Skip to main content
SpringerLink
Log in

Application of artificial neural network (ANN) for estimating reliable service life of reinforced concrete (RC) structure bookkeeping factors responsible for deterioration mechanism

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Degradation of RC structures due to corrosion induced mechanism in the reinforcing steel is a serious durability problem worldwide. It occurs essentially when the reinforcement within the concrete is subjected to marine or aggressive environment. The aim of the present work is to predict the reliable service life of the RC structures by taking into consideration of various prominent models of corrosion and comparing the output with the predicted output of ANN model. Parametric studies have been conducted on four different models to study the effect of various parameters such as corrosion rate, cover thickness, bar diameter, and perimeter of bar which actively participates in the time dependent degradation of RC structures. The outcomes of the parametric inspection of the four chosen degradation models are shown in the present study. The acceptability of the prediction models in forecasting the service life of RC structures are shown through circumstantial illustrative analysis and the best suited model sorted out. However, with the application of soft computing such as ANN, a prediction has been made to determine the service life of RC structures, and the predicted outputs validated with the intended outputs thereby yielding good outcomes for envisaging service life of RC structure.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  • Ahmad S (2003) Reinforced corrosion in concrete structures, its monitoring and service life prediction a review. Cem Concr Compos 25(4–5):459–471

    Article  Google Scholar 

  • Bazant ZP (1979) Physical model for steel corrosion in concrete sea structures-application. J Struct Div 105(6):1155–1166

    Google Scholar 

  • Beeby AW (1978) Corrosion of reinforcing steel in concrete and its relation to cracking. Struct Eng 56A(3):77–81

    Google Scholar 

  • Bhargava K, Ghosh AK, Mori Y, Ramanujam S (2005) Modeling of time to corrosion induced cover cracking in reinforced concrete structures. Cem Concr Res 35(11):2203–2218

    Article  Google Scholar 

  • Bhargava K, Ghosh AK, Mori Y, Ramanujam S (2006) Analytical model for time to cover cracking in RC structures due to rebar corrosion. Nucl Eng Des 236(11):1123–1139

    Article  Google Scholar 

  • Cardoso JB, de Almeida JR, Dias JM, Coelho PG (2008) Structural reliability analysis using Monte Carlo simulation and neural networks. Adv Eng Softw 39(6):505–513

    Article  Google Scholar 

  • Choe DE, Gardoni P, Rosowky D, Haukaas T (2008) Probabilistic capacity models and seismic fragility estimates for RC columns subject to corrosion. Reliab Eng Syst Saf 93(3):383–393

    Article  Google Scholar 

  • Chojaczyk AA, Teixeira AP, Neves LC, Cardosa JB, Guedes SC (2015) Review and application of artificial neural networks models in reliability analysis of steel structures. Struct Saf 52A:78–89

    Article  Google Scholar 

  • Cusson D, Lounis Z, Daigle L (2010) Benefits of internal curing on service life and life-cycle cost of high-performance concrete bridge decks—a case study. Cem Concr Compos 32(5):339–350

    Article  Google Scholar 

  • D’Angelo G, Laracca M, Rampone S (2016) Automated Eddy current non-destructive testing through low definition Lissajous figures. IEEE Metrol Aerosp. https://doi.org/10.1109/MetroAeroSpace.2016.7573227

    Article  Google Scholar 

  • D’Angelo G, Laracca M, Rampone S, Betta G (2018) Fast eddy current testing defect classification using Lissajous figures. IEEE Trans Instrum Meas 67(4):1–10. https://doi.org/10.1109/TIM.2018.2792848

    Article  Google Scholar 

  • Hassan JE, Bressollette P, Chateauneuf A, Tawil KE (2010) Reliability-based assessment of the effect of climatic conditions on the corrosion of RC structures subject to chloride ingress. Eng Struct 32(10):3279–3287

    Article  Google Scholar 

  • Haykin S (2005) Neural networks. Pearson Education, Hamilton

    MATH  Google Scholar 

  • Imam A, Anifowose F, Azad AK (2015) Residual strength of corroded reinforced concrete beams using an adaptive model based on ANN. Int J Concr Struct Mater 9(2):159–172

    Article  Google Scholar 

  • IRC SP 60 (2002) An approach document for assessment of remaining life of concrete bridges

  • Liang MT, Wang KL, Liang CH (1999a) Service life prediction of reinforced concrete structures. Cem Concr Res 29:1411–1418

    Article  Google Scholar 

  • Liang MT, Hong CL, Liang CH (1999b) Service life prediction of existing reinforced concrete structures under carbonation-induced corrosion. J Chin Inst Civ Hydraul Eng 11(3):485–492

    Google Scholar 

  • Liang MT, Lin LH, Liang CH (2002) Service life prediction of existing reinforced concrete bridges exposed to chloride environment. J Infrastruct Syst ASCE 8(3):76–85

    Article  Google Scholar 

  • Maaddawy TE, Soudki K (2007) A model for prediction of time from corrosion initiation to corrosion cracking. Cem Concr Compos 29(3):168–175

    Article  Google Scholar 

  • Mangat PS, Elgarf MS (1999) Bond characteristics of corroding reinforcement in concrete beams. Mater Struct 32:89–97

    Article  Google Scholar 

  • McCulloch WS, Pitts WH (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133

    Article  MathSciNet  Google Scholar 

  • Ming-Te L, Ran H, Shen-An F, Chi-Jang Y (2009) Service life prediction of pier for the existing reinforced concrete bridges in chloride-laden environment. J Mar Sci Technol 17(4):312–319

    Google Scholar 

  • Moaveni B, Conte JP, Hemez FM (2009) Uncertainty and sensitivity analysis of damage identification results obtained using finite element model updating. Comput Aided Civ Infrastruct Eng 24(5):320–334

    Article  Google Scholar 

  • Morinaga S (1988) Prediction of service lives of reinforced concrete buildings based on rate of corrosion of reinforcing steel. Report No. 23. Shimizu Corporation, Japan, pp 82–89

  • Papadrakakis M, Lagaros ND (2002) Reliability-based structural optimization using neural networks and Monte Carlo simulation. Comput Methods Appl Mech Eng 191(32):3491–3507

    Article  Google Scholar 

  • Saad T, Fu CC (2015) Determining remaining strength capacity of deteriorating RC bridge substructures. J Perform Constr Facil 29(5):1–12

    Article  Google Scholar 

  • Sadiqual Islam GM (2009) Determination of the threshold chloride concentration for corrosion of steel in concrete by half-cell potential monitoring. Division of built environment

  • Tuutti K (1982) Corrosion of steel in concrete report 4-82. Swedish Cement and Concrete Research Institute, Sweden

  • Val DV, Melchers RE (1997) Reliability of deteriorating RC slab bridges. J Struct Eng 123(12):1638–1644

    Article  Google Scholar 

  • Vu KAT, Stewart MG (2005) Predicting the likelihood and extent of reinforced concrete corrosion-induced cracking. J Struct Eng 131(11):1681–1689

    Article  Google Scholar 

  • Wang XM, Zhao HY (1993) The residual service life prediction of RC structures. In: Nagataki S (ed) Durability of building materials and components, vol 6. E & FN Spon, New York, pp 1107–1114

    Google Scholar 

  • Weyers RE (1998) Service life model for concrete structure in chloride laden environments. ACI Mater J 95(4):445–453

    Google Scholar 

  • Zarola A, Sil A (2017) Artificial neural networks (ANN) and stochastic techniques to estimate earthquake occurrences in Northeast region of India. Ann Geophys 60(4):1–37

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, NIT Silchar, Silchar, Assam, 788010, India

    Abhijeet Dey, Ghanashyam Miyani & Arjun Sil

Authors
  1. Abhijeet Dey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ghanashyam Miyani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arjun Sil
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arjun Sil.

Ethics declarations

Conflict of interest

All the authors declare that they have no conflict of interest.

Animal rights statement

This article does not contain any studies with animals performed by any of the authors.

Ethical approval

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

Additional information

Communicated by V. Loia.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dey, A., Miyani, G. & Sil, A. Application of artificial neural network (ANN) for estimating reliable service life of reinforced concrete (RC) structure bookkeeping factors responsible for deterioration mechanism. Soft Comput 24, 2109–2123 (2020). https://doi.org/10.1007/s00500-019-04042-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-019-04042-y

Keywords

Search

Navigation