Surrogate Modeling for Self-Consolidating Concrete Characteristics Estimation for Efficient Prestressed Bridge Construction

International Concrete Abstracts Portal

The International Concrete Abstracts Portal is an ACI led collaboration with leading technical organizations from within the international concrete industry and offers the most comprehensive collection of published concrete abstracts.

  


Title: Surrogate Modeling for Self-Consolidating Concrete Characteristics Estimation for Efficient Prestressed Bridge Construction

Author(s): Junwon Seo and Jharna Pokhrel

Publication: Symposium Paper

Volume: 333

Issue:

Appears on pages(s): 19-39

Keywords: bridge; characteristics; girder; material; prestressed; SCC; surrogate modeling

DOI: 10.14359/51720268

Date: 10/1/2019

Abstract:
This paper investigates the effects of material constituents on fresh and hardened properties of Self-Consolidating Concrete (SCC) mixture necessary for efficient prestressed bridge girder fabrication using a surrogate modeling technique. Response surface methodology (RSM)-based surrogate models consisting of input parameterssuch as density of coarse and fine aggregate were created based upon the past laboratory testing results for differentSCC mixture trials. These models were used to estimate various SCC material characteristics, including slump flow, J-ring flow, passing ability, filling capacity, Visual Stability Index (VSI), T50 (concrete spread time to reach the 50.8 cm [20 in] mark), column segregation, 16-hour compressive strength, and 28-days compressive strength, while examining the correlation between the input parameters on each material characteristic. To observe the effect of core input parameters in an efficient manner, 2D contour plot and 3D surface plot for material characteristics were also created. Then, statistical analyses with the testing results were performed to determine the accuracy of the surrogate models in terms of coefficient of regression (R2). Most of the R2 values are higher than 90%, indicating a higher degree of correlation among the testing and surrogate data. Average predicted-to-measure ratios of the surrogate models were almost equal to or slightly greater than 1.00, showing good agreement with the testing results, and specifically, the surrogate and testing values for J-ring flow and 28-days compressive strength were nearly identical. Key findings indicate that the coarse aggregate content significantly affected the characteristics of the SCC mixtures.

Related References:

1. Khayat, K., Ghezal, A., and Hadriche, M., 2000, “Utility of statistical models in proportioning selfconsolidating concrete”, Materials and structures, 33(5), 338-344.

2. Berry, M., Kappes, B., and Kappes, L., 2015, “Optimization of Concrete Mixtures Containing Reclaimed Asphalt Pavement”, ACI Materials Journal, 112(6), 723-733.

3. Akalin, O., Akay, K. U., and Sennaroglu, B., 2010, “Self-Consolidating High-Strength Concrete Optimization by Mixture Design Method”, ACI Materials journal, 107(4), 43-53.

4. Shilstone, J. S., 1990, “Concrete mixture optimization”, Concrete International, 12(6), 33-39.

5. Khayat. K., 2000, “Optimization and performance of air-entrained, self-consolidating concrete”, Materials Journal, 97(5), 526-35.

6. Myers, R. H., Montgomery, D. C., and Anderson-Cook, C. M., 2016, “Response surface methodology: process and product optimization using designed experiments”, John Wiley & Sons.

7. Montgomery, D. C., 2017, “Design and analysis of experiments”, John Wiley & Sons.

8. Berry, M., Kappes, B., and Schroeder, D., 2017, “Reclaimed Asphalt Pavement as Aggregate in Portland Cement Concrete”, Special Publication, 314, 1-14.

9. Nambiar, E. K., and Ramamurthy, K., 2006, “Models relating mixture composition to the density and strength of foam concrete using response surface methodology”, Cement and Concrete Composites, 28(9), 752-760.

10. Mohammed, B. S., Fang, O. C., Hossain, K. M. A, and Lachemi, M., 2012, “Mix proportioning of concrete containing paper mill residuals using response surface methodology”, Construction and Building Materials, 35, 63-68.

11. Lotfy, A., Hossain, K. M., and Lachemi, M., 2014, "Application of statistical models in proportioning lightweight self-consolidating concrete with expanded clay aggregates", Construction and Building Materials, 65, 450-469.

12. Gao, Y., Xu, J., Luo, X., Zhu, J., and Nie, L., 2016, "Experiment research on mix design and early mechanical performance of alkali-activated slag using response surface methodology (RSM)", Ceramics international, 42(10), 11666-11673.

13. Upasani, R. S., and Banga, A. K., 2004, "Response surface methodology to investigate the iontophoretic delivery of tacrine hydrochloride", Pharmaceutical research, 21(12), 2293-2299.

14. Seo, J., Dueñas-Osorio, L., Craig, J. I., and Goodno, B. J., 2012, “Metamodel-based regional vulnerability estimate of irregular steel moment-frame structures subjected to earthquake events”, Engineering Structures, 45, 585-597.

15. Seo, J., and Linzell, D. G., 2013, “Use of response surface metamodels to generate system level fragilities for existing curved steel bridges”, Engineering Structures, 52, 642-653.

16. Seo, J., and Linzell, D. G., 2012, “Horizontally curved steel bridge seismic vulnerability assessment”, Engineering Structures, 34, 21-32.

17. Cheng, J., Jiang, J. J., and Xiao, R. C., 2003, “Aerostatic stability analysis of suspension bridges under parametric uncertainty”, Engineering Structures, 25(13), 1675-1684.

18. Kim, D. H., and Lee, S. G., 2015, “Reliability analysis of offshore wind turbine support structures under extreme ocean environmental loads”, Renewable Energy, 79, 161-166.

19. Ghafari, E., Costa, H., and Júlio, E., 2014, "RSM-based model to predict the performance of selfcompacting UHPC reinforced with hybrid steel micro-fibers", Construction and Building Materials, 66, 375-383.

20. Kockal, N. U., and Ozturan, T., 2011, "Optimization of properties of fly ash aggregates for high-strength lightweight concrete production" Materials & Design, 32(6), 3586-3593.

21. Aldahdooh, M., Bunnori, N. M., and Johari, M. M., 2013, "Evaluation of ultra-high-performance-fiber reinforced concrete binder content using the response surface method", Materials & Design, 52. 957-965.

22. Muthukumar, M., Mohan, D., and Rajendran, M., 2003, "Optimization of mix proportions of mineral aggregates using Box Behnken design of experiments", Cement and Concrete Composites, 25(7), 751-758.

23. Ghezal, A., and Khayat, K. H., 2002, "Optimizing self-consolidating concrete with limestone filler by using statistical factorial design methods", Materials Journal, 99(3), 264-272.

24. Bayramov, F., Taşdemir, C., and Taşdemir, M., 2004, "Optimisation of steel fibre reinforced concretes by means of statistical response surface method", Cement and Concrete Composites, 26(6), 665-675.

25. Sonebi, M., Grunewald, S., and Walraven, J., 2007, "Filling ability and passing ability of self-consolidating concrete", ACI Materials Journal, 104(2), 162-170.

26. Box, J., and Wilson, W., 1951, “Central composites design”, JR Stat Soc, 1, 1-35.

27. Lee, J. H., Park, S. J., and Jeon, S. J., 2006, “Optimum design criteria for a synchronous reluctance motor with concentrated winding using response surface methodology”, Journal of applied physics, 99(8), 08R3251-08R3253.

28. Chen, Y., Zhao, L., Liu, B., and Zuo, S., 2012, “Application of response surface methodology to optimize microwave-assisted extraction of polysaccharide from Tremella”, Physics Procedia, 24, 429-433.

29. Gao, N., Zhu, S. A., and He, B., 2006. “A new magnetic resonance electrical impedance tomography (MREIT) algorithm: the RSM-MREIT algorithm with applications to estimation of human head conductivity”, Physics in medicine and biology, 51(12), 3067-3083.

30. Seo, J., Kim, Y. J., and Zandyavari, S., 2015, "Response surface Metamodel-based performance reliability for reinforced concrete beams Strengthened with FRP Sheets", Special Publication, 304, 1-20.

31. Simon, M. J., Lagergren, E. R., and Synder, K. A., 1997, “Concrete Mixture Optimization Statistical Mixture Design Methods,” International Sympossium on High Performance Concrete, New Orleans, LA, 230-244.

32. Torres, E., Seo, J., and Lederle, R. E., 2017, "Experimental and Statistical Investigation of Self-Consolidating Concrete Mixture Constituents for Prestressed Bridge Girder Fabrication", Journal of Materials in Civil Engineering, 29(9), 040171411-0401714111.

33. Li, C., Bai, J., Cai, Z., and Ouyang, F., 2002, “Optimization of a cultural medium for bacteriocin production by Lactococcus lactis using response surface methodology”, Journal of Biotechnology, 93(1), 27-34.

34. Mojarrad, J. S., Nemati, M., Valizadeh, H., Ansarin, M., and Bourbour, S., 2007, “Preparation of glucosamine from exoskeleton of shrimp and predicting production yield by response surface methodology”, Journal of agricultural and food chemistry, 55(6), 2246-2250.

35. Wang, X., Wu, Y., Chen, G., Yue, W., Liang, Q., and Wu, Q., 2013, “Optimisation of ultrasound assisted extraction of phenolic compounds from Sparganii rhizoma with response surface methodology”, Ultrasonics sonochemistry, 20(3), 846-854.

36. Öktem, H., Erzurumlu, T., and Kurtaran, H., 2005, "Application of response surface methodology in the optimization of cutting conditions for surface roughness," Journal of materials processing technology, 170(1),11-16.

37. Hu, J., and Wang, K., 2011, “Effect of coarse aggregate characteristics on concrete rheology”, Construction and Building Materials, 25(3), 1196-1204.

38. Alshihri, M. M., Azmy, A. M., and El-Bisy, M. S., 2009, “Neural networks for predicting compressive strength of structural light weight concrete”, Construction and Building Materials, 23(6), 2214-2219.

39. Seo, J., Torres, E., and Schaffer, W., 2017, “Self-consolidating concrete for prestressed bridge girders”, Wisconsin Department of Transportation (WisDOT), WisDOT 0092-15-03, 181.