Structural Engineering and Mechanics

  • 제87권3호
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  • Pages.221-229
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  • 2023
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

A novel analytical evaluation of the laboratory-measured mechanical properties of lightweight concrete

  • S. Sivakumar (Department of Civil Engineering, PSNA College of Engineering and Technology) ;
  • R. Prakash (Department of Civil Engineering, Government College of Engineering) ;
  • S. Srividhya (Department of Civil Engineering, VaruvanVadivelan Institute of Technology) ;
  • A.S. Vijay Vikram (Department of Civil Engineering, Global Institute of Engineering and Technology)
  • 투고 : 2022.09.17
  • 심사 : 2023.06.28
  • 발행 : 2023.08.10
⟨ 이전 논문 다음 논문 ⟩

초록

Urbanization and industrialization have significantly increased the amount of solid waste produced in recent decades, posing considerable disposal problems and environmental burdens. The practice of waste utilization in concrete has gained popularity among construction practitioners and researchers for the efficient use of resources and the transition to the circular economy in construction. This study employed Lytag aggregate, an environmentally friendly pulverized fuel ash-based lightweight aggregate, as a substitute for natural coarse aggregate. At the same time, fly ash, an industrial by-product, was used as a partial substitute for cement. Concrete mix M20 was experimented with using fly ash and Lytag lightweight aggregate. The percentages of fly ash that make up the replacements were 5%, 10%, 15%, 20%, and 25%. The Compressive Strength (CS), Split Tensile Strength (STS), and deflection were discovered at these percentages after 56 days of testing. The concrete cube, cylinder, and beam specimens were examined in the explorations, as mentioned earlier. The results indicate that a 10% substitution of cement with fly ash and a replacement of coarse aggregate with Lytag lightweight aggregate produced concrete that performed well in terms of mechanical properties and deflection. The cementitious composites have varying characteristics as the environment changes. Therefore, understanding their mechanical properties are crucial for safety reasons. CS, STS, and deflection are the essential property of concrete. Machine learning (ML) approaches have been necessary to predict the CS of concrete. The Artificial Fish Swarm Optimization (AFSO), Particle Swarm Optimization (PSO), and Harmony Search (HS) algorithms were investigated for the prediction of outcomes. This work deftly explains the tremendous AFSO technique, which achieves the precise ideal values of the weights in the model to crown the mathematical modeling technique. This has been proved by the minimum, maximum, and sample median, and the first and third quartiles were used as the basis for a boxplot through the standardized method of showing the dataset. It graphically displays the quantitative value distribution of a field. The correlation matrix and confidence interval were represented graphically using the corrupt method.

키워드

참고문헌

  1. Cheng, M., Firdausi, P.M. and Prayogo, D. (2014), "Engineering Applications of Artificial Intelligence High-performance concrete compressive strength prediction using Genetic Weighted Pyramid Operation Tree (GWPOT)", Eng. Appl. Artif. Intel., 29, 104-113. https://doi.org/10.1016/j.engappai.2013.11.014.
  2. Divyah, N., Prakash, R., Srividhya, S., Avudaiappan, S., Guindos, P., Carsalade, N.M., Arunachalam, KP.., Noroozinejad Farsangi, E. and Roco-Videla, A. (2023), "Experimental and numerical investigations of laced built-up lightweight concrete encased columns subjected to cyclic axial load", Build., 13(6), 1444. https://doi.org/10.3390/buildings13061444.
  3. El-Mir, A., El-Zahab, S., Sbartai, ZM., Homsi, F., Saliba, F. and El-Hassan, H. (2023), "Machine learning prediction of concrete compressive strength using rebound hammer test", J. Build. Eng., 64, 105538. https://doi.org/10.1016/j.jobe.2022.105538.
  4. Firouzi, A. and Rahai, A. (2012), "An integrated ANN-GA for reliability-based inspection of concrete bridge decks considering the extent of corrosion-induced cracks and life cycle costs", Scientia Iranica, 19(4), 974-981. https://doi.org/10.1016/j.scient.2012.06.002.
  5. Hidallana-Gamage, H.D., Thambiratnam, D.P. and Perera, N.J. (2014), "Numerical modeling and analysis of the blast performance of laminated glass panels and the influence of material parameters", Eng. Fail. Anal., 45, 65-84. https://doi.org/10.1016/j.engfailanal.2014.06.013.
  6. Hosan, A., Haque, S. and Shaikh, F. (2016), "Compressive behaviour of sodium and potassium activators synthetized fly ash geopolymer at elevated temperatures: A comparative study", J. Build. Eng., 8, 123-130. https://doi.org/10.1016/j.jobe.2016.10.005.
  7. Kandiri, A., Sartipi, F. and Kioumarsi, M. (2021), "Predicting compressive strength of concrete containing recycled aggregate using modified ANN with different optimization algorithms", Appl. Sci., 11(2), 485. https://doi.org/10.3390/app11020485.
  8. Kasperkiewicz, J., Racz, J. and Dubrawski, A. (1995), "HPC strength prediction using artificial neural network", J. Comput. Civil Eng., 9(4), 279-284. https://doi.org/10.1061/(ASCE)0887-3801(1995)9:4(279)
  9. Kovacevic, M., Lozancic, S., Nyarko, E.K. and Hadzima-Nyarko, M. (2022), "Application of artificial intelligence methods for predicting the compressive strength of self-compacting concrete with class F fly ash", Mater., 15(12), 4191. https://doi.org/10.3390/ma15124191.
  10. Li, D., Tang, Z., Kang, Q., Zhang, X. and Li, Y. (2023), "Machine learning-based method for predicting compressive strength of concrete", Proc., 11(2), 390. https://doi.org/10.3390/pr11020390.
  11. McKenna, S., Meyer, M., Gregg, C. and Gerber, S. (2016), "s-CorrPlot: An interactive scatterplot for exploring correlation", J. Comput. Graph. Stat., 25(2), 445-463. https://doi.org/10.1080/10618600.2015.1021926.
  12. Prakash, R., Divyah, N., Srividhya, S., Avudaiappan, S., Amran, M., Raman, S.N., Guindos, P., Vatin, N.I. and Fediuk, R. (2022), "Effect of steel fiber on the strength and flexural characteristics of coconut shell concrete partially blended with fly ash", Mater., 15(12), 4272. https://doi.org/10.3390/ma15124272.
  13. Prakash, R., Thenmozhi, R. and Raman, S. (2019), "Mechanical characterisation and flexural performance of eco-friendly concrete produced with fly ash as cement replacement and coconut shell coarse aggregate", Int. J. Environ. Sustain. Develop., 18(2), 131-148. https://doi.org/10.1504/ijesd.2019.099491.
  14. Prakash, R., Thenmozhi, R., Raman, S.N., Subramanian, C. and Divyah, N. (2021), "Mechanical characterization of sustainable fiber-reinforced lightweight concrete incorporating waste coconut shell as coarse aggregate and sisal fiber", Int. J. Environ. Sci. Technol., 18, 1579-1590. https://doi.org/10.1007/s13762-020-02900-z.
  15. Roehm, C., Sasmal, S., Novak, B. and Karusala, R. (2015), "Numerical simulation for seismic performance evaluation of fiber-reinforced concrete beam-column sub-assemblages", Eng. Struct., 91, 182-196. https://doi.org/10.1016/j.engstruct.2015.02.015.
  16. Sivakumar, S. and Kameshwari, B. (2016), "Arithmetical modelling for evaluating the performance of concrete strength with the aid of optimization techniques", Res. J. Appl. Sci., Eng. Technol., 12(6), 668-679. https://doi.org/10.19026/rjaset.12.2715.
  17. Tayfur, G., Erdem, T.K. and Kirca, O. (2014), "Strength prediction of high-strength concrete by fuzzy logic and artificial neural networks", J. Mater. Civil Eng., 26(11), 04014079. https://doi.org/10.1061/(ASCE)MT.1943-5533.0000985.
  18. Yeh, I.C. (1998), "Modeling of strength of high-performance concrete using artificial neural networks", Cement Concrete Res., 28(12), 1797-1808. https://doi.org/10.1016/S0008-8846(98)00165-3.
  19. Yuan, Z., Wang, L.N. and Ji, X. (2014), "Prediction of concrete compressive strength: Research on hybrid models genetic-based algorithms and ANFIS", Adv. Eng. Softw., 67, 156-163. https://doi.org/10.1016/j.advengsoft.2013.09.004.
  20. Zhang, X., Dai, C., Li, W. and Chen, Y. (2023), "Prediction of compressive strength of recycled aggregate concrete using machine learning and Bayesian optimization methods", Front. Earth Sci., 11, 1112105. https://doi.org/10.3389/feart.2023.1112105.