Computers and Concrete

  • 제33권1호
  • /
  • Pages.77-90
  • /
  • 2024
  • /
  • 1598-8198(pISSN)
  • /
  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

An advanced machine learning technique to predict compressive strength of green concrete incorporating waste foundry sand

  • Danial Jahed Armaghani (School of Civil and Environmental Engineering, University of Technology Sydney) ;
  • Haleh Rasekh (School of Civil and Environmental Engineering, University of Technology Sydney) ;
  • Panagiotis G. Asteris (Computational Mechanics Laboratory, School of Pedagogical and Technological Education)
  • 투고 : 2023.04.06
  • 심사 : 2023.08.24
  • 발행 : 2024.01.25
⟨ 이전 논문 다음 논문 ⟩

초록

Waste foundry sand (WFS) is the waste product that cause environmental hazards. WFS can be used as a partial replacement of cement or fine aggregates in concrete. A database comprising 234 compressive strength tests of concrete fabricated with WFS is used. To construct the machine learning-based prediction models, the water-to-cement ratio, WFS replacement percentage, WFS-to-cement content ratio, and fineness modulus of WFS were considered as the model's inputs, and the compressive strength of concrete is set as the model's output. A base extreme gradient boosting (XGBoost) model together with two hybrid XGBoost models mixed with the tunicate swarm algorithm (TSA) and the salp swarm algorithm (SSA) were applied. The role of TSA and SSA is to identify the optimum values of XGBoost hyperparameters to obtain the higher performance. The results of these hybrid techniques were compared with the results of the base XGBoost model in order to investigate and justify the implementation of optimisation algorithms. The results showed that the hybrid XGBoost models are faster and more accurate compared to the base XGBoost technique. The XGBoost-SSA model shows superior performance compared to previously published works in the literature, offering a reduced system error rate. Although the WFS-to-cement ratio is significant, the WFS replacement percentage has a smaller influence on the compressive strength of concrete. To improve the compressive strength of concrete fabricated with WFS, the simultaneous consideration of the water-to-cement ratio and fineness modulus of WFS is recommended.

키워드

참고문헌

  1. Aggarwal, Y. and Siddique, R. (2014), "Microstructure and properties of concrete using bottom ash and waste foundry sand as partial replacement of fine aggregates", Constr. Build. Mater., 54, 210-223. https://doi.org/10.1016/j.conbuildmat.2013.12.051.
  2. Alisha, S.S., Nagaraju, T.V., Onyelowe, K.C., Dumpa, V. and Sireesha, M. (2022), "Prediction of strength and stiffness behavior of glass powder stabilized expansive clay using ANN principles", International Conference on Trends and Recent Advances in Civil Engineering, Noida, India, October.
  3. Arasu, A.N., Vivek, S., Robinson, J. and Ranjith, T.T. (2017), "Experimental analysis of waste foundry sand in partial replacement of fine aggregate in concrete", Int. J. ChemTech Res., 10(6), 605-622.
  4. Armaghani, D.J., Mohamad, E.T., Narayanasamy, M.S., Narita, N. and Yagiz, S. (2017), "Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition", Tunnell. Undergr. Sp. Technol., 63, 29-43. https://doi.org/10.1016/j.tust.2016.12.009.
  5. Armaghani, D.J., Mirzaei, F., Shariati, M., Trung, N.T., Shariati, M. and Trnavac, D. (2020), "Hybrid ANN-based techniques in predicting cohesion of sandy-soil combined with fiber", Geomech. Eng, 20(3), 191-205. https://doi.org/10.12989/gae.2020.20.3.191.
  6. Asteris, P.G., Armaghani, D.J., Hatzigeorgiou, G.D., Karayannis, C.G. and Pilakoutas, K. (2019), "Predicting the shear strength of reinforced concrete beams using artificial neural networks", Comput. Concrete, 24(5), 469-488. https://doi.org/10.12989/cac.2019.24.5.469.
  7. Asteris, P.G., Koopialipoor, M., Armaghani, D.J., Kotsonis, E.A. and Lourenco, P.B. (2021), "Prediction of cement-based mortars compressive strength using machine learning techniques", Neural Comput. Appl., 33(19), 13089-13121. https://doi.org/10.1007/s00521-021-06004-8.
  8. Asteris, P.G., Lourenco, P.B., Roussis, P.C., Adami, C.E., Armaghani, D.J., Cavaleri, L., Chalioris, C.E., Hajihassani, M., Lemonis, M.E. and Mohammed, A.S. (2022), "Revealing the nature of metakaolin-based concrete materials using artificial intelligence techniques", Constr. Build. Mater., 322, 126500. https://doi.org/10.1016/j.conbuildmat.2022.126500.
  9. Asteris, P.G., Rizal, F.I.M., Koopialipoor, M., Roussis, P.C., Ferentinou, M., Armaghani, D.J. and Gordan, B. (2022), "Slope stability classification under seismic conditions using several tree-based intelligent techniques", Appl. Sci., 12(3), 1753. https://doi.org/10.3390/app12031753.
  10. Baba, D., Patil, S.G., Anuradha, B., Rahul, M. and Manjunath (2016), "Durability study of concrete using foundry waste sand", International Research Journal of Engineering and Technology, 3(8), 1-7.
  11. Basar, H.M. and Aksoy, N.D. (2012), "The effect of waste foundry sand (WFS) as partial replacement of sand on the mechanical, leaching and micro-structural characteristics of ready-mixed concrete", Constr. Build. Mater., 35, 508-515. https://doi.org/10.1016/j.conbuildmat.2012.04.078.
  12. Biswas, R., Bardhan, A., Samui, P., Rai, B., Nayak, S. and Armaghani, D.J. (2021), "Efficient soft computing techniques for the prediction of compressive strength of geopolymer concrete", Comput. Concrete, 28(2), 221-232. https://doi.org/10.12989/cac.2021.28.2.221.
  13. Chen, T. and Guestrin, C. (2015), "Xgboost: Reliable large-scale tree boosting system", Proceedings of the 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August.
  14. Chen, T. and Guestrin, C. (2016), "Xgboost: A scalable tree boosting system", Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August.
  15. Cherif, I.L. and Kortebi, A. (2019), "On using extreme gradient boosting (XGBoost) machine learning algorithm for home network traffic classification", 2019 Wireless Days (WD), Manchester, UK, April.
  16. Dabbaghi, F., Rashidi, M., Nehdi, M.L., Sadeghi, H., Karimaei, M., Rasekh, H. and Qaderi, F. (2021), "Experimental and informational modeling study on flexural strength of ecofriendly concrete incorporating coal waste", Sustainab., 13(13), 7506. https://doi.org/10.3390/su13137506.
  17. Dantas, A.T.A., Leite, M.B. and de Jesus Nagahama, K. (2013), "Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks", Constr. Build. Mater., 38, 717-722. https://doi.org/10.1016/j.conbuildmat.2012.09.026.
  18. Durica, M., Frnda, J. and Svabova, L. (2019), "Decision tree based model of business failure prediction for Polish companies", Oecon. Copern., 10(3), 453-469. https://doi.org/10.24136/oc.2019.022
  19. Erdem, S. and Blankson, M.A. (2014), "Environmental performance and mechanical analysis of concrete containing recycled asphalt pavement (RAP) and waste precast concrete as aggregate", J. Hazard. Mater., 264, 403-410. https://doi.org/10.1016/j.jhazmat.2013.11.040.
  20. Eskandari-Naddaf, H. and Kazemi, R. (2017), "ANN prediction of cement mortar compressive strength, influence of cement strength class", Constr. Build. Mater., 138, 1-11. https://doi.org/10.1016/j.conbuildmat.2017.01.132.
  21. Etxeberria, M., Pacheco, C., Meneses, J.M. and Berridi, I. (2010), "Properties of concrete using metallurgical industrial byproducts as aggregates", Constr. Build. Mater., 24, 1594-1600. https://doi.org/10.1016/j.conbuildmat.2010.02.034.
  22. Gandomi, A.H. and Roke, D.A. (2015), "Assessment of artificial neural network and genetic programming as predictive tools", Adv. Eng. Softw., 88, 63-72. https://doi.org/10.1016/j.advengsoft.2015.05.007.
  23. Ganesh Prabhu, G., Bang, J.W., Lee, B.J., Hyun, J.H. and Kim, Y.Y. (2015), "Mechanical and durability properties of concrete made with used foundry sand as fine aggregate", Adv. Mater. Sci. Eng., 2015, 1.
  24. Garg, A., Aggarwal, P., Aggarwal, Y., Belarbi, M., Chalak, H., Tounsi, A. and Gulia, R. (2022), "Machine learning models for predicting the compressive strength of concrete containing nano silica", Comput. Concr, 30(1), 33-42. https://doi.org/10.12989/cac.2022.30.1.033.
  25. Getahun, M.A., Shitote, S.M. and Gariy, Z.C.A. (2018), "Artificial neural network based modelling approach for strength prediction of concrete incorporating agricultural and construction wastes", Constr. Build. Mater., 190, 517-525. https://doi.org/10.1016/j.conbuildmat.2018.09.097.
  26. Ghanizadeh, A.R., Ghanizadeh, A., Asteris, P.G., Fakharian, P. and Armaghani, D.J. (2023), "Developing bearing capacity model for geogrid-reinforced stone columns improved soft clay utilizing MARS-EBS hybrid method", Transp. Geotech., 38, 100906. https://doi.org/10.1016/j.trgeo.2022.100906.
  27. Gholampour, A., Gandomi, A.H. and Ozbakkaloglu, T. (2017), "New formulations for mechanical properties of recycled aggregate concrete using gene expression programming", Constr. Build. Mater., 130, 122-145. https://doi.org/10.1016/j.conbuildmat.2016.10.114.
  28. Guney, Y., Aydilek, A.H. and Demirkan, M.M. (2006), "Geoenvironmental behavior of foundry sand amended mixtures for highway subbases", Waste Manag., 26(9), 932-945. https://doi.org/10.1016/j.wasman.2005.06.007.
  29. Guney, Y., Sari, Y.D., Yalcin, M., Tuncan, A. and Donmez, S. (2010), "Re-usage of waste foundry sand in high-strength concrete", Waste Manag., 30(8-9), 1705-1713. https://doi.org/10.1016/j.wasman.2010.02.018.
  30. Gurumoorthy, N. and Arunachalam, K. (2016), "Micro and mechanical behaviour of Treated Used Foundry Sand concrete", Constr. Build. Mater., 123, 184-190. https://doi.org/10.1016/j.conbuildmat.2016.06.143.
  31. Hamilton, I.W. and Sammes, N.M. (1999), "Encapsulation of steel foundry bag house dusts in cement mortar", Cement Concrete Res., 29(1), 55-61. https://doi.org/10.1016/S0008-8846(98)00169-0.
  32. Hastie, T., Tibshirani, R. and Friedman, J. (2009), "Springer series in statistics the elements of statistical learning data mining", Math. Intell., 27(2), 83-85.
  33. He, Z., Zhu, X., Wang, J., Mu, M. and Wang, Y. (2019), "Comparison of CO2 emissions from OPC and recycled cement production", Constr. Build. Mater., 211, 965-973. https://doi.org/10.1016/j.conbuildmat.2019.03.289.
  34. Iqbal, M., Liu, Q. and Azim, I. (2019), "Experimental study on the utilization of waste foundry sand as embankment and structural fill", Proceedings of the 7th Global Conference on Materials Science and Engineering, Xi'an, China, November.
  35. Iqbal, M.F., Liu, Q.F., Azim, I., Zhu, X., Yang, J., Javed, M.F. and Rauf, M. (2020), "Prediction of mechanical properties of green concrete incorporating waste foundry sand based on gene expression programming", J. Hazard. Mater., 384, 121322. https://doi.org/10.1016/j.jhazmat.2019.121322.
  36. Jadhav, S.S., Tande, S.N. and Dubal, A.C. (2017), "Beneficial reuse of waste foundry sand in concrete", Int. J. Sci. Res. Publ., 7(3), 74-95.
  37. Jafari, M.M., Jahandari, S., Ozbakkaloglu, T., Rasekh, H., Jahed Armaghani, D. and Rahmani, A. (2023), "Mechanical properties of polyamide fiber-reinforced lime-cement concrete", Sustainab., 15(15), 11484. https://doi.org/10.3390/su151511484.
  38. Jose, J. and Hossiney, N. (2016), "Characteristics of concrete containing waste foundry sand and slag sand", Int. J. Earth Sci. Eng., 9, 54-59.
  39. Kaparthi, S. and Bumblauskas, D. (2020), "Designing predictive maintenance systems using decision tree-based machine learning techniques", Int. J. Quality Reliab. Manag., 37(4), 659-686. https://doi.org/10.1108/IJQRM-04-2019-0131.
  40. Kaur, G., Siddique, R. and Rajor, A. (2012), "Properties of concrete containing fungal treated waste foundry sand", Constr. Build. Mater., 29, 82-87. https://doi.org/10.1016/j.conbuildmat.2011.08.091.
  41. Kaur, G., Siddique, R. and Rajor, A. (2013), "Micro-structural and metal leachate analysis of concrete made with fungal treated waste foundry sand", Constr. Build. Mater., 38, 94-100. https://doi.org/10.1016/j.conbuildmat.2012.07.112.
  42. Kaur, S., Awasthi, L.K., Sangal, A. and Dhiman, G. (2020), "Tunicate swarm algorithm: A new bio-inspired based metaheuristic paradigm for global optimization", Eng. Appl. Artif. Intell., 90, 103541. https://doi.org/10.1016/j.engappai.2020.103541.
  43. Khatib, J., Baig, S., Bougara, A. and Booth, C. (2010), "Foundry sand utilisation in concrete production", Second International Conference on Sustainable Construction Materials and Technologies, Ancona, Italy, June.
  44. Khatib, J. and Ellis, D. (2001), "Mechanical properties of concrete containing foundry sand", Spec. Publ., 200, 733-748. https://doi.org/10.14359/10612.
  45. Khatib, J.M., Herki, B.A. and Kenai, S. (2013), "Capillarity of concrete incorporating waste foundry sand", Constr. Build. Mater., 47, 867-871. https://doi.org/10.1016/j.conbuildmat.2013.05.013.
  46. Kirk, P. (1998), "Highway construction application using waste foundry sand", Thesis Doctoral, Purdue University, West Lafayette, IN, USA.
  47. Konapure, C.G. and Ghanate, D.J. (2015), "Effect of industrial waste foundry sand as fine aggregate on concrete", Int. J. Current Eng. Technol., 5(4), 2782-2786.
  48. Kovler, K. and Roussel, N. (2011), "Properties of fresh and hardened concrete", Cement Concrete Res., 41(7), 775-792. https://doi.org/10.1016/j.cemconres.2011.03.009
  49. Kumar, A. and Rani, D. (2016), "Performance of concrete using paper sludge ash and foundry sand", Int. J. Innov. Res. Sci. Eng. Technol., 5, 2347-6710.
  50. Laith, A., Mohammad, S., Mohammad, A. and Hamzeh, A. (2020), "Salp swarm algorithm: A comprehensive survey", Neural Comput. Appl., 32(15), 11195-11215. https://doi.org/10.1007/s00521-019-04629-4.
  51. Li, H., Deng, Q., Zhang, J., Xia, B. and Skitmore, M. (2019), "Assessing the life cycle CO2 emissions of reinforced concrete structures: Four cases from China", J. Clean. Prod., 210, 1496-1506. https://doi.org/10.1016/j.jclepro.2018.11.102.
  52. Long, T., He, B., Ghorbani, A. and Khatami, S.M.H. (2023), "Tree-based techniques for predicting the compression index of clayey soils", J. Soft Comput. Civil Eng., 7(3), 52-67. https://doi.org/10.22115/scce.2023.377601.1579.
  53. Majeed, S.S., Haido, J.H., Atrushi, D.S., Al-Kamaki, Y., Dinkha, Y.Z., Saadullah, S.T. and Tayeh, B.A. (2021), "Properties of self-compacted concrete incorporating basalt fibers: Experimental study and Gene Expression Programming (GEP) analysis", Comput. Concrete, 28(5), 451-463. https://doi.org/10.12989/cac.2021.28.5.451.
  54. Makul, N. and Sua-Iam, G. (2018), "Innovative utilization of foundry sand waste obtained from the manufacture of automobile engine parts as a cement replacement material in concrete production", J. Clean. Prod., 199, 305-320. https://doi.org/10.1016/j.jclepro.2018.07.167.
  55. Manoharan, T., Laksmanan, D., Mylsamy, K., Sivakumar, P. and Sircar, A. (2018), "Engineering properties of concrete with partial utilization of used foundry sand", Waste Manag., 71, 454-460. https://doi.org/10.1016/j.wasman.2017.10.022.
  56. Mao, L.X., Hu, Z., Xia, J., Feng, G.L., Azim, I., Yang, J. and Liu, Q.F. (2019), "Multi-phase modelling of electrochemical rehabilitation for ASR and chloride affected concrete composites", Compos. Struct., 207, 176-189. https://doi.org/10.1016/j.compstruct.2018.09.063.
  57. Martins, M.A.D.B., Barros, R.M., Silva, G. and Santos, I.F.S.D. (2019), "Study on waste foundry exhaust sand, WFES, as a partial substitute of fine aggregates in conventional concrete", Sustain. Cities Soc., 45, 187-196. https://doi.org/10.1016/j.scs.2018.11.017.
  58. Mavroulidou, M. and Lawrence, D. (2018), "Can waste foundry sand fully replace structural concrete sand?", J. Mater. Cycles Waste Manag., 21, 594-605. https://doi.org/10.1007/s10163-018-00821-1.
  59. Mehrabi, P., Shariati, M., Kabirifar, K., Jarrah, M., Rasekh, H., Trung, N.T., Shariati, A. and Jahandari, S. (2021), "Effect of pumice powder and nano-clay on the strength and permeability of fiber-reinforced pervious concrete incorporating recycled concrete aggregate", Constr. Build. Mater., 287, 122652. https://doi.org/10.1016/j.conbuildmat.2021.122652.
  60. Mirjalili, S., Gandomi, A.H., Mirjalili, S.Z., Saremi, S., Faris, H. and Mirjalili, S.M. (2017), "Salp swarm algorithm: A bioinspired optimizer for engineering design problems", Adv. Eng. Softw., 114, 163-191. https://doi.org/10.1016/j.advengsoft.2017.07.002.
  61. Mohammed, A., Kurda, R., Armaghani, D.J. and Hasanipanah, M. (2021), "Prediction of compressive strength of concrete modified with fly ash: Applications of neuro-swarm and neuro-imperialism models", Comput. Concrete, 27(5), 489-512. https://doi.org/10.12989/cac.2021.27.5.489.
  62. Monosi, S., Sani, D. and Tittarelli, F. (2010), "Used foundry sand in cement mortars and concrete production", The Open Waste Manag. J., 3(1), 18-253. http://doi.org/10.2174/1876400201003010018.
  63. Monosi, S., Tittarelli, F., Giosue, C. and Ruello, M.L. (2013), "Effect of two different sources and washing treatment on the properties of UFS by-products for mortar and concrete production", Constr. Build. Mater., 44, 260-266. https://doi.org/10.1016/j.conbuildmat.2013.02.029.
  64. Montgomery, J.M. and Olivella, S. (2018), "Tree-based models for political science data", Am. J. Pol. Sci., 62(3), 729-744. https://doi.org/10.1111/ajps.12361.
  65. Nagaraju, T.V., Bahrami, A., Azab, M. and Naskar, S. (2023), "Development of sustainable high performance geopolymer concrete and mortar using agricultural biomass-A strength performance and sustainability analysis", Front. Mater., 10, 1128095. https://doi.org/10.3389/fmats.2023.1128095.
  66. Nagaraju, T.V., Mantena, S., Azab, M., Alisha, S.S., El Hachem, C., Adamu, M. and Murthy, P.S.R. (2023), "Prediction of high strength ternary blended concrete containing different silica proportions using machine learning approaches", Results Eng., 17, 100973. https://doi.org/10.1016/j.rineng.2023.100973.
  67. Nagaraju, T.V., Bahrami, A., Prasad, C.D., Mantena, S., Biswal, M. and Islam, M.R. (2023), "Predicting California bearing ratio of lateritic soils using hybrid machine learning technique", Build., 13(1), 255. https://doi.org/10.3390/buildings13010255.
  68. Naik, T.R., Chun, Y.M., Kraus, R.N., Ramme, B.W. and Siddique, R. (2004), "Precast concrete products using industrial byproducts", Mater. J., 101(3), 199-206. https://doi.org/10.14359/13115.
  69. Naik, T.R., Kraus, R.N., Chun, Y.M., Ramme, B.W. and Singh, S.S. (2003), "Properties of field manufactured cast-concrete products utilizing recycled materials", J. Mater. Civil Eng., 15(4), 400-407. https://doi.org/10.1061/(ASCE)0899-1561(2003)15:4(400).
  70. Naik, T.R., Kraus, R.N., Ramme, B.W. and Canpolat, F. (2011), "Effects of fly ash and foundry sand on performance of architectural precast concrete", J. Mater. Civil Eng., 24(7), 851-859.
  71. Naik, T.R., Patel, V.M., Parikh, D.M. and Tharaniyil, M.P. (1994), "Utilization of used foundry sand in concrete", J. Mater. Civil Eng., 6(2), 254-263. https://doi.org/10.1061/(ASCE)0899-1561(1994)6:2(254).
  72. Nasser, I.F., Saeed, I.A. and Jihad, S.A.A. (2023), "Properties of sustainable concrete utilization foundry sand", Int. Rev. Civil Eng., 14(1), 1. https://doi.org/10.15866/irece.v14i1.22070.
  73. Nithya, M., Priya, A., Muthukumaran, R. and Arunvivek, G. (2017), "Properties of concrete containing waste foundry sand for partial replacement of fine aggregate in concrete", Indian J. Eng. Mater. Sci., 24, 162-166.
  74. Obla, K.H. (2009), "What is green concrete", The Indian Concrete J., 24, 26-28.
  75. Pathariya Saraswati, C., Rana Jaykrushna, K., Shah Palas, A. and Mehta Jay, G. (2013), "Application of waste foundry sand for evolution of low-cost concrete", Int. J. Eng. Trends Technol., 4, 4281-4286.
  76. Patil, R., Mehetre, P. and Phalak, K. (2015), "Development of concrete with partial replacement of fine aggregate by waste foundry sand", Int. J. Modern Trends Eng., 2(7), 581-587. https://doi.org/10.1088/1757-899X/955/1/012042.
  77. Patiyal, N., Kumar, J. and Sharma, A.K. (2016), "Experimental study on geopolymer concrete with partial replacement of fine aggregate with foundry sand", Int. J. Recent Res. Asp., 3, 133-138.
  78. Perera, R., Arteaga, A. and De Diego, A. (2010), "Artificial intelligence techniques for prediction of the capacity of RC beams strengthened in shear with external FRP reinforcement", Compos. Struct., 92(5), 1169-1175. https://doi.org/10.1016/j.compstruct.2009.10.027.
  79. Prabhu, G.G., Hyun, J.H. and Kim, Y.Y. (2014), "Effects of foundry sand as a fine aggregate in concrete production", Constr. Build. Mater., 70, 514-521. https://doi.org/10.1016/j.conbuildmat.2014.07.070.
  80. Ramachandra, R. and Mandal, S. (2020), "Prediction of fly ash concrete compressive strengths using soft computing techniques", Comput, Concrete, 25(1), 83-94. https://doi.org/10.12989/cac.2020.25.1.083.
  81. Rao, S.M., Reddy, B.V., Lakshmikanth, S. and Ambika, N. (2009), "Re-use of fluoride contaminated bone char sludge in concrete", J. Hazard. Mater., 166(2-3), 751-756. https://doi.org/10.1016/j.jhazmat.2008.11.115.
  82. Sahmaran, M., Lachemi, M., Erdem, T.K. and Yucel, H.E. (2011), "Use of spent foundry sand and fly ash for the development of green self-consolidating concrete", Mater. Struct., 44, 1193-1204. https://doi.org/10.1617/s11527-010-9692-7.
  83. Salokhe, E.P. and Desai, D. (2014), "Application of foundry waste sand in manufacture of concrete", IOSR J. Mech. Civil Eng., 2014, 43-48.
  84. Sastry, K.G.K., Ravitheja, A. and Reddy, T.C.S. (2018), "Effect of foundry sand and mineral admixtures on mechanical properties of concrete", Arch. Civil Eng., 64(1), 117-131. https://doi.org/10.2478/ace-2018-0008
  85. Schneider, M., Romer, M., Tschudin, M. and Bolio, H. (2011), "Sustainable cement production-present and future", Cement Concrete Res., 41(7), 642-650. https://doi.org/10.1016/j.cemconres.2011.03.019
  86. Sebaaly, H., Varma, S. and Maina, J.W. (2018), "Optimizing asphalt mix design process using artificial neural network and genetic algorithm", Constr. Build. Mater., 168, 660-670. https://doi.org/10.1016/j.conbuildmat.2018.02.118.
  87. Siddique, R., Aggarwal, Y., Aggarwal, P., Kadri, E.H. and Bennacer, R. (2011), "Strength, durability, and micro-structural properties of concrete made with used-foundry sand (UFS)", Constr. Build. Mater., 25(4), 1916-1925. https://doi.org/10.1016/j.conbuildmat.2010.11.065.
  88. Siddique, R., De Schutter, G. and Noumowe, A. (2009), "Effect of used-foundry sand on the mechanical properties of concrete", Constr. Build. Mater., 23(2), 976-980. https://doi.org/10.1016/j.conbuildmat.2008.05.005.
  89. Siddique, R. and Kadri, E.H. (2011), "Effect of metakaolin and foundry sand on the near surface characteristics of concrete", Constr. Build. Mater., 25(8), 3257-3266. https://doi.org/10.1016/j.conbuildmat.2011.03.012.
  90. Siddique, R., Kaur, G. and Rajor, A. (2010), "Waste foundry sand and its leachate characteristics", Resour. Conserv. Recycl., 54(12), 1027-1036. https://doi.org/10.1016/j.resconrec.2010.04.006.
  91. Siddique, R. and Noumowe, A. (2008), "Utilization of spent foundry sand in controlled low-strength materials and concrete", Resour. Conserv. Recycl., 53(1-2), 27-35. https://doi.org/10.1016/j.resconrec.2008.09.007.
  92. Siddique, R., Singh, G., Belarbi, R. and Ait-Mokhtar, K. (2015), "Comparative investigation on the influence of spent foundry sand as partial replacement of fine aggregates on the properties of two grades of concrete", Constr. Build. Mater., 83, 216-222. https://doi.org/10.1016/j.conbuildmat.2015.03.011.
  93. Siddique, R., Singh, G. and Singh, M. (2018), "Recycle option for metallurgical by-product (Spent foundry sand) in green concrete for sustainable construction", J. Clean. Prod., 172, 1111-1120. https://doi.org/10.1016/j.jclepro.2017.10.255.
  94. Singh, G. and Siddique, R. (2012a), "Abrasion resistance and strength properties of concrete containing waste foundry sand (WFS)", Constr. Build. Mater., 28(1), 421-426. https://doi.org/10.1016/j.conbuildmat.2011.08.087.
  95. Singh, G. and Siddique, R. (2012b), "Effect of waste foundry sand (WFS) as partial replacement of sand on the strength, ultrasonic pulse velocity and permeability of concrete", Constr. Build. Mater., 26(1), 416-422. https://doi.org/10.1016/j.conbuildmat.2011.06.041.
  96. Smiti, A. (2020), "A critical overview of outlier detection methods", Comput. Sci. Rev., 38, 100306. https://doi.org/10.1016/j.cosrev.2020.100306.
  97. Sowmya, M. and Kumar, J. D. (2015), "Mixing of waste foundry sand in concrete", Int. J. Eng. Res. Sci. Technol., 4, 322-335.
  98. Toghroli, A., Mehrabi, P., Shariati. M., Thoi Trung, N., Jahandari, S. and Rasekh, H. (2020), "Evaluating the use of recycled concrete aggregate and pozzolanic additives in fiber-reinforced pervious concrete with industrial and recycled fiber", Constr. Build. Mater., 252, 118997. https://doi.org/10.1016/j.conbuildmat.2020.118997.
  99. Torres, A., Bartlett, L. and Pilgrim, C. (2017), "Effect of foundry waste on the mechanical properties of Portland Cement Concrete", Constr. Build. Mater., 135, 674-681. https://doi.org/10.1016/j.conbuildmat.2017.01.028.
  100. Wang, L., Cho, D.W., Tsang, D.C., Cao, X., Hou, D., Shen, Z., Alessi, D.S., Ok, Y.S. and Poon, C.S. (2019), "Green remediation of As and Pb contaminated soil using cement-free clay-based stabilization/solidification", Environ. Int., 126, 336-345. https://doi.org/10.1016/j.envint.2019.02.057.
  101. Wang, L., Yu, K., Li, J.S., Tsang, D.C., Poon, C.S., Yoo, J.C., Baek, K., Ding, S., Hou, D. and Dai, J.G. (2018), "Low-carbon and low-alkalinity stabilization/solidification of high-Pb contaminated soil", Chem. Eng. J., 351, 418-427. https://doi.org/10.1016/j.cej.2018.06.118.
  102. Wang, Y.S., Dai, J.G., Wang, L., Tsang, D.C. and Poon, C.S. (2018), "Influence of lead on stabilization/solidification by ordinary Portland cement and magnesium phosphate cement", Chemosph., 190, 90-96. https://doi.org/10.1016/j.chemosphere.2017.09.114.
  103. Wickham, H. and Stryjewski, L. (2011), "40 years of boxplots", Am. Stat., 2011, 1.
  104. Yaman, M.A., Abd Elaty, M. and Taman, M. (2017), "Predicting the ingredients of self compacting concrete using artificial neural network", Alexandria Eng. J., 56(4), 523-532. https://doi.org/10.1016/j.aej.2017.04.007.
  105. Zhou, J., Qiu, Y., Zhu, S., Armaghani, D.J., Li, C., Nguyen, H. and Yagiz, S. (2021), "Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate", Eng. Appl. Artif. Intell., 97, 104015. https://doi.org/10.1016/j.engappai.2020.104015.
  106. Zorlu, K., Gokceoglu, C., Ocakoglu, F., Nefeslioglu, H. and Acikalin, S. (2008), "Prediction of uniaxial compressive strength of sandstones using petrography-based models", Eng. Geol., 96(3-4), 141-158. https://doi.org/10.1016/j.enggeo.2007.10.009.