Journal of Korean Association for Spatial Structures (한국공간구조학회논문집)

Korean Association for Spatial Structures (한국공간구조학회)
권호 표지
DOI QR코드

DOI QR Code

Machine Learning Based Strength Prediction of UHPC for Spatial Structures

대공간 구조물의 UHPC 적용을 위한 기계학습 기반 강도예측기법

  • Lee, Seunghye (Dept. of Architectural Engineering, Sejong Univ.) ;
  • Lee, Jaehong (Dept. of Architectural Engineering, Sejong Univ.)
  • Received : 2020.09.09
  • Accepted : 2020.10.20
  • Published : 2020.12.15
Download PDF
⟨ Previous Next ⟩

Abstract

There has been increasing interest in UHPC (Ultra-High Performance Concrete) materials in recent years. Owing to the superior mechanical properties and durability, the UHPC has been widely used for the design of various types of structures. In this paper, machine learning based compressive strength prediction methods of the UHPC are proposed. Various regression-based machine learning models were built to train dataset. For train and validation, 110 data samples collected from the literatures were used. Because the proportion between the compressive strength and its composition is a highly nonlinear, more advanced regression models are demanded to obtain better results. The complex relationship between mixture proportion and concrete compressive strength can be predicted by using the selected regression method.

Keywords

References

  1. de Larrard, F., & Sedran, T., "Optimization of ultra-high-performance concrete by the use of a packing model", Cement and Concrete Research, Vol.24, No.6, pp.997-1009, 1994, doi: 10.1016/0008-8846(94)90022-1
  2. Lee, N. K., Koh, K. T., Park, S. H., & Ryu, G. S., "Microstructural investigation of calcium aluminate cement-based ultra-high performance concrete (UHPC) exposed to high temperatures", Cement and Concrete Research, Vol.102, pp.109-118, 2017, doi: 10.1016/j.cemconres.2017.09.004
  3. Kang, T. H. K., Kim, S. H., Kim, M. S., & Hong, S. G., "Impact Resistance of UHPC Exterior Panels under High Velocity Impact Load", Journal of the Korea Concrete Institute, Vol.28, No.4, pp.455-462, 2016, doi: 10.4334/JKCI.2016.28.4.455
  4. Azmee, N. M., & Shafiq, N., "Ultra-high performance concrete: From fundamental to applications", Case Studies in Construction Materials, Vol.9, pp.e00197, 2018, doi: 10.1016/j.cscm.2018.e00197
  5. Park, C. J., Koh, K. T., Ahn, G. H., & Han, M. C., "Effect of Silica Fume Types on the Mechanical Properties of Ultra-High Performance Concrete", Journal of the Korean Recycled Construction Resources Institute, Vol.3, No.3, pp.220-227, 2015, doi: 10.14190/JRCR.2015.3.3.220
  6. Ghafari, E., Costa, H., & Julio, E., "Statistical mixture design approach for eco-efficient UHPC", Cement and Concrete Composites, Vol.55, pp.17-25, 2015, doi: 10.1016/j.cemconcomp.2014.07.016
  7. Ghafari, E., Bandarabadi, M., Costa, H., & Julio, E., "Prediction of fresh and hardened state properties of UHPC: comparative study of statistical mixture design and an artificial neural network model", Journal of Materials in Civil Engineering, Vol.27, No.11, pp.04015017, 2015, doi: 10.1061/(ASCE)MT.1943-5533.0001270
  8. Abellan-Garcia, J., "Four-layer perceptron approach for strength prediction of UHPC", Construction and Building Materials, Vol.256, pp.119465, 2020, doi: 10.1016/j.conbuildmat.2020.119465
  9. Stulp, F., & Sigaud, O., "Many regression algorithms, one unified model: A review", Neural Networks, Vol.69, pp.60-79, 2015, doi: 10.1016/j.neunet.2015.05.005
  10. Abuodeh, O. R., Abdalla, J. A., & Hawileh, R. A., "Assessment of compressive strength of Ultra-high Performance Concrete using deep machine learning techniques", Applied Soft Computing, Vol.95, pp.106552, 2020, doi: 10.1016/j.asoc.2020.106552
  11. Habel, K., Viviani, M., Denarie, E., & Bruhwiler, E., "Development of the mechanical properties of an Ultra-High Performance Fiber Reinforced Concrete (UHPFRC)", Cement and Concrete Research, Vol.36, No.7, pp.1362-1370, 2006, doi: 10.1016/j.cemconres.2006.03.009
  12. Ghrici, M., Kenai, S., & Said-Mansour, M., "Mechanical properties and durability of mortar and concrete containing natural pozzolana and limestone blended cements", Cement and Concrete Composites, Vol.29, No.7, pp.542-549, 2017, doi: 10.1016/j.cemconcomp.2007.04.009
  13. Graybeal, B. A., "Compressive Behavior of Ultra-High-Performance Fiber-Reinforced Concrete", ACI Materials Journal, Vol.104, No.2, pp.146-152, 2007
  14. Hassan, A. M. T., Jones, S. W., Mahmud, G. H., "Experimental test methods to determine the uniaxial tensile and compressive behaviour of ultra high performance fibre reinforced concrete (UHPFRC)", Construction and Building Materials, Vol.37, pp.874-882, 2012, doi: 10.1016/j.conbuildmat.2012.04.030
  15. Wille, K., & Boisvert-Cotulio, C., "Material efficiency in the design of ultra-high performance concrete", Construction and Building Materials, Vol.86, pp.33-43, 2015, doi: 10.1016/j.conbuildmat.2015.03.087
  16. Jang, H. O., Lee, H. S., Cho, K., & Kim, J., "Experimental study on shear performance of plain construction joints integrated with ultra-high performance concrete (UHPC)", Construction and Building Materials, Vol.152, pp.16-23, 2017, doi: 10.1016/j.conbuildmat.2017.06.156
  17. Hassan, M., & Wille, K., "Experimental impact analysis on ultra-high performance concrete (UHPC) for achieving stress equilibrium (SE) and constant strain rate (CSR) in Split Hopkinson pressure bar (SHPB) using pulse shaping technique", Construction and Building Materials, Vol.144, pp.747-757, 2017, doi: 10.1016/j.conbuildmat.2017.03.185
  18. Shafieifar, M., Farzad, M., & Azizinamini, A., "Experimental and numerical study on mechanical properties of Ultra High Performance Concrete (UHPC)", Construction and Building Materials, Vol.156, pp.402-411, 2017, doi: 10.1016/j.conbuildmat.2017.08.170
  19. Alsalman, A., Dang, C. N., Prinz, G. S., & Hale, W. M., "Evaluation of modulus of elasticity of ultra-high performance concrete", Construction and Building Materials, Vol.153, pp.918-928, 2017, doi: 10.1016/j.conbuildmat.2017.07.158
  20. Zhong, R., Wille, K., & Viegas, R., "Material efficiency in the design of UHPC paste from a life cycle point of view", Construction and Building Materials, Vol.160, pp.505-513, 2018, doi: 10.1016/j.conbuildmat.2017.11.049
  21. Alkaysi, M., & El-Tawil, S., "Factors affecting bond development between Ultra High Performance Concrete (UHPC) and steel bar reinforcement", Construction and Building Materials, Vol.144, pp.412-422, 2017, doi: 10.1016/j.conbuildmat.2017.03.091
  22. Sohail, M. G., Wang, B., Jain, A., Kahraman, R., Ozerkan, N. G., Gencturk, B., ... Belarbi, A., "Advancements in Concrete Mix Designs: High-Performance and UltrahighPerformance Concretes from 1970 to 2016", Journal of Materials in Civil Engineering, Vol.30, No.3, pp.04017310, 2018, doi: 10.1061/(ASCE)MT.1943-5533.0002144
  23. Feng, D. C., Liu, Z. T., Wang, X. D., Chen, Y., Chang, J. Q., Wei, D. F., & Jiang, Z. M., "Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach", Construction and Building Materials, Vol.230, pp.117000, 2020, doi: 10.1016/j.conbuildmat.2019.117000
  24. Liaw, A., & Wiener, M., "Classification and Regression by RandomForest", R news, Vol.2, No.3, pp.18-22, 2002, Retrieved from https://cogns.northwestern.edu/cbmg/LiawAndWiener2002.pdf
  25. Safavian, S. R., & Landgrebe, D., "A survey of decision tree classifier methodology", IEEE Transactions on Systems, Man, and Cybernetics, Vol.21, No.3, pp.660-674, 1991, doi: 10.1109/21.97458
  26. Liu, Y., & Yao, X., "Ensemble learning via negative correlation", Neural networks, Vol.12, No.10, pp.1399-1404, 1999, doi: 10.1016/S0893-6080(99)00073-8
  27. Dorogush, A. V., Ershov, V., & Gulin, A., "CatBoost: gradient boosting with categorical features support", arXiv:1810.11363, 2018
  28. Avnimelech, R., & Intrator, N., "Boosting Regression Estimators", Neural Computation, Vol.11, No.2, pp.499-520, 1999, doi: 10.1162/089976699300016746