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Modeling Temperature of Fire-Damaged Reinforced Concrete Buildings Based on Nondestructive Testing and Gene Algorithm Techniques


It is a daunting task to ascertain the level of deterioration in structural concrete and embedded reinforcing steel after fire damage. Thus, this study focuses on modelling the temperature of fire-damaged reinforced concrete buildings based on nondestructive and Gene algorithm techniques. The study employed nondestructive data (rebound hammer number (RH) and ultrasonic pulse velocity (UPV)) from laboratory simulation tests on concrete elements and field forensic investigation of fire-damaged buildings to develop mathematical models. Other data used for the model was the thickness of the member. The input parameters for the model were: RH, UPV, and breadth of the member, while the temperature was the output data. The study proposed a model based on genetic expression programming (GEP), with a correlation coefficient of 0.9895. The developed model generalised the data correctly with a high degree of accuracy; it would be a useful tool for the field assessment of fire-damaged reinforced concrete buildings.

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Data Availability

All the data used in this research will be made available upon request.


  1. 1.

    Awoyera P (2014) Forensic investigation of fire-affected concrete buildings. LAP LAMBERT Academic Publishing, Mauritius.

  2. 2.

    Meloni P, Mistretta F, Stochino F, Carcangiu G (2019) Thermal path reconstruction for reinforced concrete under fire. Fire Technol 55:1451–1475.

    Article  Google Scholar 

  3. 3.

    Stochino F, Mistretta F, Meloni P, Carcangiu G (2017) Integrated approach for post-fire reinforced concrete structures assessment. Period Polytech Civ Eng.

    Article  Google Scholar 

  4. 4.

    Stawiski B (2006) Attempt to estimate fire damage to concrete building structure. Arch Civ Mech Eng 6:23–29.

    Article  Google Scholar 

  5. 5.

    Aseem A, Baloch WL, Khushnood RA, Mushtaq A (2019) Case studies in construction materials structural health assessment of fi re damaged building using non-destructive testing and micro-graphical forensic analysis : a case study. Case Stud Constr Mater 11:e00258.

    Article  Google Scholar 

  6. 6.

    Musmar MA (2019) Journal of king saud university—engineering sciences a case study on fire damage assessment of a two-story building with precast pretensioned hollow core slabs. J King Saud Univ—Eng Sci.

    Article  Google Scholar 

  7. 7.

    Piroglu F, Baydogan M, Ozakgul K (2017) An experimental study on fi re damage of structural steel members in an industrial building. Eng Fail Anal 80:341–351.

    Article  Google Scholar 

  8. 8.

    Alcaíno P, Santa-María H, Magna-Verdugo C, López L (2020) Experimental fast-assessment of post-fire residual strength of reinforced concrete frame buildings based on non-destructive tests. Constr Build Mater 234:117371.

    Article  Google Scholar 

  9. 9.

    Wang Y, Chen Z, Jiang Y et al (2020) Residual properties of three-span continuous reinforced concrete slabs subjected to different compartment fires. Eng Struct 208:110352.

    Article  Google Scholar 

  10. 10.

    Ryu E, Kim H, Chun Y et al (2020) Effect of heated areas on thermal response and structural behavior of reinforced concrete walls exposed to fire. Eng Struct 207:110165.

    Article  Google Scholar 

  11. 11.

    Hajiloo H, Green MF (2018) Post-fire residual properties of GFRP reinforced concrete slabs: a holistic investigation. Compos Struct 201:398–413.

    Article  Google Scholar 

  12. 12.

    Knyziak P, Kowalski R, Krentowski JR (2019) Fire damage of RC slab structure of a shopping center. Eng Fail Anal 97:53–60.

    Article  Google Scholar 

  13. 13.

    Ni S, Gernay T (2020) Predicting residual deformations in a reinforced concrete building structure after a fire event. Eng Struct 202:109853.

    Article  Google Scholar 

  14. 14.

    Fu F (2020) Fire induced progressive collapse potential assessment of steel framed buildings using machine learning. J Constr Steel Res 166:105918.

    Article  Google Scholar 

  15. 15.

    Nematzadeh M, Shahmansouri AA, Zabihi R (2021) Innovative models for predicting post-fire bond behavior of steel rebar embedded in steel fiber reinforced rubberized concrete using soft computing methods. Structures 31:1141–1162.

    Article  Google Scholar 

  16. 16.

    Andrushia AD, Anand N, Prince Arulraj G (2021) Evaluation of thermal cracks on fire exposed concrete structures using Ripplet transform. Math Comput Simul 180:93–113.

    MathSciNet  Article  MATH  Google Scholar 

  17. 17.

    Kou L, Wang X, Guo X et al (2021) Deep learning based inverse model for building fire source location and intensity estimation. Fire Saf J 121:103310.

    Article  Google Scholar 

  18. 18.

    Iqbal MF, Liu Q, Azim I et al (2020) Prediction of mechanical properties of green concrete incorporating waste foundry sand based on gene expression programming. J Hazard Mater 384:121322.

    Article  Google Scholar 

  19. 19.

    Jafari S, Mahini SS (2017) Lightweight concrete design using gene expression programing. Constr Build Mater 139:93–100.

    Article  Google Scholar 

  20. 20.

    Gholampour A, Gandomi AH, Ozbakkaloglu T (2017) New formulations for mechanical properties of recycled aggregate concrete using gene expression programming. Constr Build Mater 130:122–145.

    Article  Google Scholar 

  21. 21.

    Aseem A, Baloch WL, Khushnood RA, Mushtaq A (2019) Structural health assessment of fire damaged building using non-destructive testing and micro-graphical forensic analysis: a case study. Case Stud Constr Mater 11:e00258.

    Article  Google Scholar 

  22. 22.

    Narendra K, Ray F, Dilip C (2008) Evaluation and repair of fire-damaged buildings. Structural Forensics, 1–5

  23. 23.

    ACI 288. 2R (2013) Report on nondestructive test methods for evaluation of concrete in structures. American Concrete Institute, USA

  24. 24.

    Dolinar U, Trtnik G, Turk G, Hozjan T (2019) The feasibility of estimation of mechanical properties of limestone concrete after fire using nondestructive methods. Constr Build Mater 228:116786.

    Article  Google Scholar 

  25. 25.

    Yang Y, Zhan B, Wang J, Zhang Y (2020) Nondestructive assessment of the compressive strength of concrete with high volume slag. Mater Charact 162:110223.

    Article  Google Scholar 

  26. 26.

    Jalal M, Nassir N, Jalal H, Arabali P (2019) On the strength and pulse velocity of rubberized concrete containing silica fume and zeolite: Prediction using multivariable regression models. Constr Build Mater 223:530–543.

    Article  Google Scholar 

  27. 27.

    Panedpojaman P, Tonnayopas D (2018) Rebound hammer test to estimate compressive strength of heat exposed concrete. Constr Build Mater 172:387–395.

    Article  Google Scholar 

  28. 28.

    International Atomic Energy Agency (2002) Guidebook on non-destructive testing of concrete structures. IAEA: N. p., Web.

  29. 29.

    Emamian SA, Eskandari-Naddaf H (2020) Genetic programming based formulation for compressive and flexural strength of cement mortar containing nano and micro silica after freeze and thaw cycles. Constr Build Mater 241:118027.

    Article  Google Scholar 

  30. 30.

    Faradonbeh RS, Armaghani DJ, Monjezi M, Mohamad ET (2016) Genetic programming and gene expression programming for flyrock assessment due to mine blasting. Int J Rock Mech Min Sci 88:254–264.

    Article  Google Scholar 

  31. 31.

    Sarıdemir M (2014) Effect of specimen size and shape on compressive strength of concrete containing fly ash: application of genetic programming for design. Mater Des 56:297–304.

    Article  Google Scholar 

  32. 32.

    Farzampour A, Mansouri I, Mortazavi SJ, Hu JW (2019) Force-displacement relationship of a butterfly-shaped beams based on gene expression programming. In: 10th International Symposium on Steel Structures. Jeju, Korea, pp. 10–13

  33. 33.

    Mansouri I, Azmathulla HM, Hu JW (2018) Gene expression programming application for prediction of ultimate axial strain of FRP-confined concrete. Electron J Fac Civ Eng Osijek-e-GFOS 16:64–76

    Google Scholar 

  34. 34.

    Hanif A, Lu Z, Cheng Y et al (2017) Effects of different lightweight functional fillers for use in cementitious composites. Int J Concr Struct Mater 11:99–113.

    Article  Google Scholar 

  35. 35.

    Emamian SA, Eskandari-Naddaf H (2019) Effect of porosity on predicting compressive and flexural strength of cement mortar containing micro and nano-silica by ANN and GEP. Constr Build Mater 218:8–27.

    Article  Google Scholar 

  36. 36.

    BS 1881 part 202 (1986) Recommendations for surface hardness by rebound hammer. British Standard Institute, London

  37. 37.

    BS 1881 part 203 (1986) Recommendation for measurement of velocity of ultrasonic pulses in concrete. British Standard Institute, London

  38. 38.

    Smith NG (1986) Probability and statistics in civil engineering. Accessed 17 Dec 2020

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The authors would like to acknowledge the support received from the Covenant University, Ota, Nigeria, to carry out this research in an enabling environment.


No funding was received for conducting this research.

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All authors whose names appear on the submission made substantial contributions to the conception, design of the work, the acquisition, analysis, interpretation of data and writing/revision of article.

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Correspondence to Paul O. Awoyera.

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Awoyera, P.O., Olalusi, O.B. Modeling Temperature of Fire-Damaged Reinforced Concrete Buildings Based on Nondestructive Testing and Gene Algorithm Techniques. Fire Technol (2021).

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  • Forensic investigation
  • Fire-damaged buildings
  • Genetic programming
  • Temperature
  • Nondestructive testing