Computers and Concrete

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Elastic modulus of ASR-affected concrete: An evaluation using Artificial Neural Network

  • Nguyen, Thuc Nhu (Centre for Built Infrastructure Research, Faculty of Engineering and Information Technology, University of Technology Sydney) ;
  • Yu, Yang (Centre for Built Infrastructure Research, Faculty of Engineering and Information Technology, University of Technology Sydney) ;
  • Li, Jianchun (Centre for Built Infrastructure Research, Faculty of Engineering and Information Technology, University of Technology Sydney) ;
  • Gowripalan, Nadarajah (Centre for Built Infrastructure Research, Faculty of Engineering and Information Technology, University of Technology Sydney) ;
  • Sirivivatnanon, Vute (Centre for Built Infrastructure Research, Faculty of Engineering and Information Technology, University of Technology Sydney)
  • Received : 2019.01.28
  • Accepted : 2019.11.18
  • Published : 2019.12.25
⟨ Previous Next ⟩

Abstract

Alkali-silica reaction (ASR) in concrete can induce degradation in its mechanical properties, leading to compromised serviceability and even loss in load capacity of concrete structures. Compared to other properties, ASR often affects the modulus of elasticity more significantly. Several empirical models have thus been established to estimate elastic modulus reduction based on the ASR expansion only for condition assessment and capacity evaluation of the distressed structures. However, it has been observed from experimental studies in the literature that for any given level of ASR expansion, there are significant variations on the measured modulus of elasticity. In fact, many other factors, such as cement content, reactive aggregate type, exposure condition, additional alkali and concrete strength, have been commonly known in contribution to changes of concrete elastic modulus due to ASR. In this study, an artificial intelligent model using artificial neural network (ANN) is proposed for the first time to provide an innovative approach for evaluation of the elastic modulus of ASR-affected concrete, which is able to take into account contribution of several influence factors. By intelligently fusing multiple information, the proposed ANN model can provide an accurate estimation of the modulus of elasticity, which shows a significant improvement from empirical based models used in current practice. The results also indicate that expansion due to ASR is not the only factor contributing to the stiffness change, and various factors have to be included during the evaluation.

Keywords

Acknowledgement

Supported by : Australian Research Council

References

  1. Altarazi, S., Ammouri, M. and Hijazi, A. (2018), "Artificial neural network modeling to evaluate polyvinylchloride composites' properties", Comput. Mater. Sci., 153, 1-9. https://doi.org/10.1016/j.commatsci.2018.06.003.
  2. AS-1141.60.1 (2014), Methods for Sampling and Testing Aggregates Part 60.1: Alkali Aggregate Reactivity-Accelerated Mortar Bar Method, Sydney, Australia.
  3. AS-1141.60.2 (2014), Methods for Sampling and Testing Aggregates Part 60.2: Alkali Aggregate Reactivity-Concrete Prism Method, Sydney, Australia.
  4. Ashteyat, A.M. and Ismeik, M. (2018), "Predicting residual compressive strength of self-compacted concrete under various temperatures and relative humidity conditions by artificial neural networks", Compu. Concrete, 21(1), 47-54. https://doi.org/10.12989/cac.2018.21.1.047.
  5. Bal, L. and Buyle-Bodin, F. (2013), "Artificial neural network for predicting drying shrinkage of concrete", Constr. Build. Mater., 38, 248-254. https://doi.org/10.1016/j.conbuildmat.2012.08.043.
  6. Blight, G.E. and Alexander, M.G. (2011), Alkali-aggregate Reaction and Structural Damage to Concrete: Engineering Assessment, Repair and Management, CRC Press.
  7. Bui, D.K., Nguyen, T., Chou, J.S., Nguyen-Xuan, H. and Ngo, T. D. (2018), "A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete", Constr. Build. Mater., 180, 320-333. https://doi.org/10.1016/j.conbuildmat.2018.05.201.
  8. Burden, F. and Winkler, D. (2008), Bayesian Regularization of Neural Networks, Artificial Neural Networks, Springer.
  9. Dimopoulos, Y., Bourret, P. and Lek, S. (1995), "Use of some sensitivity criteria for choosing networks with good generalization ability", Neur. Proc. Lett., 2(6), 1-4. https://doi.org/10.1007/BF02309007.
  10. Duan, Z.H., Kou, S.C. and Poon, C.S. (2013), "Using artificial neural networks for predicting the elastic modulus of recycled aggregate concrete", Constr. Build. Mater., 44, 524-532. https://doi.org/10.1016/j.conbuildmat.2013.02.064.
  11. Esposito, R., Anac, C., Hendriks, M. A. and Copuroglu, O. (2016), "Influence of the alkali-silica reaction on the mechanical degradation of concrete", J. Mater. Civil Eng., 28(6), 04016007. https://doi.org/10.1061/(ASCE)MT.1943-5533.0001486.
  12. Eurocode 2 (2004), Design of Concrete Structures. Part 1-1: General rules and rules for buildings, European Committee for Standardization, Brussels, Belgium.
  13. Ferche, A.C., Panesar, D.K., Sheikh, S.A. and Vecchio, F.J. (2017), "Toward Macro-Modeling of Alkali-Silica Reaction-Affected Structures", ACI Struct. J., 114(5), 1121. https://doi.org/10.14359/51700778
  14. Foresee, F.D. and Hagan, M.T. (1997), "Gauss-Newton approximation to Bayesian learning", Proceedings of the 1997 International Joint Conference on Neural Networks, Piscataway, IEEE.
  15. Gandomi, A.H., Alavi, A.H., Shadmehri, D.M. and Sahab, M. (2013), "An empirical model for shear capacity of RC deep beams using genetic-simulated annealing", Arch. Civil Mech. Eng., 13(3), 354-369. https://doi.org/10.1016/j.acme.2013.02.007.
  16. Gao, X.X., Multon, S., Cyr, M. and Sellier, A. (2011), "Optimising an expansion test for the assessment of alkali-silica reaction in concrete structures", Mater. Struct., 44(9), 1641-1653. https://doi.org/10.1617/s11527-011-9724-y.
  17. Gautam, B.P. and Panesar, D.K. (2017), "The effect of elevated conditioning temperature on the ASR expansion, cracking and properties of reactive Spratt aggregate concrete", Constr. Build. Mater., 140, 310-320. https://doi.org/10.1016/j.conbuildmat.2017.02.104.
  18. Gautam, B.P., Panesar, D.K., Sheikh, S.A. and Vecchio, F.J. (2017), "Effect of coarse aggregate grading on the ASR expansion and damage of concrete", Cement Concrete Res., 95, 75-83. https://doi.org/10.1016/j.cemconres.2017.02.022.
  19. Gevrey, M., Dimopoulos, I. and Lek, S. (2003), "Review and comparison of methods to study the contribution of variables in artificial neural network models", Ecol. Model., 160(3), 249-264. https://doi.org/10.1016/S0304-3800(02)00257-0.
  20. Giaccio, G., Torrijos, M.C., Tobes, J.M., Batic, O.R. and Zerbino, R. (2009), "Development of alkali-silica reaction under compressive loading and its effects on concrete behavior", ACI Mater. J., 106(3), 223.
  21. Giaccio, G., Zerbino, R., Ponce, J. and Batic, O.R. (2008), "Mechanical behavior of concretes damaged by alkali-silica reaction", Cement Concrete Res., 38(7), 993-1004. https://doi.org/10.1016/j.cemconres.2008.02.009.
  22. Giannini, E.R. (2012), "Evaluation of concrete structures affected by alkali-silica reaction and delayed ettringite formation", Doctor of Philosophy, University of Texas at Austin.
  23. Hariri-Ardebili, M.A. and Saouma, V.E. (2018), "Sensitivity and uncertainty analysis of AAR affected reinforced concrete shear walls", Eng. Struct., 172, 334-345. https://doi.org/10.1016/j.engstruct.2018.05.115.
  24. Hariri-Ardebili, M.A., Saouma, V.E. and Merz, C. (2018), "Risk-informed condition assessment of a bridge with Alkali-Aggregate reaction", ACI Struct. J., 115(2), 475-487. https://doi.org/10.14359/51701106
  25. Heaton, J. (2008). Introduction to Neural Networks with Java, Heaton Research, Inc.
  26. Hodhod, O.A., Said, T.E. and Ataya, A.M. (2018), "Prediction of creep in concrete using genetic programming hybridized with ANN", Comput. Concrete, 21(5), 513-523. https://doi.org/10.12989/cac.2018.21.5.513.
  27. ISE (1992), Structural Effects of Alkali-Aggregate Reaction: Technical Guidance on the Appraisal of Existing Structures.
  28. Islam, M.S. and Ghafoori, N. (2018), "A new approach to evaluate alkali-silica reactivity using loss in concrete stiffness", Constr. Build. Mater., 167, 578-586. https://doi.org/10.1016/j.conbuildmat.2018.02.047.
  29. Kagimoto, H., Yasuda, Y. and Kawamura, M. (2014), "ASR expansion, expansive pressure and cracking in concrete prisms under various degrees of restraint", Cement Concrete Res., 59, 1-15. https://doi.org/10.1016/j.cemconres.2014.01.018.
  30. Kawabata, Y., Seignol, J.F., Martin, R.P. and Toutlemonde, F. (2017), "Macroscopic chemo-mechanical modeling of alkali-silica reaction of concrete under stresses", Constr. Build. Mater., 137, 234-245. https://doi.org/10.1016/j.conbuildmat.2017.01.090.
  31. Kim, J.K., Han, S.H. and Song, Y.C. (2002), "Effect of temperature and aging on the mechanical properties of concrete: Part I. Experimental results", Cement Concrete Res., 32(7), 1087-1094. https://doi.org/10.1016/S0008-8846(02)00744-5.
  32. Kong, L., Chen, X. and Du, Y. (2016), "Evaluation of the effect of aggregate on concrete permeability using grey correlation analysis and ANN", Comput. Concrete, 17(5), 613-628. https://doi.org/10.12989/cac.2016.17.5.613.
  33. Kubo, Y. and Nakata, M. (2012) "Effect of reactive aggregate on mechanical properties of concrete affected by Alkali-Silica reaction", Proceedings of the 14th International Conference on Alkali-Aggregate Reaction, Austin, Texas, USA.
  34. Larive, C. (1997), "Apports combines de l'experimentation et de la modelisation a la comprehension de l'alcali-reaction et de ses effets mecaniques", Ph.D., Ecole Nationale des Ponts et Chaussees, Paris.
  35. Lindgard, J. andic-Cakir, O., Fernandes, I., Ronning, T.F. and Thomas, M.D. (2012), "Alkali-silica reactions (ASR): literature review on parameters influencing laboratory performance testing", Cement Concrete Res., 42(2), 223-243. https://doi.org/10.1016/j.cemconres.2011.10.004.
  36. MacKay, D.J. (1992), "Bayesian interpolation", Neur. Comput., 4(3), 415-447. https://doi.org/10.1162/neco.1992.4.3.415.
  37. Martin, R.P., Sanchez, L., Fournier, B. and Toutlemonde, F. (2017), "Evaluation of different techniques for the diagnosis & prognosis of Internal Swelling Reaction (ISR) mechanisms in concrete", Constr. Build. Mater., 156, 956-964. https://doi.org/10.1016/j.conbuildmat.2017.09.047.
  38. Moallemi, S. and Pietruszczak, S. (2018), "Numerical analysis of propagation of macrocracks in 3D concrete structures affected by ASR", Comput. Concrete, 22(1), 1-10. https://doi.org/10.12989/cac.2018.22.1.001.
  39. Mohammed, T.U., Hamada, H. and Yamaji, T. (2003), "Relation between strain on surface and strain over embedded steel bars in ASR affected concrete members", J. Adv. Concrete Technol., 1(1), 76-88. https://doi.org/10.3151/jact.1.76.
  40. Multon, S. (2003), "Evaluation experimentale et theorique des effets mecaniques de l'alcali-reaction sur des structures modeles", Ph.D., Universite de Marne-la-Vallee (in collaboration with LCPCEDF), Champs sur Marne.
  41. Nayira, S., Erdogdu, S. and Kurbetcib, S. (2017), "Effectiveness of mineral additives in mitigating alkali-silica reaction in mortar", Comput. Concrete, 20(6), 155-163. https://doi.org/10.12989/cac.2017.20.6.705.
  42. Olden, J.D. and Jackson, D.A. (2002), "Illuminating the "black box": a randomization approach for understanding variable contributions in artificial neural networks", Ecolog. Model., 154(1-2), 135-150. https://doi.org/10.1016/S0304-3800(02)00064-9.
  43. Olden, J.D., Joy, M.K. and Death, R.G. (2004), "An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data", Ecolog. Model., 178(3-4), 389-397. https://doi.org/10.1016/j.ecolmodel.2004.03.013.
  44. Ongpeng, J., Soberano, M., Oreta, A. and Hirose, S. (2017), "Artificial neural network model using ultrasonic test results to predict compressive stress in concrete", Comput. Concrete, 19, 59-68. https://doi.org/10.12989/cac.2017.19.1.059.
  45. Ozesmi, S.L. and Ozesmi, U. (1999), "An artificial neural network approach to spatial habitat modelling with interspecific interaction", Ecolog. Model., 116(1), 15-31. https://doi.org/10.1016/S0304-3800(98)00149-5.
  46. Pleau, R., Berube, M., Pigeon, M., Fournier, B. and Raphael, S. (1989), "Mechanical behaviour of concrete affected by ASR", Proceedings of the 8th International Conference on Alkali-Aggregate Reaction, 721-726.
  47. Poyet, S., Sellier, A., Capra, B., Foray, G., Torrenti, J.-M., Cognon, H. and Bourdarot, E. (2007), "Chemical modelling of alkali silica reaction: influence of the reactive aggregate size distribution", Mater. Struct., 40(2), 229. https://doi.org/10.1617/s11527-006-9139-3.
  48. Sanchez, L. (2014), "Contribution to the assessment of damage in aging concrete infrastructures affected by alkali-aggregate reaction", Doctor of Philosophy, Universite Laval.
  49. Sanchez, L., Fournier, B., Jolin, M. and Duchesne, J. (2015), "Reliable quantification of AAR damage through assessment of the Damage Rating Index (DRI)", Cement Concrete Res., 67, 74-92. https://doi.org/10.1016/j.cemconres.2014.08.002.
  50. Sanchez, L.F.M., Fournier, B., Jolin, M., Mitchell, D. and Bastien, J. (2017), "Overall assessment of Alkali-Aggregate Reaction (AAR) in concretes presenting different strengths and incorporating a wide range of reactive aggregate types and natures", Cement Concrete Res., 93, 17-31. https://doi.org/10.1016/j.cemconres.2016.12.001.
  51. Saouma, V. and Perotti, L. (2006), "Constitutive model for alkali-aggregate reactions", ACI Mater. J., 103(3), 194-202.
  52. Sargolzahi, M., Kodjo, S.A., Rivard, P. and Rhazi, J. (2010), "Effectiveness of nondestructive testing for the evaluation of alkali-silica reaction in concrete", Constr. Build. Mater., 24(8), 1398-1403. https://doi.org/10.1016/j.conbuildmat.2010.01.018
  53. Seignol, J.F., Baghdadi, N. and Toutlemonde, F. (2009), "A macroscopic chemo-mechanical model aimed at re-assessment of delayed ettringite formation affected concrete structures", Proceedings of the first International Conference on Computational Technologies in Concrete Structures (CTCS'09), 422-440.
  54. Shayan, A. and Ivanusec, I. (1989), "Influence of NaOH on mechanical properties of cement paste and mortar with and without reactive aggregate", Proceedings of the 8th International Conference on Alkali-Aggregate Reaction, Kyoto, Japan, 715-720.
  55. Sims, I. and Poole, A.B. (2017), Alkali-Aggregate Reaction in Concrete: A World Review, CRC Press.
  56. Sirivivatnanon, V., Mohammadi, J. and South, W. (2016), "Reliability of new Australian test methods in predicting alkali silica reaction of field concrete", Constr. Build. Mater., 126, 868-874. https://doi.org/10.1016/j.conbuildmat.2016.09.055.
  57. Smaoui, N., Berube, M., Fournier, B., Bissonnette, B. and Durand, B. (2005), "Effects of alkali addition on the mechanical properties and durability of concrete", Cement Concrete Res., 35(2), 203-212. https://doi.org/10.1016/j.cemconres.2004.05.007.
  58. Smaoui, N., Bissonnette, B., Berube, M.A., Fournier, B. and Durand, B. (2005), "Mechanical properties of ASR-affected concrete containing fine or coarse reactive aggregates", J. ASTM Int., 3(3), 1-16. https://doi.org/10.1520/JAI12010.
  59. Sonebi, M., Grunewald, S., Cevik, A. and Walraven, J. (2016), "Modelling fresh properties of self-compacting concrete using neural network technique", Comput. Concrete, 18(4), 903-920. https://doi.org/10.12989/cac.2016.18.4.903.
  60. Yu, Y., Li, W., Li, J. and Nguyen, T.N. (2018), "A novel optimised self-learning method for compressive strength prediction of high performance concrete", Constr. Build. Mater., 184, 229-247. https://doi.org/10.1016/j.conbuildmat.2018.06.219.
  61. Yu, Y., Li, Y. and Li J. (2015), "Nonparametric modeling of magnetorheological elastomer base isolator based on artificial neural network optimized by ant colony algorithm", J. Intel. Mater. Syst. Struct., 26(14), 1789-1798. https://doi.org/10.1177/1045389X15577649.
  62. Yu, Y., Zhang, C., Gu, X. and Cui, Y. (2019), "Expansion prediction of alkali aggregate reactivity-affected concrete structures using a hybrid soft computing method", Neur. Comput. Appl., 31(12), 8641-8660. https://doi.org/10.1007/s00521-018-3679-7.
  63. Yuksel, C., Mardani-Aghabaglou, A., Beglarigale, A., Ramyar, K. and Andic-Cakir, O. (2016), "Influence of water/powder ratio and powder type on alkali-silica reactivity and transport properties of self-consolidating concrete", Mater. Struct., 49(1-2), 289-299. https://doi.org/10.1617/s11527-014-0497-y.

Cited by

  1. A neuro-fuzzy approach to predict the shear contribution of end-anchored FRP U-jackets vol.26, pp.5, 2019, https://doi.org/10.12989/cac.2020.26.5.397
  2. Pozzolanic properties of trachyte and rhyolite and their effects on alkali-silica reaction vol.11, pp.4, 2019, https://doi.org/10.12989/acc.2021.11.4.299