Advances in nano research

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Estimation of lightweight aggregate concrete characteristics using a novel stacking ensemble approach

  • Kaloop, Mosbeh R. (Department of Civil and Environmental Engineering, Incheon National University) ;
  • Bardhan, Abidhan (Department of Civil Engineering, National Institute of Technology Patna) ;
  • Hu, Jong Wan (Department of Civil and Environmental Engineering, Incheon National University) ;
  • Abd-Elrahman, Mohamed (Structural Engineering Department, Mansoura University)
  • Received : 2022.02.09
  • Accepted : 2022.09.26
  • Published : 2022.11.25
⟨ Previous Next ⟩

Abstract

This study investigates the efficiency of ensemble machine learning for predicting the lightweight-aggregate concrete (LWC) characteristics. A stacking ensemble (STEN) approach was proposed to estimate the dry density (DD) and 28 days compressive strength (Fc-28) of LWC using two meta-models called random forest regressor (RFR) and extra tree regressor (ETR), and two novel ensemble models called STEN-RFR and STEN-ETR, were constructed. Four standalone machine learning models including artificial neural network, gradient boosting regression, K neighbor regression, and support vector regression were used to compare the performance of the proposed models. For this purpose, a sum of 140 LWC mixtures with 21 influencing parameters for producing LWC with a density less than 1000 kg/m3, were used. Based on the experimental results with multiple performance criteria, it can be concluded that the proposed STEN-ETR model can be used to estimate the DD and Fc-28 of LWC. Moreover, the STEN-ETR approach was found to be a significant technique in prediction DD and Fc-28 of LWC with minimal prediction error. In the validation phase, the accuracy of the proposed STEN-ETR model in predicting DD and Fc-28 was found to be 96.79% and 81.50%, respectively. In addition, the significance of cement, water-cement ratio, silica fume, and aggregate with expanded glass variables is efficient in modeling DD and Fc-28 of LWC.

Keywords

Acknowledgement

This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 21CFRP-C163381-01).

References

  1. Abd, A.M. and Abd, S.M. (2017), "Modelling the strength of lightweight foamed concrete using support vector machine (SVM)", Case Stud. Constr. Mater., 6, 8-15. https://doi.org/10.1016/j.cscm.2016.11.002.
  2. Abdelaziz, N., Abd El-Hakim, R., El-Badawy, S. and Afify, H.A. (2020), "International Roughness Index prediction model for flexible pavements", Int. J. Pavement Eng., 21(1), 88-99. https://doi.org/10.1080/10298436.2018.1441414.
  3. Abd-Elrahman, M., Chung, S.Y., Sikora, P., Rucinska, T. and Stephan, D. (2019), "Influence of nanosilica on mechanical properties, sorptivity, and microstructure of lightweight concrete", Materials, 12, 3078. https://doi.org/10.3390/ma12193078
  4. Abubakar, A.U. and Tabra, M.S. (2019), "Prediction of compressive strength in high performance concrete with hooked-end steel fiber using K-nearest neighbor algorithm", Int. J. Integr. Eng., 11(1). https://doi.org/10.30880/ijie.2019.11.01.016.
  5. Alpaydin, E. (2020) Introduction to Machine Learning, MIT press.
  6. Alsultan, A. (2021), "Assessment of microstructure and surface effects on vibrational characteristics of public transportation", Adv. Nano Res., 11(1), 101-113. https://doi.org/10.12989/anr.2021.11.1.101.
  7. Altman, N.S. (1992), "An introduction to kernel and nearestneighbor nonparametric regression", Am. Statistician, 46(3), 175-185.
  8. Alwash, M.F.A. (2017), "Assessment of concrete strength in existing structures using nondestructive tests and cores : analysis of current methodology and recommendations", Ph.D., Thesis, Universite de Bordeaux.
  9. Anyaoha, U., Peng, X. and Liu, Z. (2019), Concrete Performance Prediction Using Boosting Smooth Transition Regression Trees (Boost), in Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XIII. https://doi.org/10.1117/12.2518279.
  10. Asteris, P.G., Skentou, A., Bardhan, A., Samui, P. and Pilakoutas, K. (2021), "Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models", Cem. Concr. Res., 145, 106449. https://doi.org/10.1016/j.cemconres.2021.106449.
  11. Braspenning, P.J., Thuijsman, F. and Weijters, A. (1995) Artificial Neural Networks: An Introduction to Ann Theory and Practice, Psicothema, Springer Science & Business Media.
  12. Bui, X.N. Jaroonpattanapong, P., Nguyen, H., Tran, Q. and Long, N. (2019), "A novel hybrid model for predicting blast-induced ground vibration based on k-nearest neighbors and particle swarm optimization", Sci. Rep., 9(1), 1-14. https://doi.org/10.1038/s41598-019-50262-5.
  13. Chakraborty, D., Awolusi, I. and Gutierrez, L. (2021), "An explainable machine learning model to predict and elucidate the compressive behavior of high-performance concrete", Results Eng., 11, 100245. https://doi.org/10.1016/j.rineng.2021.100245.
  14. Chou, J.S., Ngo, N.T. and Pham, A.D. (2016), "Shear strength prediction in reinforced concrete deep beams using natureinspired metaheuristic support vector regression", J. Comput. Civ. Eng., 30(1), 04015002. https://doi.org/10.1061/(asce)cp.1943-5487.0000466.
  15. Chung, S.Y., Abd Elrahman, M., Kim, JS., Han, TS., Stephan, D. and Sikora, P. (2019), "Comparison of lightweight aggregate and foamed concrete with the same density level using imagebased characterizations", Constr. Build. Mater., 211, 988-999. https://doi.org/10.1016/j.conbuildmat.2019.03.270.
  16. Cui, L., Chen, P., Wang, L., Li, J. and Ling, H. (2021), "Application of extreme gradient boosting based on grey relation analysis for prediction of compressive strength of concrete", Adv. Civ. Eng., 8878396. https://doi.org/10.1155/2021/8878396.
  17. Du, H., Du, S. and Liu, X. (2015), "Effect of nano-silica on the mechanical and transport properties of lightweight concrete", Constr. Build. Mater., 82, 114-122. https://doi.org/10.1016/j.conbuildmat.2015.02.026
  18. Fatahi, O. and Jafari, S. (2018), "Prediction of lightweight aggregate concrete compressive strength", J. Rehabil. Civ. Eng., 6(2), 154-163. https://doi.org/10.22075/jrce.2017.11556.1192.
  19. Friedman, J.H. (2001), "Greedy function approximation: A gradient boosting machine", Ann. Stat., 29(5), 1189-1232. https://doi.org/10.1214/aos/1013203451.
  20. Gabr, A.R., Roy, B., Kaloop, M.R., Kumar, D., Arisha, A., Shiha, M., Shwally, S., Hu, J. and El-Badawy, S. (2021), "A novel approach for resilient modulus prediction using extreme learning machine-equilibrium optimiser techniques", Int. J. Pavement Eng., 1-11. https://doi.org/10.1080/10298436.2021.1892109.
  21. Geurts, P., Ernst, D. and Wehenkel, L. (2006), "Extremely randomized trees", Mach. Learn., 63(1), 3-42. https://doi.org/10.1007/s10994-006-6226-1.
  22. Gouasmi, M.T., Benosman, A.S., Taibi, H., Kazi-Tani, N. and Belbachir, M. (2017), "Destructive and non-destructive testing of an industrial screed mortar made with lightweight composite aggregates WPLA", Int. J. Eng. Res. Africa, 33, 140-158. https://doi.org/10.4028/www.scientific.net/JERA.33.140.
  23. Hadianfard, M.A. and Jafari, S. (2016), "Prediction of lightweight aggregate concrete compressive strength using ultrasonic pulse velocity test through gene expression programming", Sci. Iran., 23(6), 2506-2513. https://doi.org/10.24200/sci.2016.2309.
  24. Jalal, F.E., Xu, Y., Iqbal, M., Jamhiri, B. and Javed, M. (2021), "Predicting the compaction characteristics of expansive soils using two genetic programming-based algorithms", Transp. Geotech., 30, 100608. https://doi.org/10.1016/j.trgeo.2021.100608.
  25. Kaloop, M.R., El-Badawy, S.M., Ahn, J., Sim, H., Hu, J. and Abd El-Hakim, R. (2020), "A hybrid wavelet-optimally-pruned extreme learning machine model for the estimation of international roughness index of rigid pavements", Int. J. Pavement Eng., 1-15. https://doi.org/10.1080/10298436.2020.1776281.
  26. Kaloop, M.R., Samui, P., Shafeek, M. and Hu, J. (2020), "Estimating slump flow and compressive strength of selfcompacting concrete using emotional neural networks", Appl. Sci., 10(23), 8543. https://doi.org/10.3390/app10238543.
  27. Kaloop, M.R., Bardhan, A., Kardani, N., Samui, P., Hu, J. and Ramzy, A. (2021), "Novel application of adaptive swarm intelligence techniques coupled with adaptive network-based fuzzy inference system in predicting photovoltaic power", Renew. Sustain. Energy Rev., 148, 111315. https://doi.org/10.1016/j.rser.2021.111315.
  28. Kaloop, M.R., Elrahman, M.A. and Hu, J.W. (2022), "Nondestructive tests for defections detection of nanoparticles in cement-based materials: A review", Adv. Nano Res., 12(1), 1-23. https://doi.org/10.12989/anr.2022.12.1.001.
  29. Kasiri, R. and Massah, S. (2022), "Mathematical modeling of concrete beams containing GO nanoparticles for vibration analysis and measuring their compressive strength using an experimental method", Adv. Nano Res., 12(1), 73-79, https://doi.org/10.12989/anr.2022.12.1.073
  30. Khan, M.U.A., Shukla, S.K. and Raja, M.N.A. (2021), "Soilconduit interaction: an artificial intelligence application for reinforced concrete and corrugated steel conduits", Neural Comput. Appl., 33(21), 14861-14885. https://doi.org/10.1007/s00521-021-06125-0.
  31. Khater, H.M. (2016), "Nano-Silica effect on the physicomechanical properties of geopolymer composites", Adv. Nano Res., 4(3), 181-195, https://doi.org/10.12989/anr.2016.4.3.181
  32. Koya, B.P., Aneja, S., Gupta, R. and Valeo, C. (2021), "Comparative analysis of different machine learning algorithms to predict mechanical properties of concrete", Mech. Adv. Mater. Struct. 1-18. https://doi.org/10.1080/15376494.2021.1917021.
  33. Lee, D.G. and Ahn, K.H. (2021), "A stacking ensemble model for hydrological post-processing to improve streamflow forecasts at medium-range timescales over South Korea", J. Hydrol., 600, 126681. https://doi.org/10.1016/j.jhydrol.2021.126681.
  34. Lin, C.J. and Wu, N.J. (2021), "An ANN model for predicting the compressive strength of concrete", Appl. Sci., 11(9), 3798. https://doi.org/10.3390/app11093798.
  35. Marani, A. and Nehdi, M.L. (2020), "Machine learning prediction of compressive strength for phase change materials integrated cementitious composites", Constr. Build. Mater., 265, 120286. https://doi.org/10.1016/j.conbuildmat.2020.120286.
  36. Meshram, S.G., Singh, V., Kisi, O., Karimi, V. and Meshram, C. (2020), "Application of artificial neural networks, support vector machine and multiple model-ANN to sediment yield prediction", Water Resour. Manag., 34(15), 4561-4575. https://doi.org/10.1007/s11269-020-02672-8.
  37. Mozumder, R.A., Roy, B. and Laskar, A.I. (2017), "Support vector regression approach to predict the strength of FRP confined concrete", Arab. J. Sci. Eng., 42(3), 1129-1146. https://doi.org/10.1007/s13369-016-2340-y.
  38. Nagarajan, D., Rajagopal, T. and Meyappan, N. (2020), "A comparative study on prediction models for strength properties of LWA concrete using artificial neural network", Rev. Constr., 103-111. https://doi.org/10.7764/rdlc.19.1.103-111.
  39. Naniz, O.A. and Mazloom, M. (2018), "Effects of colloidal nanosilica on fresh and hardened properties of self-compacting lightweight concrete", J. Build. Eng., 20, 400-410. https://doi.org/10.1016/j.jobe.2018.08.014
  40. Narasimman, K., Jassam, T., Velayutham, T.S., Yaseer, M. and Ruzaima, R. (2020), "The synergic influence of carbon nanotube and nanosilica on the compressive strength of lightweight concrete", J. Build. Eng., 32, 101719. https://doi.org/10.1016/j.jobe.2020.101719
  41. Nguyen-Sy, T., Wakim, J., To, Q., Vu, M., Nguyen, T. and Nguyen, T.T. (2020), "Predicting the compressive strength of concrete from its compositions and age using the extreme gradient boosting method", Constr. Build. Mater., 260, 119757. https://doi.org/10.1016/j.conbuildmat.2020.119757.
  42. Nie, P., Roccotelli, M., Fanti, M., Ming, Z. and Li, Z. (2021), "Prediction of home energy consumption based on gradient boosting regression tree", 7, 1246-1255. https://doi.org/https://doi.org/10.1016/j.egyr.2021.02.006.
  43. Pal, M. and Deswal, S. (2011), "Support vector regression based shear strength modelling of deep beams", Comput. Struct., 89(13), 1430-1439. https://doi.org/10.1016/j.compstruc.2011.03.005.
  44. Sandhu, A.K. and Batth, R.S. (2021), "Software reuse analytics using integrated random forest and gradient boosting machine learning algorithm", Softw. Pract. Exp., 51(4), 735-747. https://doi.org/10.1002/spe.2921.
  45. Sedghi, Y., Zandi, Y., Paknahad, M., Assilzadeh, H. and Khadimallah, M. (2021), "Optimization of shear connectors with high strength nano concrete using soft computing techniques", Adv. Nano Res., 11(6), 595-606. https://doi.org/10.12989/anr.2021.11.6.595.
  46. Shariati, A., Barati, M., Ebrahimi, F. and Toghroli, A. (2020), "Investigation of microstructure and surface effects on vibrational characteristics of nanobeams based on nonlocal couple stress theory", Adv. Nano Res., 8(3), 191-202. https://doi.org/10.12989/anr.2021.11.6.595.
  47. Sidiq, S.J., Zaman, M. and Butt, M. (2018), "An empirical comparison of classifiers for multi-class imbalance learningNo Title", Int. J. Data Min. Emerg. Technol., 8(1), 115-122. https://doi.org/10.5958/2249-3220.2018.00013.7
  48. Sikora, P., Rucinska, T., Stephan, D., Chung, S. and Abd Elrahman, M. (2020), "Evaluating the effects of nanosilica on the material properties of lightweight and ultra-lightweight concrete using image-based approaches", Constr. Build. Mater., 264, 120241. https://doi.org/10.1016/j.conbuildmat.2020.120241.
  49. Tanyildizi, H. (2021), "Predicting the geopolymerization process of fly ash-based geopolymer using deep long short-term memory and machine learning", Cem. Concr. Compos., 123, 104177. https://doi.org/10.1016/j.cemconcomp.2021.104177.
  50. Tavakkol, S., Alapour, F., Kazemian, A., Hasaninejad, A., Ghanbari, A. and Ramezanianpour, A.A. (2013), "Prediction of lightweight concrete strength by categorized regression, MLR and ANN", Comput. Concr., 12(2), 151-167. https://doi.org/10.12989/cac.2013.12.2.151.
  51. Tuhta, S. (2021), "The determination of effect of TiO2 on dynamic behavior of scaled concrete structure by OMA", Adv. Nano Res., 11(6), 641-648. https://doi.org/10.12989/anr.2021.11.6.641
  52. Vapnik, V. (1995) The Nature of Statistical Learning Theory. Springer, New York, U.S.A.
  53. Wan, Z., Xu, Y. and Savija, B. (2021), "On the use of machine learning models for prediction of compressive strength of concrete: Influence of dimensionality reduction on the model performance", Materials, 14(4), 713. https://doi.org/10.3390/ma14040713.
  54. Wong, L.S., Marani, A. and Nehdi, M.L. (2021), "Gradient boosting coupled with oversampling model for prediction of concrete pipe-joint infiltration using designwise data set", J. Pipeline Syst. Eng. Pract., 12(3), 04021015. https://doi.org/10.1061/(asce)ps.1949-1204.0000557.
  55. Wu, N.J. (2021), "Predicting the compressive strength of concrete using an RBF-ANN model", Appl. Sci., 11(14), 6382. https://doi.org/10.3390/app11146382.
  56. Yaseen, Z.M., Tran, M., Kim, S., Bakhshpoori, T. and Deo, R. (2018), "Shear strength prediction of steel fiber reinforced concrete beam using hybrid intelligence models: A new approach", Eng. Struct., 177, 244-255. https://doi.org/10.1016/j.engstruct.2018.09.074.
  57. Yoon, J.Y., Kim, H., Lee, Y. and Sim, S. (2019), "Prediction model for mechanical properties of lightweight aggregate concrete using artificial neural network", Materials, 12(17), 2678. https://doi.org/10.3390/ma12172678.
  58. Zanoni, M., Arcelli Fontana, F. and Stella, F. (2015), "On applying machine learning techniques for design pattern detection", J. Syst. Softw., 103, 102-117. https://doi.org/https://doi.org/10.1016/j.jss.2015.01.037.
  59. Zhou, C., Zhou, L., Liu, F., Chen, W., Wang, Q., Liang, K., Guo, W. and Zhou, L. (2021), "A novel stacking heterogeneous ensemble model with hybrid wrapper-based feature selection for reservoir productivity predictions", Complexity, 6675638. https://doi.org/10.1155/2021/6675638.