Computers and Concrete

  • 제33권2호
  • /
  • Pages.137-145
  • /
  • 2024
  • /
  • 1598-8198(pISSN)
  • /
  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Prediction of compressive strength of sustainable concrete using machine learning tools

  • Lokesh Choudhary (Department of Multidisciplinary Engineering, The NorthCap University) ;
  • Vaishali Sahu (Department of Multidisciplinary Engineering, The NorthCap University) ;
  • Archanaa Dongre (Department of Structural Engineering, Veermata Jijabai Technological Institute) ;
  • Aman Garg (Department of Multidisciplinary Engineering, The NorthCap University)
  • 투고 : 2023.01.19
  • 심사 : 2023.08.30
  • 발행 : 2024.02.25
⟨ 이전 논문 다음 논문 ⟩

초록

The technique of experimentally determining concrete's compressive strength for a given mix design is time-consuming and difficult. The goal of the current work is to propose a best working predictive model based on different machine learning algorithms such as Gradient Boosting Machine (GBM), Stacked Ensemble (SE), Distributed Random Forest (DRF), Extremely Randomized Trees (XRT), Generalized Linear Model (GLM), and Deep Learning (DL) that can forecast the compressive strength of ternary geopolymer concrete mix without carrying out any experimental procedure. A geopolymer mix uses supplementary cementitious materials obtained as industrial by-products instead of cement. The input variables used for assessing the best machine learning algorithm not only include individual ingredient quantities, but molarity of the alkali activator and age of testing as well. Myriad statistical parameters used to measure the effectiveness of the models in forecasting the compressive strength of ternary geopolymer concrete mix, it has been found that GBM performs better than all other algorithms. A sensitivity analysis carried out towards the end of the study suggests that GBM model predicts results close to the experimental conditions with an accuracy between 95.6 % to 98.2 % for testing and training datasets.

키워드

과제정보

LC would like to acknowledge the contribution made by AG in the work carried out for the present study.

참고문헌

  1. Ahmad, A., Ahmad, W., Aslam, F. and Joyklad, P. (2022), "Compressive strength prediction of fly ash-based geopolymer concrete via advanced machine learning techniques", Case Stud. Constr. Mater., 16, e00840. https://doi.org/10.1016/j.cscm.2021.e00840.
  2. Ahmad, A., Ahmad, W., Chaiyasarn, K., Ostrowski, K.A., Aslam, F., Zajdel, P. and Joyklad, P. (2021), "Prediction of geopolymer concrete compressive strength using novel machine learning algorithms", Polym., 13, 3389. https://doi.org/10.3390/polym13193389.
  3. Alacali, S. (2022), "A prediction model for strength and strain of CFRP-confined concrete cylinders using gene expression programming", Comput. Concrete, 30, 377-391. https://doi.org/10.12989/cac.2022.30.6.377.
  4. Aldred, J. and Day, J. (2012), "Is geopolymer concrete a suitable alternative to traditional concrete?", 37th Conference on Our World in Concrete & Structures, Singapore, August.
  5. Almashaqbeh, H.K., Irshidat, M.R., Najjar, Y. and Elmahmoud, W. (2022), "Artificial neural network modeling to predict the flexural behavior of RC beams retrofitted with CFRP modified with carbon nanotubes", Comput. Concrete, 30, 209-224. https://doi.org/10.12989/cac.2022.30.3.209.
  6. Aly, T. and Sanjayan, J.G. (2010), "Effect of pore-size distribution", J. Mater. Civil Eng., 22, 525-532. https://doi.org/10.1061/(ASCE)0899-1561(2010)22:5(525)
  7. Anjali, R. and Venkatesan, G. (2022), "Optimization of mechanical properties and composition of M-sand and pet particle added concrete using hybrid deep neural network-horse herd optimization algorithm", Constr. Build. Mater., 347, 128334. https://doi.org/10.1016/j.conbuildmat.2022.128334.
  8. Calkins, M. (2009), Materials for Sustainable Sites: A Complete Guide to the Evaluation, Selection and Use of Sustainable Construction Materials, John Wiley & Sons, Hoboken, NJ, USA.
  9. Cao, R., Fang, Z., Jin, M. and Shang, Y. (2022), "Application of machine learning approaches to predict the strength property of geopolymer concrete", Mater., 15, 2400. https://doi.org/10.3390/ma15072400.
  10. Choudhary, L., Bansal, S., Kalra, M. and Dagar, L. (2022), "Mechanical evaluation of recycled aggregate mixes and its application in reclaimed asphalt pavement (RAP) stretch", Beni-Suef Univ. J. Basic Appl. Sci., 11, 127. https://doi.org/10.1186/s43088-022-00302-3.
  11. Choudhary, L. and Pachouri, A. (2017), "Analysis of non-engineered structure using SAP 2000", 2nd International Conference on Science, Technology and Management, New Delhi, India, September.
  12. Choudhary, L., Sahu, V., Dongre, A. and Tonk, A. (2023), "Macro- and microstructural durability investigations of sustainable ternary geopolymer concrete paver blocks", Eur. Chem. Bull., 12, 5474-5494. https://doi.org/10.31838/ecb/2023.12.si6.472.
  13. Craveiro, F., Duarte, J.P., Bartolo, H. and Bartolo, P.J. (2019), "Additive manufacturing as an enabling technology for digital construction: A perspective on Construction 4.0", Autom. Constr., 103, 251-267. https://doi.org/10.1016/j.autcon.2019.03.011.
  14. Crentsil, K.S. (2009), "Role of oxide ratios on engineering performance of fly ash geopolymer binder systems", Ceram. Eng. Sci. Proc., 29, 175-184.
  15. Davidovits, J. (2008), Geopolymer Chemistry and Application, Institut Geopolymere, Saint-Quentin, France.
  16. Davidovits, J. (1994), "High-alkali cements for 21st century concretes", Spec. Publ., 144, 383-398. https://doi.org/10.14359/4523.
  17. Davidovits, J. (1991), "Geopolymers: Inorganic polymeric new materials", J. Therm. Anal. Calorim., 37, 1633-1656. https://doi.org/doi.org/10.1007/bf01912193.
  18. Davidovits, J. and Davidovics, M. (1991), "Geopolymer: Ultra-high temperature tooling material for the manufacture of advanced composites", SAMPE, 36(2), 1939-1949.
  19. Deb, P.S., Nath, P. and Sarker, P.K. (2014), "The effects of ground granulated blast-furnace slag blending with fly ash and activator content on the workability and strength properties of geopolymer concrete cured at ambient temperature", Mater. Des., 62, 32-39. https://doi.org/10.1016/j.matdes.2014.05.001.
  20. Diaz, E.I., Allouche, E.N. and Eklund, S. (2010), "Factors affecting the suitability of fly ash as source material for geopolymers", Fuel, 89, 992-996. https://doi.org/10.1016/j.fuel.2009.09.012.
  21. Garg, A., Aggarwal, P., Aggarwal, Y., Belarbi, M.O., Chalak, H.D., Tounsi, A. and Gulia, R. (2022a), "Machine learning models for predicting the compressive strength of concrete containing nano silica", Comput. Concrete, 30, 33-42. https://doi.org/10.12989/cac.2022.30.1.033.
  22. Garg, A., Belarbi, M., Tounsi, A., Li, L., Singh, A. and Mukhopadhyay, T. (2022b), "Predicting elemental stiffness matrix of FG nanoplates using gaussian process regression based surrogate model in framework of layerwise model", Eng. Anal. Bound. Elem., 143, 779-795. https://doi.org/10.1016/j.enganabound.2022.08.001.
  23. Garg, A., Mukhopadhyay, T., Belarbi, M.O., Chalak, H.D., Singh, A. and Zenkour, A.M. (2023a), "On accurately capturing the through-thickness variation of transverse shear and normal stresses for composite beams using FSDT coupled with GPR", Compos. Struct., 305, 116551. https://doi.org/10.1016/j.compstruct.2022.116551.
  24. Garg, A., Mukhopadhyay, T., Belarbi, M.O. and Li, L. (2023b), "Random forest-based surrogates for transforming the behavioral predictions of laminated composite plates and shells from FSDT to Elasticity solutions", Compos. Struct., 309, 116756. https://doi.org/10.1016/j.compstruct.2023.116756.
  25. Geurts, P., Ernst, D. and Wehenkel, L. (2006), "Extremely randomized trees", Mach. Learn., 63, 3-42. https://doi.org/10.1007/s10994-006-6226-1.
  26. Ghahremani, B. and Rizzo, P. (2022), "Multi-gene genetic programming for the prediction of the compressive strength of concrete mixtures", Comput. Concrete, 30, 225-236. https://doi.org/10.12989/cac.2022.30.3.225.
  27. Habibi Rad, M., Mojtahedi, M. and Ostwald, M.J. (2021), "Industry 4.0, disaster risk management and infrastructure resilience: A systematic review and bibliometric analysis", Build., 11(9), 411. https://doi.org/10.3390/buildings11090411.
  28. Hermann, M., Pentek, T. and Otto, B. (2016), "Design principles for industrie 4.0 scenarios", 2016 49th Hawaii International Conference on System Sciences (HICSS), Koloa, HI, USA, January.
  29. Ismail, I., Bernal, S.A., Provis, J.L., Hamdan, S. and van Deventer, J.S.J. (2013), "Microstructural changes in alkali activated fly ash/slag geopolymers with sulfate exposure", Mater. Struct., 46, 361-373. https://doi.org/10.1617/s11527-012-9906-2.
  30. Ismail, I., Bernal, S.A., Provis, J.L., San Nicolas, R., Hamdan, S. and van Deventer, J.S.J. (2014), "Modification of phase evolution in alkali-activated blast furnace slag by the incorporation of fly ash", Cem. Concrete Compos., 45, 125-135. https://doi.org/10.1016/j.cemconcomp.2013.09.006.
  31. Jang, D., Bang, J., Yoon, H.., Seo, J., Jung, J., Jang, J.G. and Yang, B. (2022), "Deep learning-based LSTM model for prediction of long-term piezoresistive sensing performance of cement-based sensors incorporating multi-walled carbon nanotube", Comput. Concrete, 30, 301-310. https://doi.org/10.12989/cac.2022.30.5.301.
  32. Jong, S.C., Ong, D.E.L. and Oh, E. (2022), "A novel Bayesian inference method for predicting optimum strength gain in sustainable geomaterials for greener construction", Constr. Build. Mater., 344, 128255. https://doi.org/10.1016/j.conbuildmat.2022.128255.
  33. Jong, S.C., Ong, D.E.L. and Oh, E. (2021), "State-of-the-art review of geotechnical-driven artificial intelligence techniques in underground soil-structure interaction", Tunn. Undergr. Sp. Technol., 113, 103946. https://doi.org/10.1016/j.tust.2021.103946.
  34. Kagermann, H., Helbig, J., Hellinger, A. and Wahlster, W. (2013), "Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry. Final report of the Industrie 4.0 Working Group", Forschungsunion, Berlin, Gemany.
  35. Kalra, M., Kumar, G. and Choudhary, L. (2018), "Seismic response of RCC framed structure with floating columns", Int. J. Sustain. Build. Technol. Urban Dev., 9, 18-30. https://doi.org/https://doi.org/10.22712/susb.20180003.
  36. Kumar, S., Kumar, R. and Mehrotra, S.P. (2010), "Influence of granulated blast furnace slag on the reaction, structure and properties of fly ash based geopolymer", J. Mater. Sci., 45, 607-615. https://doi.org/10.1007/s10853-009-3934-5.
  37. Lee, N.K. and Lee, H.K. (2013), "Setting and mechanical properties of alkali-activated fly ash/slag concrete manufactured at room temperature", Constr. Build. Mater., 47, 1201-1209. https://doi.org/10.1016/j.conbuildmat.2013.05.107.
  38. Lloyd, N. and Rangan, B. (2010), "Geopolymer concrete with fly ash", Proceedings of 2nd International Conference on Sustainable Construction Materials and Technologies, Ancona, Italy, June.
  39. Meena, S., Choudhary, L. and Dey, A. (2013), "Quasi-static analysis of geotextile reinforced unpaved road resting on c-φ subgrade", Procedia Soc. Behav. Sci., 104, 235-244. https://doi.org/10.1016/j.sbspro.2013.11.116. 
  40. Nguyen, K.T., Nguyen, Q.D., Le, T.A., Shin, J. and Lee, K. (2020), "Analyzing the compressive strength of green fly ash based geopolymer concrete using experiment and machine learning approaches", Constr. Build. Mater., 247, 118581. https://doi.org/10.1016/j.conbuildmat.2020.118581.
  41. Nguyen, N.H., Tong, K.T., Lee, S., Karamanli, A. and Vo, T.P. (2022), "Prediction compressive strength of cement-based mortar containing metakaolin using explainable categorical gradient boosting model", Eng. Struct., 269, 114768. https://doi.org/10.1016/j.engstruct.2022.114768.
  42. Partha, S.D., Pradip, N. and Prabir, K.S. (2013), "Strength and permeation properties of slag blended fly ash based geopolymer concrete", Adv. Mater. Res., 651, 168-173. https://doi.org/10.4028/www.scientific.net/AMR.651.168.
  43. Paruthi, S., Sharma, N., Gulia, R., Choudhary, L., Sharma, A., Belarbi, M.O., Garg, A., Li, L. and Chalak, H.D. (2023), "Thermal-based free vibration and buckling behavior of bioinspired cross- and double-helicoidal/bouligand laminated composite plates", Acta Mech. Solida Sin., 2023, 1-10. https://doi.org/10.1007/s10338-023-00415-x.
  44. Puligilla, S. and Mondal, P. (2013), "Cement and concrete research role of slag in microstructural development and hardening of fly ash-slag geopolymer", Cem. Concrete Res., 43, 70-80. https://doi.org/10.1016/j.cemconres.2012.10.004.
  45. Roy, D., Choudhary, L., Sharma, N. and Sharma, N. (2018), "Study on physical properties of quaternary cement concrete with novocon XR steel fibers", Int. J. Sustain. Build. Technol. Urban Dev., 9, 197-208. https://doi.org/https://doi.org/10.22712/susb.20180020.
  46. Wang, X., Liu, Y., Chen, A. and Ruan, X. (2022), "Flexural capacity assessment of precast deck joints based on deep forest", Struct., 41, 270-286. https://doi.org/10.1016/j.istruc.2022.05.009.