Neural Computing and Applications

, Volume 31, Issue 12, pp 8641–8660 | Cite as

Expansion prediction of alkali aggregate reactivity-affected concrete structures using a hybrid soft computing method

  • Yang Yu
  • Chunwei ZhangEmail author
  • Xiaoyu Gu
  • Yifei Cui
Original Article


The phenomenon of alkali aggregate reactivity (AAR) in concrete structures corresponds to the reaction between aggregates with some ingredients and alkali hydroxide in concretes. This AAR could potentially lead to concrete deformation, micro-cracks and eventually wide visible cracks. In this study, to predict the expansion of the concrete caused by AAR, a novel hybrid model is proposed based on support vector machine (SVM). In the proposed model, the inputs are the aggregate components and concrete age, while the output is the induced expansion in the concrete. To improve the generalisation capacity of the proposed model, the enhanced particle swarm optimisation algorithm is employed to select optimal SVM parameters. The proposed method is evaluated and compared with other conventional soft computing methods based on the experimental data. Finally, the evaluated results endorse the effectiveness of the proposed hybrid model.


Alkali aggregate reactivity Concrete expansion Support vector machine Particle swarm optimisation 



This work was supported by Australian Research Council via Research Hub (IH150100006) for Nanoscience Based Construction Materials Manufacturing (NANOCOMM), the National Natural Science Foundation of China (Grant No. 51678322) and the first-class discipline project funded by the Education Department of Shandong Province. The authors are grateful to the funding bodies.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.


  1. 1.
    Isgor OB, Razaqpur AG (2004) Finite element modeling of coupled heat transfer, moisture transport and carbonation processes in concrete structures. Cement Concr Compos 26(1):57–73Google Scholar
  2. 2.
    Al Rikabi FT, Sargand SM, Khoury I, Hussein HH (2018) Material properties of synthetic fiber-reinforced concrete under freeze-thaw conditions. J Mater Civil Eng 30(6):04018090Google Scholar
  3. 3.
    Wu B, Li Z (2017) Mechanical properties of compound concrete containing demolished concrete lumps after freeze-thaw cycles. Constr Build Mater 155:187–199MathSciNetGoogle Scholar
  4. 4.
    Sanchez LFM, Fournier B, Jolin M, Mitchell D, 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 Concr Res 93:17–31Google Scholar
  5. 5.
    Kubat T, Al-Mahaidi R, Shayan A (2016) CFRP confinement of circular concrete columns affected by alkali-aggregate reaction. Constr Build Mater 116:98–109Google Scholar
  6. 6.
    Tian W, Han N (2017) Experiment analysis of concrete’s mechanical property deterioration suffered sulfate attack and drying-wetting cycles. Adv Mater Sci Eng 2017:5673985Google Scholar
  7. 7.
    Tang J, Cheng H, Zhang Q, Chen W, Li Q (2018) Development of properties and microstructure of concrete with coral reef sand under sulphate attack and drying-wetting cycles. Constr Build Mater 165:647–654Google Scholar
  8. 8.
    Shi C, Shi Z, Hu X, Zhao R, Chong L (2015) A review on alkali-aggregate reactions in alkali-activated mortars/concretes made with alkali-reactive aggregates. Mater Struct 48(3):621–628Google Scholar
  9. 9.
    Stanton TE (1940) Expansion of concrete through reaction between cement and aggregate. Trans Am Soc Civ Eng 107(1):54–84Google Scholar
  10. 10.
    Godart B, de Rooij MR, Wood JGM (2013) Guide to diagnosis and appraisal of AAR damage to concrete in structures. Springer, BerlinGoogle Scholar
  11. 11.
    Yang H, Li P, Li P (2017) Long term investigation and inhibition on alkali-aggregates reaction of Three Gorges Dam concrete. Constr Build Mater 151:673–681Google Scholar
  12. 12.
    Gautam BP, Panesar DK, Sheikh SA, Vecchio FJ (2017) Effect of multiaxial stresses on alkali-silica reaction damage of concrete. ACI Mater J 114(4):595–604Google Scholar
  13. 13.
    Grattan-Bellew PE, Chan G (2013) Comparison of the morphology of alkali-silica gel formed in limestones in concrete affected by the so-called alkali-carbonate reaction (ACR) and alkali-silica reaction (ASR). Cement Concr Res 47:51–54Google Scholar
  14. 14.
    Mazarei V, Trejo D, Ideker JH, Isgor OB (2017) Synergistic effects of ASR and fly ash on the corrosion characteristics of RC systems. Constr Build Mater 153:647–655Google Scholar
  15. 15.
    Krivenko P, Drochytka R, Gelevera A, Kavalerova E (2014) Mechanism of preventing the alkali-aggregate reaction in alkali activated cement concretes. Cement Concr Comp 45:157–165Google Scholar
  16. 16.
    Wallau W, Pirskawetz S, Voland K, Meng B (2018) Continuous expansion measurement in accelerated concrete prism testing for verifying ASR-expansion models. Mater Struct 51(3):79Google Scholar
  17. 17.
    Shehata MH, Jagdat S, Rogers C, Lachemi M (2017) Long-term effects of different cementing blends on alkali-carbonate reaction. ACI Mater J 114(4):661–672Google Scholar
  18. 18.
    Beauchemin S, Fournier B, Duchesne J (2018) Evaluation of the concrete prisms test method for assessing the potential alkali-aggregate reactivity of recycled concrete aggregates. Cement Concr Res 104:25–36Google Scholar
  19. 19.
    Choi YC, Choi S (2015) Alkali-silica reactivity of cementitious materials using ferro-nickel slag fine aggregates produced in different cooling conditions. Constr Build Mater 99:279–287Google Scholar
  20. 20.
    Gonzalez LM, Santos Silva A, Jalali S (2015) Results comparison of alkali-reactivity tests for same aggregates, using a kinetic model. Key Eng Mater 634:498–505Google Scholar
  21. 21.
    Naziemiec Z, Pabiś-Mazgaj E (2017) Preliminary evaluation of the alkali reactivity of crushed aggregates from glacial deposits in Northern Poland. Roads Bridges Drogi i Mosty 16(3):203–222Google Scholar
  22. 22.
    Kang F, Liu J, Li J, Li S (2017) Concrete dam deformation prediction model for health monitoring based on extreme learning machine. Struct Control Hlth 24(10):e1997Google Scholar
  23. 23.
    Jafari S, Mahini SS (2017) Lightweight concrete design using gene expression programming. Constr Build Mater 139:93–100Google Scholar
  24. 24.
    Golafshani EM, Rahai A, Sebt MH, Akbarpour H (2012) Prediction of bond strength of spliced steel bars in concrete using artificial neural network and fuzzy logic. Constr Build Mater 36:411–418Google Scholar
  25. 25.
    Suleiman AR, Nehdi ML (2017) Modeling self-healing of concrete using hybrid genetic algorithm-artificial neural network. Materials 10(2):135Google Scholar
  26. 26.
    Mansouri I, Gholampour A, Kisi O, Ozbakkaloglu T (2016) Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques. Neural Comput Appl 29(3):873–888Google Scholar
  27. 27.
    Bal L, Buyle-Bodin F (2014) Artificial neural network for predicting creep of concrete. Neural Comput Appl 25(6):1359–1367Google Scholar
  28. 28.
    Bal L, Buyle-Bodin F (2013) Artificial neural network for predicting drying shrinkage of concrete. Constr Build Mater 36:411–418Google Scholar
  29. 29.
    Mansouri I, Kisi O (2015) Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches. Compos Part B Eng 70:247–255Google Scholar
  30. 30.
    Sonebi M, Cevik A, Grünewald S, Walraven J (2016) Modelling the fresh properties of self-compacting concrete using support vector machine approach. Constr Build Mater 160:55–64Google Scholar
  31. 31.
    Mansouri I, Ozbakkaloglu T, Kisi O, Xie T (2016) Predicting behavior of FRP-confined concrete using neuro fuzzy, neural network, multivariate adaptive regression splines and M5 model tree techniques. Mater Struct 49(10):4319–4334Google Scholar
  32. 32.
    Mansouri I, Kisi O, Sadeghian P, Lee CH, Hu JW (2017) Prediction of ultimate strain and strength of FRP-confined concrete cylinders using soft computing methods. Appl Sci Basel 7(8):751Google Scholar
  33. 33.
    Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297zbMATHGoogle Scholar
  34. 34.
    Ab Wahab MN, Nefti-Meziani S, Atyabi A (2015) A comprehensive review of swarm optimization algorithms. PLoS ONE 10(5):e0122827Google Scholar
  35. 35.
    Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, Nagoya, Japan, 4–6 October, pp 39–43Google Scholar
  36. 36.
    Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Proceedings of the IEEE conference on evolutionary computation. Anchorage, USA, 4–9 May, pp 69–73Google Scholar
  37. 37.
    Fourie PC, Groenwold AA (2002) The particle swarm optimization algorithm in size and shape optimization. Struct Multidiscip O 23(4):259–267Google Scholar
  38. 38.
    Chaturvedi KT, Pandit M, Srivastava L (2009) Particle swarm optimization with crazy particles for nonconvex economic dispatch. Appl Soft Comput 9(3):962–969Google Scholar
  39. 39.
    Bondar D, Ganjian E (2013) Managing structural impacts by application of neural network to predict concrete expansion due to AAR. In: Proceedings of 81st annual meeting of international commission on large dams (ICOLD), Seattel, Washington, USAGoogle Scholar
  40. 40.
    Yu Y, Li Y, Li J, Gu X (2016) Self-adaptive step fruit fly algorithm optimized support vector regression model for dynamic response prediction of magnetorheological elastomer base isolator. Neurocomputing 211:41–52Google Scholar
  41. 41.
    Ye W, Feng W, Fan S (2017) A novel multi-swarm particle swarm optimization with dynamic learning strategy. Appl Soft Comput 61:832–843Google Scholar
  42. 42.
    Rubio JDJ, Elias I, Cruz DR, Pacheco J (2017) Uniform stable radial basis function neural network for the prediction in two mechatronic processes. Neurocomputing 227:122–130Google Scholar
  43. 43.
    Grande A, Hernández T, Curtidor V, Páramo A, Tapia R, Cázares IO, Meda JA (2017) Analysis of fuzzy observability property for a class of TS fuzzy models. IEEE Latin Am Trans 15(4):595–602Google Scholar
  44. 44.
    Rubio JDJ (2017) USNFIS: uniform stable neuro fuzzy inference system. Neurocomputing 262:57–66Google Scholar
  45. 45.
    Pan Y, Liu Y, Xu B, Yu H (2017) Hybrid feedback feedforward: an efficient design of adaptive neural network control. Neural Netw 76:122–134zbMATHGoogle Scholar
  46. 46.
    Rubio JDJ (2017) Stable Kalman filter and neural network for the chaotic systems identification. J Franklin I 354(16):7444–7462MathSciNetzbMATHGoogle Scholar
  47. 47.
    Pan Y, Yu H (2017) Biomimetic hybrid feedback feedforward neural-network learning control. IEEE Trans Neur Netw Learn Syst 28(6):1481–1487Google Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Centre for Built Infrastructure Research, School of Civil and Environmental EngineeringUniversity of Technology SydneySydneyAustralia
  2. 2.School of Civil EngineeringQingdao University of TechnologyQingdaoChina
  3. 3.Centre for Infrastructure EngineeringWestern Sydney UniversitySydneyAustralia

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