubber and silica fume10.3390/ma13081821 ↗
31. A study on the prediction of compressive strength of self-compacting recycled aggregate concrete utilizing novel computational approaches10.3390/ma15155232 ↗
32. A comparative study for the prediction of the compressive strength of self-compacting concrete modified with fly ash10.3390/ma14174934 ↗
33. Application of artificial intelligence methods for predicting the compressive strength of self-compacting concrete with class F fly ash10.3390/ma15124191 ↗
34. Prediction of compressive strength of self-compacting concrete using machine learning techniques10.18702/acf.2023.9.1.17 ↗
35. Estimating the compressive strength of self-compacting concrete with fiber using an extreme gradient boosting model10.58845/jstt.utt.2023.en.3.1.12-26 ↗
36. Machine learning interpretability: a survey on methods and metrics10.3390/electronics8080832 ↗
37. Conceptual challenges for interpretable machine learning10.1007/s11229-022-03485-5 ↗
38. Definitions, methods, and applications in interpretable machine learning10.1073/pnas.1900654116 ↗
39. Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data
40. Explainable AI: a review of machine learning interpretability methods10.3390/e23010018 ↗
41. Stability of feature selection algorithm: a review
42. A. Gramegna, P. Giudici, SHAP and LIME: An Evaluation of Discriminative Power in Credit Risk, (in English), Frontiers in Artificial Intelligence, Original Research vol. 4, 2021-September-17 2021, doi: 10.3389/frai.2021.752558.10.3389/frai.2021.752558 ↗
43. Explainable Artificial Intelligence (XAI): what we know and what is left to attain Trustworthy Artificial Intelligence10.1016/j.inffus.2023.101805 ↗
44. Explainable AI (XAI): core ideas, techniques, and solutions10.1145/3561048 ↗
45. Y. Lou, R. Caruana, J. Gehrke, and G. Hooker, Accurate intelligible models with pairwise interactions, 2013: ACM, doi: 10.1145/2487575.2487579. [Online]. Available: https://dx.doi.org/10.1145/2487575.2487579.10.1145/2487575.2487579 ↗
46. H. Nori, S. Jenkins, P. Koch, and R. Caruana, Interpretml: A unified framework for machine learning interpretability, arXiv preprint arXiv:1909.09223, 2019.
47. GAMI-Net: an explainable neural network based on generalized additive models with structured interactions10.1016/j.patcog.2021.108192 ↗
48. Quantification of multifactorial effects on particle distributions at urban neighborhood scale using machine learning and unmanned aerial vehicle measurement10.1016/j.jclepro.2022.134494 ↗
49. A.E. Maxwell, M. Sharma, and K.A. Donaldson, Explainable Boosting Machines for Slope Failure Spatial Predictive Modeling, Remote Sensing, vol. 13, no. 24, doi: 10.3390/rs13244991.10.3390/rs13244991 ↗
50. Credit default prediction of Chinese real estate listed companies based on explainable machine learning10.1016/j.frl.2023.104305 ↗
51. Explainable Boosting Machine Model with a Parallel Ensemble Design Predicts Local Failure for Head and Neck Cancer With Clinical, CT, and Delta CBCT Radiomic Features10.1016/j.ijrobp.2021.07.527 ↗
52. Explainable Boosting Machine for Predicting Alzheimer’s Disease from MRI Hippocampal Subfields
53. Explainable boosting machine for predicting wind shear-induced aircraft go-around based on pilot reports10.1007/s12205-023-0410-8 ↗
54. Assessing wind field characteristics along the airport runway glide slope: an explainable boosting machine-assisted wind tunnel study10.1038/s41598-023-36495-5 ↗
55. Predicting and optimizing the concrete compressive strength using an explainable boosting machine learning model
56. Concrete compressive strength prediction using an explainable boosting machine model
57. Z.T. Deger, G.T. Kaya, and J.W. Wallace, Estimate Deformation Capacity of Non-Ductile RC Shear Walls using Explainable Boosting Machine, arXiv preprint arXiv:2301.04652, 2023.
58. M.T. Kashifi, B.A. Salami, S.M. Rahman, and W. Alimi, Using Explainable Machine Learning to Predict Compressive Strength of Blended Concrete: A Data-Driven Metaheuristic Approach, ed: Research Square Platform LLC, 2023.10.21203/rs.3.rs-2998256/v1 ↗
59. Building energy loads prediction using bayesian-based metaheuristic optimized-explainable tree-based model
60. T. Li and L. Han, Dealing with Explainability Requirements for Machine Learning Systems, presented at the 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), 2023/06, 2023. [Online]. Available: http://dx.doi.org/10.1109/compsac57700.2023.00182.10.1109/COMPSAC57700.2023.00182 ↗
61. Notions of explainability and evaluation approaches for explainable artificial intelligence10.1016/j.inffus.2021.05.009 ↗
62. Sound Explanation for Trustworthy Machine Learning
63. Considerations for Evaluation and Generalization in Interpretable Machine Learning10.1007/978-3-319-98131-4_1 ↗
64. Dosage effect of superplasticizer on self-compacting concrete: correlation between rheology and strength10.1016/j.jmrt.2019.01.015 ↗
65. Prediction of self-compacting concrete homogeneity by ultrasonic velocity10.1016/j.aej.2015.08.002 ↗
66. Relationship between workability and rheological parameters of self-compacting concrete used for vertical pump up to supertall buildings
67. Interpretable auto-tune machine learning prediction of strength and flow properties for self-compacting concrete10.1016/j.conbuildmat.2023.132101 ↗
68. T. Chen and C. Guestrin, Xgboost: A scalable tree boosting system, in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016, pp. 785-794.10.1145/2939672.2939785 ↗
69. Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: a case study in China10.1016/j.enconman.2018.02.087 ↗
70. A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP)
71. G. Ke et al., LightGBM: A Highly Efficient Gradient Boosting Decision Tree, presented at the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA., 2017.
72. Freund · 1996 · 148
73. AdaBoost for feature selection, classification and its relation with SVM, a review10.1016/j.phpro.2012.03.160 ↗
74. Wood · 2017
75. Effect of aggregate size on the fracture and mechanical properties of a simple concrete10.1016/j.engfracmech.2008.02.011 ↗
76. The effect of coarse aggregate on fresh and hardened properties of self-compacting concrete (SCC)10.1016/j.proeng.2011.07.102 ↗
77. Rheology, durability, and mechanical performance of sustainable self-compacting concrete with metakaolin and limestone filler
78. Effect of fly ash and silica fume on rheology, compressive strength and self-compacting in cement mixtures10.15446/dyna.v85n206.68960 ↗
79. S. EFNARC, Guidelines for Self-Compacting Concrete, European Federation for Specialist Construction Chemicals and Concrete Systems, Norfolk, UK, English ed., February, 2002.