Skip to main content
SpringerLink
Log in

Data driven models for compressive strength prediction of concrete at high temperatures

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

Abstract

The use of data driven models has been shown to be useful for simulating complex engineering processes, when the only information available consists of the data of the process. In this study, four data-driven models, namely multiple linear regression, artificial neural network, adaptive neural fuzzy inference system, and K nearest neighbor models based on collection of 207 laboratory tests, are investigated for compressive strength prediction of concrete at high temperature. In addition for each model, two different sets of input variables are examined: a complete set and a parsimonious set of involved variables. The results obtained are compared with each other and also to the equations of NIST Technical Note standard and demonstrate the suitability of using the data driven models to predict the compressive strength at high temperature. In addition, the results show employing the parsimonious set of input variables is sufficient for the data driven models to make satisfactory results.

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. Düğenci O, Haktanir T, Altun F. Experimental research for the effect of high temperature on the mechanical properties of steel fiber-reinforced concrete. Construction & Building Materials, 2015, 75: 82–88

    Google Scholar 

  2. Choel G, Kim G, Gucunski N, Lee S. Evaluation of the mechanical properties of 200 MPa ultra-high-strength concrete at elevated temperatures and residual strength of column. Construction & Building Materials, 2015, 86: 159–168

    Google Scholar 

  3. Ergün A, Kürklü G M, Serhat B, Mansour M Y. The effect of cement dosage on mechanical properties of concrete exposed to high temperatures. Fire Safety Journal, 2013, 55: 160–167

    Google Scholar 

  4. Handoo S K, Agarwal S, Agarwal S K. Physicochemical, mineralogical, and morphological characteristics of concrete exposed to elevated temperatures. Cement and Concrete Research, 2002, 32(7): 1009–1018

    Google Scholar 

  5. Lil M, Qian C, Sun W. Mechanical properties of high-strength concrete after fire. Cement and Concrete Research, 2004, 34(6): 1001–1005

    Google Scholar 

  6. Balázs G L, Lubloy E. Post-heating strength of fiber-reinforced concretes. Fire Safety Journal, 2012, 49: 100–106

    Google Scholar 

  7. Tanyildizi H. Variance analysis of crack characteristics of structural lightweight concrete containing silica fume exposed to high temperature. Construction & Building Materials, 2013, 47: 1154–1159

    Google Scholar 

  8. Hamdia K M, Arafa M, Alqedra M. Structural damage assessment criteria for reinforced concrete buildings by using a Fuzzy Analytic Hierarchy Process. Underground Space, 2018, 3(3): 243–249

    Google Scholar 

  9. Cülfik M S, Özturan T. Mechanical properties of normal and high strength concretes subjected to high temperatures and using image analysis to detect bond deteriorations. Construction & Building Materials, 2010, 24(8): 1486–1493

    Google Scholar 

  10. Venecanin S D. Thermal incompatibility of concrete components and thermal properties of carbonate rocks. ACI Materials Journal, 1990, 87: 602–607

    Google Scholar 

  11. Yüzer N, Aköz F, Öztürk L D. Compressive strength-color change relation in mortars at high temperature. Cement and Concrete Research, 2004, 34(10): 1803–1807

    Google Scholar 

  12. Crook D N, Murray M J. Regain of strength after firing of concrete. Magazine of Concrete Research, 1970, 22(72): 149–154

    Google Scholar 

  13. Petzoldl A, Rohrs M. Concrete for High Temperatures. Lincoln: Maclaren and Sons Ltd., 1970

    Google Scholar 

  14. Ožbolt J, Bošnjak J, Periškić G, Sharma A. 3D numerical analysis of reinforced concrete beams exposed to elevated temperature. Engineering Structures, 2014, 58: 166–174

    Google Scholar 

  15. Caggianol A, Etse G. Coupled thermo-mechanical interface model for concrete failure analysis under high temperature. Computer Methods in Applied Mechanics and Engineering, 2015, 289: 498–516

    MathSciNet  MATH  Google Scholar 

  16. Rabczukl T, Zi G, Bordas S, Nguyen-Xuan H. A geometrically nonlinear three-dimensional cohesive crack method for reinforced concrete structures. Engineering Fracture Mechanics, 2008, 75(16): 4740–4758

    Google Scholar 

  17. Rabczukl T, Zi G, Bordas S, Nguyen-Xuan H. A simple and robust three-dimensional cracking-particle method without enrichment. Computer Methods in Applied Mechanics and Engineering, 2010, 199(37-40): 2437–2455

    MATH  Google Scholar 

  18. Rabczukl T, Belytschko T. A three-dimensional large deformation meshfree method for arbitrary evolving cracks. Computer Methods in Applied Mechanics and Engineering, 2007, 196(29-30): 2777–2799

    MathSciNet  MATH  Google Scholar 

  19. Rabczukl T, Belytschko T. Application of particle methods to static fracture of reinforced concrete structures. International Journal of Fracture, 2006, 137(1-4): 19–49

    MATH  Google Scholar 

  20. Rabczukl T, Bordas S, Zi G. On three-dimensional modelling of crack growth using partition of unity methods. Computers & Structures, 2010, 88(23-24): 1391–1411

    Google Scholar 

  21. Rabczukl T, Belytschko T. Cracking particles: A simplified meshfree method for arbitrary evolving cracks. International Journal for Numerical Methods in Engineering, 2004, 61(13): 2316–2343

    MATH  Google Scholar 

  22. Yeginoball A, Sobolev K G, Soboleva S V, Kiyici B. Thermal resistance of blast furnace slag high strength concrete cement. In: The First International symposium on mineral admixtures in cement. Istanbul: Turkish Cement Manufacturers Association, 1997, 106–117

    Google Scholar 

  23. Saemann J G, Washa G W. Variation of mortar and concrete properties with temperature. ACI Journal Proceedings, 1997, 54: 385–395

    Google Scholar 

  24. Gustaferro A H, Selvaggio S L. Fire endurance of simply supported prestressed concrete slabs. Journal-Prestressed Concrete Institute, 1967, 12(1): 37–52

    Google Scholar 

  25. Schneider U. Concrete at high temperatures—A general review. Fire Safety Journal, 1988, 13(1): 55–68

    MathSciNet  Google Scholar 

  26. Castillol C, Durrani A J. Effects of transient high temperature on high strength concrete. ACI Materials Journal, 1990, 87: 47–53

    Google Scholar 

  27. Shah S P, Ahmad S H. High Performance Concrete: Properties and Applications. New York: McGraw-Hill, 1994

    Google Scholar 

  28. Poon C S, Azhar S, Anson M, Wong Y L. Performance of metakolin concrete at elevated temperatures. Cement and Concrete Composites, 2003, 25(1): 83–89

    Google Scholar 

  29. Phan L T. Fire Performance of High Strength Concrete: A Report of the State-of the-Art. Maryland: Building and Fire Research Laboratory, National Institute of Standards and Technology, 1996

    Google Scholar 

  30. Abrams M S. Compressive Strength of Concrete at Temperatures to 1600F. Detroit: American Concrete Institute (ACI) SP 25, Temperature and Concrete, 1971

    Google Scholar 

  31. Malhotra H L. The effect of temperature on the compressive strength of concrete. Magazine of Concrete Research, 1956, 8(23): 85–94

    Google Scholar 

  32. Morital T, Saito H, Kumagai H. Residual mechanical properties of HSC members exposed to high temperature—Part 1: Test on mechanical properties, summaries of annual meeting. In: Summaries of Annual Meeting. London: Architectural Institute of Japan, 1992

    Google Scholar 

  33. Euro-International Committee for Concrete. Fire design of concrete structures-in accordance with CEB/FIB Model Code 90. London: Euro-International Committee for Concrete, 1991

    Google Scholar 

  34. European Committee for Standardisation. prENV1992-1-2: Eurocode 2: Design of Concrete Structures. Parts 1-2: Structural Fire Design, CEN/TC 250/SC 2. Brusseles: European Committee for Standardisation, 1993

    Google Scholar 

  35. European Committee for Standardisation. Eurocode 4: Design of Composite Steel and concrete Structures. Parts 1-2: General Rules-Structural Fire Design, CEN ENV. Brusseles: European Committee for Standardisation, 1994

    Google Scholar 

  36. Concrete Association of Finland. High Strength Concrete Supplementary Rules and Fire Design, RakMK B4. Finland: Concrete Association of Finland, 1991

    Google Scholar 

  37. ACI 216.1. Standard Method for Determining Fire Resistance of Concrete and Masonry Construction Assemblies (AC-216.1-07/TMS 0216.1-07). Farmington Hills, MI: American Concrete Institute, 2007

  38. BS EN 1992-1-2:2004. Eurocode 2: Design of Concrete Structures, General Rules, Structural Fire Design. Brussels: European Committee for Standardization, 2005

  39. Tsivilisl S, Parissakis G. Amathematical-model for the prediction of cement strength. Cement and Concrete Research, 1995, 25(1): 9–14

    Google Scholar 

  40. de Siqueira Tango C E. An extrapolation method for compressive strength prediction of hydraulic cement products. Cement and Concrete Research, 1998, 28(7): 969–983

    Google Scholar 

  41. Anderson D A, Seals R K. Pulse velocity as a predictor of 28- and 90-day strength. ACI Journal Proceedings, 1981, 78: 116–122

    Google Scholar 

  42. Phan L T, McAllister T P, Gross J L, Hurley M J. Best Practice Guidelines for Structural Fire Resistance Design of Concrete and Steel Buildings NIST Technical Note 1681. Gaithersburg, MD: National Institute of Standards and Technology, 2010

    Google Scholar 

  43. Duan Z H, Kou S C, Poon C S. Using artificial neural networks for predicting the elastic modulus of recycled aggregate concrete. Construction & Building Materials, 2013, 44: 524–532

    Google Scholar 

  44. Khademil F, Akbari M, Jamal S M. Prediction of concrete compressive strength using ultrasonic pulse velocity test and artificial neural network modeling. Romanian Journal of Materials, 2016, 46: 343–350

    Google Scholar 

  45. Sadrmomtazil A, Sobhani J, Mirgozar M A. Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS. Construction & Building Materials, 2013, 42: 205–216

    Google Scholar 

  46. Tayfurl G, Erdem T K, Kırca Ö. Strength prediction of high-strength concrete by fuzzy logic and artificial neural networks. Journal of Materials in Civil Engineering, 2014, 26(11): 04014079

    Google Scholar 

  47. Akbaril M, Overloop P J, Afshar A. Clustered k nearest neighbor algorithm for daily inflow forecasting. Journal Water Resources Management, 2011, 25(5): 1341–1357

    Google Scholar 

  48. Akbaril M, Afshar A, Mousavi S J. Multiobjective reservoir operation under emergency condition: Abbaspour reservoir case study with nonfunctional spillways. Journal of Flood Risk Management, 2014, 7(4): 374–384

    Google Scholar 

  49. Khademil F, Akbari M, Nikoo M. Displacement determination of concrete reinforcement building using data-driven models. International Journal of Sustainable Built Environment, 2017, 6(2): 400–411

    Google Scholar 

  50. Hamdia K M, Lahmer T, Nguyen-Thoi T, Rabczuk T. Predicting the fracture toughness of PNCs: A stochastic approach based on ANN and ANFIS. Computational Materials Science, 2015, 102: 304–313

    Google Scholar 

  51. Hornikl K, Stinchcombe M, White H. Multilayer feed-forward networks are universal approximators. Neural Networks, 1989, 2(5): 359–366

    MATH  Google Scholar 

  52. Akbaril M, Afshar A. Similarity-based error prediction approach for real-time inflow forecasting. Hydrology Research, 2014, 45(4-5): 589–602

    Google Scholar 

  53. Mamdani E H, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 1975, 7(1): 1–13

    MATH  Google Scholar 

  54. Takagil T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 1985, 15(1): 116–132

    Google Scholar 

  55. Akbaril M, Afshar A, Sadrabadi M R. Fuzzy rule based models modification by new data: Application to flood flow forecasting. Water Resources Management, 2009, 23(12): 2491–2504

    Google Scholar 

  56. Vernieuwel H, Georgieva O, De Baets B, Pauwels V, Verhoest N E C, De Troch F P. Comparison of data-driven Takagi-Sugeno models of rainfall-discharge dynamics. Journal of Hydrology (Amsterdam), 2005, 302(1-4): 173–186

    Google Scholar 

  57. Kangl P, Cho S. Locally linear reconstruction for instance-based learning. Pattern Recognition, 2008, 41(11): 3507–3518

    MATH  Google Scholar 

  58. Behnoodl A, Ziari H. Effects of silica fume addition and water to cement ratio on the properties of high-strength concrete after exposure to high temperatures. Cement and Concrete Composites, 2008, 30(2): 106–112

    Google Scholar 

  59. Bastamil M, Baghbadrani M, Aslani F. Performance of nano-Silica modied high strength concrete at elevated temperatures. Construction & Building Materials, 2014, 68: 402–408

    Google Scholar 

  60. Chenl L, Fang Q, Jiang X, Ruan Z, Hong J. Combined effects of high temperature and high strain rate on normal weight concrete. International Journal of Impact Engineering, 2015, 86: 40–56

    Google Scholar 

  61. Xiongl Y, Deng S, Wu D. Experimental study on compressive strength recovery effect of fire-damaged high strength concrete after realkalisation treatment. Procedia Engineering, 2016, 135: 476–481

    Google Scholar 

  62. Magda I M. Effect of elevated temperature on the properties of silica fume and recycled rubber-lled high strength concretes (RHSC). Housing and Building National Research Center, 2015, 13: 1–7

    Google Scholar 

  63. Fu Y F, Wong Y L, Poon C S, Tang C A. Stress-strain behavior of high-strength concrete at elevated temperatures. Magazine of Concrete Research, 2005, 57(9): 535–544

    Google Scholar 

  64. Husem M. The effects of high temperature on compressive and exural strengths of ordinary and high-performance concrete. Fire Safety Journal, 2006, 41(2): 155–163

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Faculty of Engineering, University of Kashan, Kashan, 8731753153, Iran

    Mahmood Akbari & Vahid Jafari Deligani

Authors
  1. Mahmood Akbari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vahid Jafari Deligani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mahmood Akbari.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Akbari, M., Jafari Deligani, V. Data driven models for compressive strength prediction of concrete at high temperatures. Front. Struct. Civ. Eng. 14, 311–321 (2020). https://doi.org/10.1007/s11709-019-0593-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11709-019-0593-8

Keywords

Search

Navigation