Uncertainty-Based Performance Prediction and Optimization of High-Fluidization Cement Grouting Material Using Machine Learning and Bayesian Inference
Source abstract
Abstract
Abstract In pavement engineering, cement grouting material is widely used to pour into large void asphalt concrete to prepare semi-flexible composite mixtures. It plays an essential role in the performance of the semi-flexible composite mixture. To meet specific engineering requirements, various additives are mixed into the grouting material to improve the physical and mechanical properties. As a result, the uncertainty of the grouting material is also more significant as the complexity of material composition increases during the material design. It will bring some unknown risks for the engineering application. Hence, it is necessary to quantize the uncertainty during the material design of the grouting material and evaluate the reliability of the material formula. In this study, a novel framework of material design was developed by combing the Multioutput support vector machine (MSVM), Bayesian inference, and laboratory experiments. The MSVM was used to approximate and characterize the complex and nonlinear relationship between the grouting material formula and its properties based on laboratory experiments. The Bayesian inference was adopted to deal with the uncertainty of material design using the Markov Chain Monte Carlo. An optimized formula of the cement grouting material is obtained based on the developed framework. Experimental results show that the optimized formula improves engineering properties and performance stability, especially early strength. The developed framework provides a helpful, valuable, and promising tool for evaluating the reliability of the material design of the grouting material considering the uncertainty.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Asphalt Pavement Performance Evaluation · Infrastructure Maintenance and Monitoring · Grouting, Rheology, and Soil Mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (42)
Show source-provided references
1. Cai, X., Zhang, H., Zhang, J., Chen, X., Yang, J., & Hong, J. (2019). Investigation on reinforcing mechanisms of semi-flexible pavement material through micromechanical model. Construction and Building Materials, 198, 732–741. https://doi.org/10.1016/j.conbuildmat.2018.11.24310.1016/j.conbuildmat.2018.11.243 ↗
2. China Association for Engineering Construction Standardization. (2019). Technical specification for road semi-flexible pavement (T\CECS G:D51-01-2019). Beijing, China.
3. Doh, J., & Lee, J. (2018). Bayesian estimation of the lethargy coefficient for probabilistic fatigue life model. Journal of Computational Design and Engineering, 5(2), 191–197. https://doi.org/10.1016/j.jcde.2017.10.00210.1016/j.jcde.2017.10.002 ↗
4. Guo, X., & Hao, P. (2021). Influential factors and evaluation methods of the performance of grouted semi-flexible pavement (GSP)—A review. Applied Sciences, 11, 6700. https://doi.org/10.1016/j.jclepro.2020.12461410.1016/j.jclepro.2020.124614 ↗
5. Hong, J., Wang, K., Xiong, Z., Gong, M., Deng, C., Peng, G., & Zhu, H. (2019). Investigation into the freeze-thaw durability of semi-flexible pavement mixtures. Road Materials Pavement Design, 21, 2198–2214. https://doi.org/10.1080/14680629.2019.159999510.1080/14680629.2019.1599995 ↗
6. Hu, S., Huang, S., Liu, Z., Ding, Q., & Wu, G. (2008). Design and construction of the semi-flexible based permeable anti-sliding noise-reducing pavement. Journal of Wuhan University of Technology, 30, 27–31.
7. Jones, R. E., Rimsza, J. M., Trageser, J. E., & Hogancamp, J. R. (2021). Simulation of hardened cement degradation and estimation of uncertainty in predicted failure times with peridynamics. Construction and Building Materials, 286, 122927. https://doi.org/10.1016/j.conbuildmat.2021.12292710.1016/j.conbuildmat.2021.122927 ↗
8. Jong, S. C., Ong, D. E. L., & Oh, E. (2022). A novel Bayesian inference method for predicting optimum strength gain in sustainable geomaterials for greener construction. Construction and Building Materials, 344, 128255. https://doi.org/10.1016/j.conbuildmat.2022.12825510.1016/j.conbuildmat.2022.128255 ↗
9. Kim, J. S., Kim, S. Y., & Han, T. S. (2020). Sensitivity and uncertainty estimation of cement paste properties to microstructural characteristics using FOSM method. Construction and Building Materials, 242, 118159. https://doi.org/10.1016/j.conbuildmat.2020.11815910.1016/j.conbuildmat.2020.118159 ↗
10. Li, S., Zhao, H., & Ru, Z. (2016). Probabilistic back analysis based on Bayesian and multi-output Support Vector Machine for a high cut rock slope. Engineering Geology, 203, 178–190. https://doi.org/10.1016/j.enggeo.2015.11.00410.1016/j.enggeo.2015.11.004 ↗
11. Li, X., & Hao, J. (2018). New preparation of super-early-strength grouting materials by ternary complex system. Advances in Cement Research, 30, 139–147. https://doi.org/10.1680/jadcr.17.0007710.1680/jadcr.17.00077 ↗
12. Lu, P., Ye, L., Zhong, W., Qu, Y., Zhai, B., Tang, Y., & Zhao, Y. (2020). A novel spatio-temporal wind power forecasting framework based on multi-output support vector machine and optimization strategy. Journal of Cleaner Production, 254, 119993. https://doi.org/10.1016/j.jclepro.2020.11999310.1016/j.jclepro.2020.119993 ↗
13. Lynch, S. M. (2007). Introduction to applied Bayesian statistics and estimation for social scientists. Springer.10.1007/978-0-387-71265-9 ↗
14. Markus, R., Gustau, C., Biorn, S., Martin, J., Joachim, D., & Nuno, C. (2019). Deep learning and process understanding for data-driven earth system science. Nature, 566, 195–203. https://doi.org/10.1038/s41586-019-0912-110.1038/s41586-019-0912-1 ↗
15. Miller, S. A. (2021). The role of data variability and uncertainty in the probability of mitigating environmental impacts from cement and concrete. Environmental Research Letters, 16(5), 054053. https://doi.org/10.1088/1748-9326/abe67710.1088/1748-9326/abe677 ↗
16. Ministry of Transport of the People's Public of China. (2020). Test methods of cement and concrete for highway engineering (JTG 3420-2020). Beijing, China.
17. Nahvi, A., Sadoughi, M. K., Arabzadeh, A., Sassani, Al., Hu, C., Ceylan, H., & Kim, S. (2018). Multi-objective Bayesian optimization of super hydrophobic coatings on asphalt concrete surfaces. Journal of Computational Design and Engineering, 6(4), 693–704. https://doi.org/10.1016/j.jcde.2018.11.00510.1016/j.jcde.2018.11.005 ↗
18. Ni, P., Han, Q., Du, X., & Cheng, X. (2022). Bayesian model updating of civil structures with likelihood-free inference approach and response reconstruction technique. Mechanical Systems and Signal Processing, 164, 108204. https://doi.org/10.1016/j.ymssp.2021.10820410.1016/j.ymssp.2021.108204 ↗
19. Pham, A. D., Ngo, N. T., & Nguyen, T. K. (2020). Machine learning for predicting long-term deflections in reinforce concrete flexural structures. Journal of Computational Design and Engineering, 7(1), 95–106. https://doi.org/10.1093/jcde/qwaa01010.1093/jcde/qwaa010 ↗
20. Ren, J., Li, D., Xu, Y., Huang, J., & Liu, W. (2021a). Fatigue behaviour of rock asphalt concrete considering moisture, high-temperature, and stress level. International Journal of Pavement Engineering. https://doi.org/10.1080/10298436.2021.196901810.1080/10298436.2021.1969018 ↗
21. Ren, J., Xue, B., Zhang, L., Liu, W., Li, D., & Xu, Y. (2020). Characterization and prediction of rutting resistance of rock asphalt mixture under the coupling effect of water and high-temperature. Construction and Building Materials, 254, 119316. https://doi.org/10.1016/j.conbuildmat.2020.11931610.1016/j.conbuildmat.2020.119316 ↗
22. Ren, J., Zang, G., & Xu, Y. (2019). Formula and pavement properties of a composite modified bioasphalt binder considering performance and economy. Journal of Materials in Civil Engineering, 31, 04019243. https://doi.org/10.1061/(ASCE)MT.1943-5533.000288810.1061/(ASCE)MT.1943-5533.0002888 ↗
23. Ren, J., Zhang, L., Zhao, H., Zhao, Z., & Wang, S. (2022a). Determination of the fatigue equation for the cement-stabilized cold recycled mixtures with road construction waste materials based on data-driven. International Journal of Fatigue, 158, 106765. https://doi.org/10.1016/j.ijfatigue.2022.10676510.1016/j.ijfatigue.2022.106765 ↗
24. Ren, J., Zhao, H., Zhang, L., Zhao, Z., Xu, Y., Cheng, Y., Wang, M., Chen, J., & Wang, J. (2022b). Design optimization of cement grouting material based on adaptive boosting algorithm and simplicial homology global optimization. Journal of Building Engineering, 49, 104049. https://doi.org/10.1016/j.jobe.2022.10404910.1016/j.jobe.2022.104049 ↗
25. Ren, J., Zhao, Z., Xu, Y., Wang, S., Chen, H., Huang, J., Xue, B., Wang, J., Chen, J., & Yang, C. (2021b). High-fluidization, early strength cement grouting material enhanced by nano-SiO2: Formula and mechanisms. Materials, 14, 6144. https://doi.org/10.3390/ma1420614410.3390/ma14206144 ↗
26. Schölkopf, B., & Smola, A. (2002). Learning with kernels. MIT Press.
27. Su, N., & Miao, B. (2003). A new method for the mix design of medium strength flowing concrete with low cement content. Cement & Concrete Composites, 25, 215–222. https://doi.org/10.1016/S0958-9465(02)00013-610.1016/S0958-9465(02)00013-6 ↗
28. Thomas, A. J., Barocio, E., Bilionis, I., & Pipes, R. B. (2022). Bayesian inference of fiber orientation and polymer properties in short fiber-reinforced polymer composites. Composites Science and Technology, 228, 109630. https://doi.org/10.1016/j.compscitech.2022.10963010.1016/j.compscitech.2022.109630 ↗
29. Tsamatsoulis, D., & Nikolakakos, N. (2013). Optimizing the sulphates content of cement using multivariable modelling and uncertainty analysis. Chemical and Biochemical Engineering Quarterly, 27(2), 133–144. https://doi.org/10.1515/revce-2013-000310.1515/revce-2013-0003 ↗
30. Tuia, D., Verrelst, J., Alonso, L., Perez-Cruz, F., & Camps-Valls, G. (2011). Multi output support vector regression for remote sensing biophysical parameter estimation. IEEE Geoscience and Remote Sensing Letters, 8, 804–808. https://doi.org/10.1109/LGRS.2011.210993410.1109/LGRS.2011.2109934 ↗
31. Venkovic, N., Sorelli, L., Sudret, B., Yalamas, T., & Gagné, R. (2013). Uncertainty propagation of a multiscale poromechanics-hydration model for poroelastic properties of cement paste at early-age. Probabilistic Engineering Mechanics, 32, 5–20. https://doi.org/10.1016/j.probengmech.2012.12.00310.1016/j.probengmech.2012.12.003 ↗
32. Vigliotti, A., Csányi, G., & Deshpande, V. S. (2018). Bayesian inference of the spatial distributions of material properties. Journal of the Mechanics and Physics of Solids, 118, 74–97. https://doi.org/10.1016/j.jmps.2018.05.00710.1016/j.jmps.2018.05.007 ↗
33. Zhang, L., Wang, M., Zhao, H., & Chang, X. (2022). Uncertainty quantification for the mechanical behavior of fully grouted rockbolts subjected to pull-out tests. Computers and Geotechnics, 145, 104665. https://doi.org/10.1016/j.compgeo.2022.10466510.1016/j.compgeo.2022.104665 ↗
34. Zhang, W., Shen, S., Goodwin, R. D., Wang, D., & Zhong, J. (2020). Performance characterization of semi-flexible composite mixture. Materials, 13, 342. https://doi.org/10.3390/ma1302034210.3390/ma13020342 ↗
35. Zhang, Y., Li, W., & Li, D. (2017). Preparation and performance research of cement-based grouting materials with high early strength and expansion. Journal of Wuhan University of Technology-Materials Science Edition, 32, 1115–1118. https://doi.org/10.1007/s11595-017-1719-210.1007/s11595-017-1719-2 ↗
36. Zhao, H. (2022). A practical and efficient reliability-based design optimization method for rock tunnel support. Tunneling and Underground Space Technology, 127, 104587. https://doi.org/10.1016/j.tust.2022.10458710.1016/j.tust.2022.104587 ↗
37. Zhao, H., Chen, B., Li, S., Li, Z., & Zhu, C. (2021b). Updating the models and uncertainty of mechanical parameters for rock tunnels using Bayesian inference. Geoscience Frontiers, 12(5), 101198. https://doi.org/10.1016/j.gsf.2021.10119810.1016/j.gsf.2021.101198 ↗
38. Zhao, H., Li, S., & Zhu, C. (2021a). Uncertainty analysis of rock tunnel based on fractional moment and dimensional reduction method. International Journal of Rock Mechanics and Mining Sciences, 139, 104656. https://doi.org/10.1016/j.ijrmms.2021.10465610.1016/j.ijrmms.2021.104656 ↗
39. Zhao, H., & Yin, S. (2016). Inverse analysis of geomechanical parameters by artificial bee colony algorithm and multi-output support vector machine. Inverse Problems in Science and Engineering, 24(7), 1266–1281. https://doi.org/10.1080/17415977.2016.117825710.1080/17415977.2016.1178257 ↗
40. Zhao, Z., Wang, S., Ren, J., Wang, Y., & Wang, C. (2021c). Fatigue characteristics and prediction of cement-stabilized cold recycled mixture with road-milling materials considering recycled aggregate composition. Construction and Building Materials, 301, 124122. https://doi.org/10.1016/j.conbuildmat.2021.12412210.1016/j.conbuildmat.2021.124122 ↗
41. Zhou, Y., Chang, F. J., Chang, L. C., Kao, I. F., Wang, Y. S., & Kang, C. C. (2019). Multi-output support vector machine for regional multi-step-ahead PM2.5 forecasting. Science of the Total Environment, 651, 230–240. https://doi.org/10.1016/j.scitotenv.2018.09.11110.1016/j.scitotenv.2018.09.111 ↗
42. Zhu, Z. R., Cheng, Q. Q., Chen, Y. Y., Chen, E. J., & Su, Y. (2021). Statistically quantifying the uncertainty in strength of marine clay stabilized by a type of portland-fly ash cement. Advances in Civil Engineering, 2021, 6654802. https://doi.org/10.1155/2021/665480210.1155/2021/6654802 ↗