Smart Structures and Systems

  • 제31권1호
  • /
  • Pages.1-11
  • /
  • 2023
  • /
  • 1738-1584(pISSN)
  • /
  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Application of internet of things for structural assessment of concrete structures: Approach via experimental study

  • D. Jegatheeswaran (Department of Civil Engineering, Sona College of Technology) ;
  • P. Ashokkumar (Department of Civil Engineering, Sona College of Technology)
  • 투고 : 2021.09.20
  • 심사 : 2022.05.17
  • 발행 : 2023.01.25
⟨ 이전 논문 다음 논문 ⟩

초록

Assessment of the compressive strength of concrete plays a major role during formwork removal and in the prestressing process. In concrete, temperature changes occur due to hydration which is an influencing factor that decides the compressive strength of concrete. Many methods are available to find the compressive strength of concrete, but the maturity method has the advantage of prognosticating strength without destruction. The temperature-time factor is found using a LM35 temperature sensor through the IoT technique. An experimental investigation was carried out with 56 concrete cubes, where 35 cubes were for obtaining the compressive strength of concrete using a universal testing machine while 21 concrete cubes monitored concrete's temperature by embedding a temperature sensor in each grade of M25, M30, M35, and M40 concrete. The mathematical prediction model equation was developed based on the temperature-time factor during the early age compressive strength on the 1st, 2nd, 3rd and 7th days in the M25, M30, M35, and M40 grades of concrete with their temperature. The 14th, 21st and 28th day's compressive strength was predicted with the mathematical predicted equation and compared with conventional results which fall within a 2% difference. The compressive strength of concrete at any desired age (day) before reaching 28 days results in the discovery of the prediction coefficient. Comparative analysis of the results found by the predicted mathematical model show that, it was very close to the results of the conventional method.

키워드

참고문헌

  1. Abd Elaty, M.A.A. (2014), "Compressive strength prediction of Portland cement concrete with age using a new model", HBRC Journal, 10(2), 145-155. https://doi.org/10.1016/j.hbrcj.2013.09.005
  2. Al-Swaidani, A., Soud, A. and Hammami, A. (2017), "Improvement of the early-age compressive strength, water permeability, and sulfuric acid resistance of scoria-based mortars/concrete using limestone filler", Adv. Mater. Sci. Eng. https://doi.org/10.1155/2017/8373518
  3. Ashokkumar Palanisamy, D.D.J. and Sampath, S. (2020), "An Overview of Structural Health Monitoring By Using Smart Sensing Technology-"A Review"", PalArch's J. Archaeol. Egypt/Egyptol., 17(6), 13412-13417.
  4. ASTM, C. (1998), 1074. "Standard Practice for Estimating Concrete Strength by the Maturity Method", ASTM International, West Conshohocken, PA, USA.
  5. ASTM, C. (2002), 918-02. "Standar Metode Tes Untuk Menentukan Kuat Tekan Beton Umur Muda Dan Memperkirakan Kekuatan Di Umur Selanjutnya".
  6. Blumauer, U., Hozjan, T. and Trtnik, G. (2020), "Prediction of mechanical properties of limestone concrete after high temperature exposure with artificial neural networks", Adv. Concrete Constr., Int. J., 10(3), 247-256. https://doi.org/10.12989/acc.2020.10.3.247
  7. Brooks, A.G., Schindler, A.K. and Barnes, R.W. (2007), "Maturity method evaluated for various cementitious materials", J. Mater. Civil Eng., 19(12), 1017-1025. https://doi.org/10.1061/(ASCE)0899-1561(2007)19:12(1017)
  8. Cui, X., Wang, Y., Zeng, S., Zhou, D., Han, B., Yu, X. and Ou, J. (2017), "Numerical analysis on design and application of cement-based sensor for structural health monitoring", J. Intell. Mater. Syst. Struct., 28(18), 2579-2602. https://doi.org/10.1177/1045389X17692051
  9. Erdal, H., Erdal, M., Simsek, O. and Erdal, H.I. (2018), "Prediction of concrete compressive strength using non-destructive test results", Comput. Concrete, Int. J., 21(4), 407-417. https://doi.org/10.12989/cac.2018.21.4.407
  10. Ghiasi, R. and Ghasemi, M.R. (2018), "An intelligent health monitoring method for processing data collected from the sensor network of structure", Steel Compos. Struct., Int. J., 29(6), 703-716. https://doi.org/10.12989/scs.2018.29.6.703
  11. Hannan, M.A., Hassan, K. and Jern, K.P. (2018), "A review on sensors and systems in structural health monitoring: Current issues and challenges", Smart Struct. Syst., Int. J., 22(5), 509-525. https://doi.org/10.12989/sss.2018.22.5.509
  12. Jin, N.J., Yeon, K.-S., Min, S.-H. and Yeon, J. (2017), "Using the Maturity Method in Predicting the Compressive Strength of Vinyl Ester Polymer Concrete at an Early Age", Adv. Mater. Sci. Eng., 2017. https://doi.org/10.1155/2017/4546732
  13. John, S.T., Roy, B.K., Sarkar, P. and Davis, R. (2020), "IoT enabled real-time monitoring system for early-age compressive strength of concrete", J. Constr. Eng. Manage., 146(2), 5019020. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001754
  14. Kim, T. and Rens, K.L. (2008), "Concrete maturity method using variable temperature curing for normal and high-strength concrete. I: Experimental study", J. Mater. Civil Eng., 20(12), 727-734. https://doi.org/10.1061/(ASCE)0899-1561(2008)20:12(727)
  15. Kim, J.-K., Moon, Y.-H. and Eo, S.-H. (1998), "Compressive strength development of concrete with different curing time and temperature", Cement Concrete Res., 28(12), 1761-1773. https://doi.org/10.1016/S0008-8846(98)00164-1
  16. Leng, J.S., Barnes, R.A., Hameed, A., Winter, D., Tetlow, J., Mays, G.C. and Fernando, G.F. (2006), "Structural NDE of concrete structures using protected EFPI and FBG sensors", Sensors and Actuators A: Physical, 126(2), 340-347. https://doi.org/10.1016/j.sna.2005.10.050
  17. Nataraja, M.C. and Das, L. (2010), "Concrete mix proportioning as per IS 10262: 2009-Comparison with IS 10262: 1982 and ACI 211.1-91", Indian Concrete J., 64-70.
  18. Olawale, D.O., Sullivan, G., Dickens, T., Tsalickis, S., Okoli, O.I., Sobanjo, J.O. and Wang, B. (2012), "Development of a triboluminescence-based sensor system for concrete structures", Struct. Health Monitor., 11(2), 139-147. https://doi.org/10.1177/1475921711414231
  19. Plowman, J.M. (1956), "Discussion: Maturity and the strength of concrete", Magaz. Concrete Res., 8(24), 169-183. https://doi.org/10.1680/macr.1956.8.24.169
  20. Sadowski, L., Nikoo, M. and Nikoo, M. (2018), "Concrete compressive strength prediction using the imperialist competitive algorithm", Comput. Concrete, Int. J., 22(4), 355-363. https://doi.org/10.12989/cac.2018.22.4.355
  21. Standard, I. (2000), Plain and Reinforced Concrete-code of Practice, Bureau of Indian Standards, New Delhi, India.
  22. Utepov, Y., Khudaibergenov, O., Kabdush, Y. and Kazkeev, A. (2019), "Prototyping an embedded wireless sensor for monitoring reinforced concrete structures", Comput. Concrete, Int. J., 24(2), 95-102. https://doi.org/10.12989/cac.2019.24.2.095
  23. Ye, X.W., Jin, T. and Yun, C.B. (2019), "A review on deep learning-based structural health monitoring of civil infrastructures", Smart Struct. Syst., Int. J., 24(5), 567-585. https://doi.org/10.12989/sss.2019.24.5.567
  24. Yikici, T.A. and Chen, H.-L.R. (2015), "Use of maturity method to estimate compressive strength of mass concrete", Constr. Build. Mater., 95, 802-812. https://doi.org/10.1016/j.conbuildmat.2015.07.026
  25. Yoon, H., Kim, Y.J., Kim, H.S., Kang, J.W. and Koh, H.-M. (2017), "Evaluation of early-age concrete compressive strength with ultrasonic sensors", Sensors, 17(8), 1817. https://doi.org/10.3390/s17081817