Skip to main content
SpringerLink
Log in

A new systematic firefly algorithm for forecasting the durability of reinforced recycled aggregate concrete

  • Research Article
  • Published:
Frontiers of Structural and Civil Engineering Aims and scope Submit manuscript

Abstract

This study presents a new systematic algorithm to optimize the durability of reinforced recycled aggregate concrete. The proposed algorithm integrates machine learning with a new version of the firefly algorithm called chaotic based firefly algorithm (CFA) to evolve a rational and efficient predictive model. The CFA optimizer is augmented with chaotic maps and Lévy flight to improve the firefly performance in forecasting the chloride penetrability of strengthened recycled aggregate concrete (RAC). A comprehensive and credible database of distinctive chloride migration coefficient results is used to establish the developed algorithm. A dataset composite of nine effective parameters, including concrete components and fundamental characteristics of recycled aggregate (RA), is used as input to predict the migration coefficient of strengthened RAC as output. k-fold cross validation algorithm is utilized to validate the hybrid algorithm. Three numerical benchmark analyses are applied to prove the superiority and applicability of the CFA algorithm in predicting chloride penetrability. Results show that the developed CFA approach significantly outperforms the firefly algorithm on almost tested functions and demonstrates powerful prediction. In addition, the proposed strategy can be an active tool to recognize the contradictions in the experimental results and can be especially beneficial for assessing the chloride resistance of RAC.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Shaban W M, Elbaz K, Yang J, Thomas B S, Shen X, Li L, Du Y, Xie J, Li L. Effect of pozzolan slurries on recycled aggregate concrete: Mechanical and durability performance. Construction & Building Materials, 2021, 276: 121940

    Article  Google Scholar 

  2. Ouyang K, Shi C, Chu H, Guo H, Song B, Ding Y, Guan X, Zhu J, Zhang H, Wang Y, Zheng J. An overview on the efficiency of different pretreatment techniques for recycled concrete aggregate. Journal of Cleaner Production, 2020, 263: 121264

    Article  Google Scholar 

  3. Guo H, Shi C, Guan X, Zhu J, Ding Y, Ling T C, Zhang H, Wang Y. Durability of recycled aggregate concrete—A review. Cement and Concrete Composites, 2018, 89: 251–259

    Article  Google Scholar 

  4. Shaban W M, Yang J, Su H, Mo K H, Li L, Xie J. Quality improvement techniques for recycled concrete aggregate: A review. Journal of Advanced Concrete Technology, 2019, 17(4): 151–167

    Article  Google Scholar 

  5. Shaban W M, Yang J, Su H, Liu Q F, Tsang D C W, Wang L, Xie J, Li L. Properties of recycled concrete aggregates strengthened by different types of pozzolan slurry. Construction & Building Materials, 2019, 216: 632–647

    Article  Google Scholar 

  6. Kisku N, Joshi H, Ansari M, Panda S K, Nayak S, Dutta S C. A critical review and assessment for usage of recycled aggregate as sustainable construction material. Construction & Building Materials, 2017, 131: 721–740

    Article  Google Scholar 

  7. Vázquez E, Barra M, Aponte D, Jiménez C, Valls S. Improvement of the durability of concrete with recycled aggregates in chloride exposed environment. Construction & Building Materials, 2014, 67: 61–67

    Article  Google Scholar 

  8. Xuan D X, Zhan B J, Poon C S. Durability of recycled aggregate concrete prepared with carbonated recycled concrete aggregates. Cement and Concrete Composites, 2013, 35: 32–38

    Article  Google Scholar 

  9. Kwon S J, Kim S C. Concrete mix design for service life of RC structures exposed to chloride attack. Computers and Concrete, 2012, 10(6): 587–607

    Article  Google Scholar 

  10. Zhuang X, Guo H, Alajlan N, Zhu H, Rabczuk T. Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning. European Journal of Mechanics. A-Solids, 2021, 87: 104225

    Article  MathSciNet  MATH  Google Scholar 

  11. Samaniego E, Anitescu C, Goswami S, Nguyen-Thanh V M, Guo H, Hamdia K, Zhuang X, Rabczuk T. An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications. Computer Methods in Applied Mechanics and Engineering, 2020, 362: 112790

    Article  MathSciNet  MATH  Google Scholar 

  12. Shaban W M, Yang J, Elbaz K, Xie J, Li L. Fuzzy-metaheuristic ensembles for predicting the compressive strength of brick aggregate concrete. Resources, Conservation and Recycling, 2021, 169: 105443

    Article  Google Scholar 

  13. Shaban W M, Elbaz K, Yang J, Shen S L. A multi-objective optimization algorithm for forecasting the compressive strength of RAC with pozzolanic materials. Journal of Cleaner Production, 2021, 327: 129355

    Article  Google Scholar 

  14. Anitescu C, Atroshchenko E, Alajlan N, Rabczuk T. Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials & Continua, 2019, 59(1): 345–359

    Article  Google Scholar 

  15. Guo H, Zhuang X, Rabczuk T. A deep collocation method for the bending analysis of kirchhoff plate. Computers, Materials & Continua, 2019, 59(2): 433–456

    Article  Google Scholar 

  16. Garson GD. Interpreting neural-network connection weights. AI Expert, 1991, 6(4): 47–51

    Google Scholar 

  17. Swingler K. Applying Neural Networks: A Practical Guide. New York, NY: Academic, 1996: 442

    Google Scholar 

  18. Derrac J, García S, Molina D, Herrera F. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 2011, 1(1): 3–18

    Article  Google Scholar 

  19. Elbaz K, Shen S L, Zhou A N, Yuan D J, Xu Y S. Optimization of EPB shield performance with adaptive neuro-fuzzy inference system and genetic algorithm. Applied Sciences-Basel, 2019, 9(4): 780

    Article  Google Scholar 

  20. Kohavi R. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In: International joint conference on Artificial intelligence. 1995, 1137–1143

  21. May R M. Simple mathematical models with very complicated dynamics. Nature, 1976, 261(5560): 459–467

    Article  MATH  Google Scholar 

  22. Elbaz K, Shen S L, Zhou A, Yin Z Y, Lyu H M. Prediction of disc cutter life during shield tunneling with AI via incorporation of genetic algorithm into GMDH-type neural network. Engineering, 2021, 7(2): 238–251

    Article  Google Scholar 

  23. Friedman M. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 1937, 32(200): 675–701

    Article  MATH  Google Scholar 

  24. Elbaz K, Shen S L, Sun W J, Yin Z Y, Zhou A N. Prediction model of shield performance during tunneling via incorporating improved particle swarm optimization into ANFIS. IEEE Access, 2020, 8: 39659–39671

    Article  Google Scholar 

  25. Miskony B, Wang D. Construction of prediction intervals using adaptive neurofuzzy inference systems. Applied Soft Computing, 2018, 72: 579–586

    Article  Google Scholar 

  26. Bui D K, Nguyen T, Chou J S, Nguyen-Xuan H, Ngo T D. A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of highperformance concrete. Construction & Building Materials, 2018, 180: 320–333

    Article  Google Scholar 

  27. Thomas K, Michal P, Adam V, Roman S. Firefly algorithm enhanced by orthogonal learning. In: 7th Computer Science On-Line Conference (CSOC), Artificial intelligence and algorithms in intelligent systems. Cham: Springer International Publishing, 2019, 477–488

    Google Scholar 

  28. Wang H, Wang W, Zhou X, Sun H, Zhao J, Yu X. Firefly algorithm with neighborhood attraction. Information Sciences, 2017, 383: 374–387

    Article  Google Scholar 

  29. Wang C F, Song W X. A novel firefly algorithm based on gender difference and its convergence. Applied Soft Computing, 2019, 80: 107–124

    Article  Google Scholar 

  30. Li G, Liu P, Le C, Zhou B. A novel hybrid meta-heuristic algorithm based on the cross-entropy method and firefly algorithm for global optimization. Entropy (Basel, Switzerland), 2019, 21(5): 494

    Article  MathSciNet  Google Scholar 

  31. Wang W, Xu L, Chau K, Xu D. Yin-Yang firefly algorithm based on dimensionally Cauchy mutation. Expert Systems with Applications, 2020, 150: 113216

    Article  Google Scholar 

  32. Zhang H, Zhao Y, Meng T, Shah S P. Surface treatment on recycled coarse aggregates with nanomaterials. Journal of Materials in Civil Engineering, 2016, 28(2): 04015094

    Article  Google Scholar 

  33. Yang J, Shaban W M, Elbaz K, Thomas B S, Xie J, Li L. Properties of concrete containing strengthened crushed brick aggregate by pozzolan slurry. Construction & Building Materials, 2020, 247: 118612

    Article  Google Scholar 

  34. Kou S C, Poon C S. Long-term mechanical and durability properties of recycled aggregate concrete prepared with the incorporation of fly ash. Cement and Concrete Composites, 2013, 37: 12–19

    Article  Google Scholar 

  35. Lotfy A, Al-Fayez M. Performance evaluation of structural concrete using controlled quality coarse and fine recycled concrete aggregate. Cement and Concrete Composites, 2015, 61: 36–43

    Article  Google Scholar 

  36. Somna R, Jaturapitakkul C, Chalee W, Rattanachu P. Effect of the water to binder ratio and ground fly ash on properties of recycled aggregate concrete. Journal of Materials in Civil Engineering, 2012, 24(1): 16–22

    Article  Google Scholar 

  37. Mousa M I, Mahdy M G, Abdel-Reheem A H, Yehia A Z. Self-curing concrete types: water retention and durability. Alexandria Engineering Journal, 2015, 54(3): 565–575

    Article  Google Scholar 

  38. Kou S C, Poon C S, Chan D. Influence of fly ash as a cement addition on the hardened properties of recycled aggregate concrete. Materials and Structures, 2008, 41(7): 1191–1201

    Article  Google Scholar 

  39. Bui N K, Satomi T, Takahashi H. Mechanical properties of concrete containing 100% treated coarse recycled concrete aggregate. Construction & Building Materials, 2018, 163: 496–507

    Article  Google Scholar 

  40. Vakhshouri B, Nejadi S. Prediction of compressive strength of self-compacting concrete by ANFIS models. Neurocomputing, 2018, 280: 13–22

    Article  Google Scholar 

  41. Jang J S. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, 1993, 23(3): 665–685

    Google Scholar 

  42. Jang J S R, Sun C T, Mizutani E. Neuro-fuzzy and soft computing-A computational approach to learning and machine intelligence. IEEE Transactions on Automatic Control, 1997, 42(10): 1482–1484

    Article  Google Scholar 

  43. Taherdangkoo M, Bagheri M H. A powerful hybrid clustering method based on modified stem cells and Fuzzy C-means algorithms. Engineering Applications of Artificial Intelligence, 2013, 26(5–6): 1493–1502

    Article  Google Scholar 

  44. Yang X S. Firefly algorithm, stochastic test functions and design optimization. International Journal of Bio-inspired Computation, 2010, 2(2): 78–84

    Article  Google Scholar 

  45. Wang H, Wang W, Sun H, Rahnamayan S. Firefly algorithm with random attraction. International Journal of Bio-inspired Computation, 2016, 8(1): 33–51

    Article  Google Scholar 

  46. Yu S, Su S, Lu Q, Huang L. A novel wise step strategy for firefly algorithm. International Journal of Computer Mathematics, 2014, 91(12): 2507–2513

    Article  MathSciNet  MATH  Google Scholar 

  47. Coelho L D S, Mariani V C. Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization. Expert Systems with Applications, 2008, 34(3): 1905–1913

    Article  Google Scholar 

  48. Gandomi A H, Yang X S, Talatahari S, Alavi A H. Firefly algorithm with chaos. Communications in Nonlinear Science and Numerical Simulation, 2013, 18(1): 89–98

    Article  MathSciNet  MATH  Google Scholar 

  49. Zhou W H, Garg A, Garg A. Study of the volumetric water content based on density, suction and initial water content. Measurement, 2016, 94: 531–537

    Article  Google Scholar 

  50. Jiang P, Chen J. Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation. Neurocomputing, 2016, 198: 40–47

    Article  Google Scholar 

  51. Bravo M, de Brito J, Pontes J, Evangelista L. Durability performance of concrete with recycled aggregates from construction and demolition waste plants. Construction & Building Materials, 2015, 77: 357–369

    Article  Google Scholar 

  52. Kou S C, Poon C S. Effect of the quality of parent concrete on the properties of high performance recycled aggregate concrete. Construction & Building Materials, 2015, 77: 501–508

    Article  Google Scholar 

  53. Ann K Y, Moon H Y, Kim Y B, Ryou J. Durability of recycled aggregate concrete using pozzolanic materials. Waste Management, 2008, 28(6): 993–999

    Article  Google Scholar 

  54. Kong D, Lei T, Zheng J, Ma C, Jiang J, Jiang J. Effect and mechanism of surface-coating pozzolanic materials around aggregate on properties and ITZ microstructure of recycled aggregate concrete. Construction & Building Materials, 2010, 24(5): 701–708

    Article  Google Scholar 

  55. Kou S C, Poon C S, Chan D. Influence of fly ash as cement replacement on the properties of recycled aggregate concrete. Journal of Materials in Civil Engineering, 2007, 19(9): 709–717

    Article  Google Scholar 

  56. Kou S C, Poon C S, Agrela F. Comparisons of natural and recycled aggregate concretes prepared with the addition of different mineral admixtures. Cement and Concrete Composites, 2011, 33(8): 788–795

    Article  Google Scholar 

  57. Kou S C, Poon C S. Enhancing the durability properties of concrete prepared with coarse recycled aggregate. Construction & Building Materials, 2012, 35: 69–76

    Article  Google Scholar 

Download references

Acknowledgments

The research work was funded by “The Pearl River Talent Recruitment project” in 2019 (No. 2019CX01G338), Guangdong Province & Shantou University Research Funding for New Faculty Member (No. NTF19024-2019).

Author information

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, College of Engineering, Shantou University, Shantou, 515063, China

    Wafaa Mohamed Shaban & Khalid Elbaz

  2. Department of Civil Engineering, Misr Higher Institute of Engineering and Technology, Mansoura, Egypt

    Wafaa Mohamed Shaban

  3. Department of Civil Engineering, Higher Future Institute of Engineering and Technology, Mansoura, Egypt

    Khalid Elbaz

  4. Guangdong Engineering Center for Structure Safety and Health Monitoring, Shantou University, Shantou, 515063, China

    Khalid Elbaz

  5. Civil and Architectural Construction Department, Faculty of Technology and Education, Suez University, Ismailia, Egypt

    Mohamed Amin

  6. Department of Civil and Environmental Engineering, Collage of Engineering, University of Sharjah, Sharjah, 27272, UAE

    Ayat Gamal Ashour

Authors
  1. Wafaa Mohamed Shaban
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Khalid Elbaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohamed Amin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ayat Gamal Ashour
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Khalid Elbaz.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shaban, W.M., Elbaz, K., Amin, M. et al. A new systematic firefly algorithm for forecasting the durability of reinforced recycled aggregate concrete. Front. Struct. Civ. Eng. 16, 329–346 (2022). https://doi.org/10.1007/s11709-022-0801-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11709-022-0801-9

Keywords

Search

Navigation