An optimization-based methodology for the definition of amplitude thresholds of the ground penetrating radar

  • Eslam Mohammed AbdelkaderEmail author
  • Mohamed Marzouk
  • Tarek Zayed
Methodologies and Application


Existing infrastructure is aging, while the demands are growing for a better infrastructure system in response to the high standards of safety, health, population growth, and environmental protection. Bridges are subjected to severe deterioration agents such as variable traffic loading, deferred maintenance, cycles of freeze and thaw. The development of Bridge Management Systems (BMSs) has become a fundamental imperative nowadays due to the huge variance between the need for maintenance actions and the available funds to perform such actions. Condition assessment is regarded as one of the most critical and vital components of BMSs. Ground penetrating radar (GPR) is one of the nondestructive techniques that are used to evaluate the condition of bridge decks which are subjected to the rebar corrosion. There is a major issue associated with the GPR which is the absence of a scale for the amplitude values. The objective of the proposed model is to compute standardized amplitude thresholds for corrosion maps. The proposed model considers eight un-supervised clustering algorithms to obtain the thresholds. The proposed model incorporates a multi-objective optimization-based methodology that employs three evolutionary optimization algorithms to calculate the optimum thresholds which are: (1) genetic algorithm, (2) particle swarm optimization algorithm, and (3) shuffled frog-leaping algorithm. Five multi-criteria decision-making techniques are used to provide a ranking for the solutions. Finally, group decision-making is performed to aggregate the results and obtain a consensus and compromise solution. The standardized thresholds obtained from the proposed methodology are: − 16.7619, − 8.8161, and − 2.9744 dB.


Bridge Management System Ground penetrating radar Nondestructive techniques Corrosion Amplitude thresholds Evolutionary optimization algorithms Multi-criteria decision-making 



This project was funded by the Academy of Scientific Research and Technology (ASRT), Egypt, JESOR-Development Program—Project ID: 40.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The article does not contain any studies with human participants or animals performed by any of the authors.


  1. AASHTO Highways Subcommittee on Bridges and Structures (2011) The manual for bridge evaluation, 2nd edn. American Association of State Highway and Transportation Officials, Washington, DCGoogle Scholar
  2. Akyene T (2012) Cell phone evaluation base on entropy and TOPSIS. Interdiscip J Res Bus 1(12):9–15Google Scholar
  3. Baltar AM, Fontane DG (2008) Use of multiobjective particle swarm optimization in water resources management. J Water Resour Plan Manag 134(3):257–265CrossRefGoogle Scholar
  4. Barnes CL, Trottier J-F, Forgeron D (2008) Improved concrete bridge deck evaluation using GPR by accounting for signal depth-amplitude effects. NDT&E Int 41(6):427–433CrossRefGoogle Scholar
  5. Caldas LG, Norford LK (2002) A design optimization tool based on a genetic algorithm. Autom Constr 11(2):173–184CrossRefGoogle Scholar
  6. Cristóbal SJR (2011) Multi-criteria decision-making in the selection of a renewable energy project in Spain: the VIKOR method. Renew Energy 36(2):498–502. CrossRefGoogle Scholar
  7. Czepiel E (1995) Bridge management systems literature review and search. Northwestern University, EvanstonGoogle Scholar
  8. Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 1(2):224–227CrossRefGoogle Scholar
  9. Dinh K, Zayed T (2016) GPR-based fuzzy model for bridge deck corrosiveness index. J Perform Constr Facil 30(4):1–14CrossRefGoogle Scholar
  10. Dobbs R, Pohl H, Lin D-Y, Mischke J, Garemo N, Hexter J, Matzinger S, Palter R, Nanavatty R (2013) Infrastructure productivity: how to save $1 trillion a year. Mckinsey Global Institute, CanadaGoogle Scholar
  11. Dragisa S, Bojan D, Mira D (2013) Comparative analysis of some prominent MCDM methods: a case of ranking Serbian banks. Serb J Manag 8(2):213–241CrossRefGoogle Scholar
  12. Dunn JC (1974) Well-separated clusters and optimal fuzzy partitions. J Cybern 4(1):95–104MathSciNetCrossRefzbMATHGoogle Scholar
  13. Elbeltagi E, Hegazy T, Grierson D (2005) Comparison among five evolutionary-based optimization algorithms. Adv Eng Inform 19(1):43–53CrossRefGoogle Scholar
  14. Felio G (2016) Canadian infrastructure report card. Canadian Construction Association, Canadian Public Works Association, Canadian Society for Civil Engineering, and Federation of Canadian Municipalities, CanadaGoogle Scholar
  15. Fornell C (1983) Issues in the application of covariance structure analysis: a comment. J Consum Res 9(4):443–448CrossRefGoogle Scholar
  16. Fu G, Kapelan Z, Reed P (2012) Reducing the complexity of multi-objective water distribution system optimization through global sensitivity analysis. J Water Resour Plan Manag 138(3):196–207CrossRefGoogle Scholar
  17. Garg R, Mittal S (2014) Optimization by genetic algorithm. Int J Adv Res Comput Sci Softw Eng 4:3–5Google Scholar
  18. Golden Software LLC (2014) Surfer12 software. Accessed 05 June 2016
  19. Grant T, Urszula C, Reershemius G, Pollard D, Hayes S, Plappert G (2017) Quantitative research methods for linguists: a questions and answers approach for students. Taylor & Francis, RoutledgeCrossRefGoogle Scholar
  20. Heidari E, Movaghar A (2011) An efficient method based on genetic algorithm to solve sensor network optimization problem. Int J Appl Graph Theory Wirel Ad hoc Netw Sens Netw 3(1):18–33Google Scholar
  21. Ishibuchi H, Hiroyuki M, Yuki T, Yusuke N (2014) Difficulties in specifying reference points to calculate the inverted generational distance for many-objective optimization problems. In: 2014 IEEE symposium on computational intelligence in multi-criteria decision-making (MCDM), Florida, United States of America, pp 1–14Google Scholar
  22. Keskin GA (2015) Using integrated fuzzy DEMATEL and Fuzzy C: means algorithm for supplier evaluation and selection. Int J Prod Res 53(12):3586–3602CrossRefGoogle Scholar
  23. KNIME Company (2016) KNIME 3.3.1 software. Accessed 06 Aug 2016
  24. Kühn M, Severin T, Salzwedel H (2013) Variable mutation rate at genetic algorithms: introduction of chromosome fitness in connection with multi-chromosome representation. Int J Comput Appl 72:31–38Google Scholar
  25. Kumar DN, Reddy MJ (2007) Multipurpose reservoir operation using particle swarm optimization. J Water Resour Plan Manag 133(3):192–201CrossRefGoogle Scholar
  26. Kuo Y, Yang T, Huang GW (2008) The use of grey relational analysis In solving multiple attribute decision-making problems. Comput Ind Eng 55(1):80–93. CrossRefGoogle Scholar
  27. Mackenzie H (2013) Canada’s infrastructure gap: where it came from and why it will cost so much to close. Canadian Centre for Policy Alternatives, OttawaGoogle Scholar
  28. Martino N, Maser K, Birken R, Wang M (2016) Quantifying bridge deck corrosion using ground penetrating radar. Res Nondestr Eval 27(2):112–124CrossRefGoogle Scholar
  29. Mashwani KN, Salhi A (2016) Multiobjective evolutionary algorithm based on multimethod with dynamic resources allocation. Appl Soft Comput 39:292–309. CrossRefGoogle Scholar
  30. MathWorks (2013) Matlab R2013a software. Accessed 06 Sept 2014
  31. Mirza S (2007) Danger ahead: the coming collapse of canada’s municipal infrastructure. Federation of Canadian Municipalities, OttawaGoogle Scholar
  32. Mulliner E, Smallbone K, Maliene V (2013) An assessment of sustainable housing affordability using a multiple criteria decision making method. Omega 41(2):270–279CrossRefGoogle Scholar
  33. Nebro AJ, Durillo JJ, Coello CAC (2013) Analysis of leader selection strategies in a multi-objective particle swarm optimizer. In: 2013 IEEE congress on evolutionary computation, Cancún, pp 3153–3160Google Scholar
  34. Orouji H, Mahmoudi N, Pazoki M, Biswas A (2016) Shuffled frog-leaping algorithm for optimal design of open channels. J Irrig Drain Eng 142(10):06016008CrossRefGoogle Scholar
  35. Pourbahman Z, Hamzeh A (2015) A fuzzy based approach for fitness approximation in multi-objective evolutionary algorithms. J Intell Fuzzy Syst 29:2111–2131CrossRefGoogle Scholar
  36. RapidMiner Inc (2016) RapidMiner 7.5 software. Accessed 06 Aug 2016
  37. Riquelme N, Lucken C, Baran B (2015) Performance metrics in multi-objective optimization. In: 2015 XLI Latin American Computing Conference (CLEI), Peru, pp 1–11Google Scholar
  38. Sahani R, Bhuyan PK (2014) Pedestrian level of service criteria for urban off-street facilities in mid-sized cities. Transport 32(2):221–232CrossRefGoogle Scholar
  39. Sawant KB (2015) Efficient determination of clusters in K-mean algorithm using neighborhood distance. Int J Emerg Eng Res Technol 3(1):22–27Google Scholar
  40. Shami A (2015) Ground penetrating radar-based deterioration assessment of bridge decks. M.Sc thesis, Concordia UniversityGoogle Scholar
  41. Statistics Canada (2009a) Age of public infrastructure: a provincial perspective. Accessed 20 Dec 2016
  42. Statistics Canada (2009b) Average age of public infrastructure by province and type of infrastructure, 2007. Accessed 20 Dec 2016
  43. Statistics Canada (2014) From roads to rinks: government spending on infrastructure in Canada, 1961 to 2005. Accessed 20 Dec 2016
  44. Venkatesan T, Sanavullah MY (2013) SFLA approach to solve PBUC problem with emission limitation. Electr Power Energy Syst 46:1–9CrossRefGoogle Scholar
  45. Wang L, Fang C (2011) An effective shuffled frog-leaping algorithm for multi-mode resource-constrained project scheduling problem. Inf Sci 181(20):4804–4822MathSciNetCrossRefzbMATHGoogle Scholar
  46. Yang I (2007) Using elitist particle swarm optimization to facilitate bicriterion time-cost trade-off analysis. J Constr Eng Manag 133(7):498–505CrossRefGoogle Scholar
  47. Zhang H, Li H (2010) Multi-objective particle swarm optimization for construction time-cost tradeoff problems. Constr Manag Econ 28(1):75–88MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Eslam Mohammed Abdelkader
    • 1
    • 2
    Email author
  • Mohamed Marzouk
    • 3
  • Tarek Zayed
    • 4
  1. 1.Department of Building, Civil, and Environmental EngineeringConcordia UniversityMontrealCanada
  2. 2.Structural Engineering Department, Faculty of EngineeringCairo UniversityGizaEgypt
  3. 3.Construction Engineering and Management, Structural Engineering Department, Faculty of EngineeringCairo UniversityGizaEgypt
  4. 4.Construction and Real Estate DepartmentThe Hong Kong Polytechnic UniversityHung HomHong Kong

Personalised recommendations