Computers and Concrete

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Knowledge-based learning for modeling concrete compressive strength using genetic programming

  • Tsai, Hsing-Chih (Department of Civil and Construction Engineering, National Taiwan University of Science and Technology) ;
  • Liao, Min-Chih (Department of Civil and Construction Engineering, National Taiwan University of Science and Technology)
  • Received : 2018.06.26
  • Accepted : 2019.03.28
  • Published : 2019.04.25
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Abstract

The potential of using genetic programming to predict engineering data has caught the attention of researchers in recent years. The present paper utilized weighted genetic programming (WGP), a derivative model of genetic programming (GP), to model the compressive strength of concrete. The calculation results of Abrams' laws, which are used as the design codes for calculating the compressive strength of concrete, were treated as the inputs for the genetic programming model. Therefore, knowledge of the Abrams' laws, which is not a factor of influence on common data-based learning approaches, was considered to be a potential factor affecting genetic programming models. Significant outcomes of this work include: 1) the employed design codes positively affected the prediction accuracy of modeling the compressive strength of concrete; 2) a new equation was suggested to replace the design code for predicting concrete strength; and 3) common data-based learning approaches were evolved into knowledge-based learning approaches using historical data and design codes.

Keywords

Acknowledgement

Supported by : Ministry of Science and Technology, Taiwan

References

  1. Babanajad, S.K., Gandomi, A.H., Mohammadzadeh, S.D. and Alavi, A.H. (2013), "Numerical modeling of concrete strength under multiaxial confinement pressures using linear genetic programming", Autom. Constr., 36, 136-144. https://doi.org/10.1016/j.autcon.2013.08.016
  2. Babu, K.G. and Rao, G.S.N. (1996), "Efficiency of fly ash in concrete with age", Cement Concrete Res., 26(3), 465-474. https://doi.org/10.1016/0008-8846(96)00011-7
  3. Baykasoglu, A., Gullu, H., Canakci, H. and Ozbakir, L. (2008) "Prediction of compressive and tensile strength of limestone via genetic programming", Exp. Syst. Appl., 35(1-2), 111-123. https://doi.org/10.1016/j.eswa.2007.06.006
  4. Baykasoglu, A., Oztas, A. and Ozbay, E. (2009), "Prediction and multi-objective optimization of high-strength concrete parameters via soft computing approaches", Exp. Syst. Appl., 36(3), 6145-6155. https://doi.org/10.1016/j.eswa.2008.07.017
  5. Behzad, M., Asghari, K., Eazi, M. and Palhang, M. (2009), "Generalization performance of support vector machines and neural networks in runoff modeling", Exp. Syst. Appl., 36(4), 7624-7629. https://doi.org/10.1016/j.eswa.2008.09.053
  6. Berardi, L., Kapelan, Z., Giustolisi, O. and Savic, D. (2008), "Development of pipe deterioration models for water distribution systems using EPR", J. Hydroinf., 10(2), 113-126. https://doi.org/10.2166/hydro.2008.012
  7. Bhattacharya, M., Abraham, A. and Nath, B. (2001), "A linear genetic programming approach for modeling electricity demand prediction in Victoria", Proceedings of the Hybrid Information Systems, First International Workshop on Hybrid Intelligent Systems, Adelaide, Australia, 379-393.
  8. Bilgehan, M. and Turgut, P. (2010), "The use of neural networks in concrete compressive strength estimation", Comput. Concrete, 7(3), 271-283. https://doi.org/10.12989/cac.2010.7.3.271
  9. Canakci, H., Gullu, H. and Dwle, M.I.K. (2018), "Effect of glass powder added grout for deep mixing of marginal sand with clay", Arab. J. Sci. Eng., 43(4), 1583-1595. https://doi.org/10.1007/s13369-017-2655-3
  10. Ferreira, C. (2001), "Gene expression programming: A new adaptive algorithm for solving problems", Complex Syst., 13(2), 87-129.
  11. Fiore, A., Quaranta, G., Marano, G.C. and Monti, G. (2016), "Evolutionary polynomial regression-based statistical determination of the shear capacity equation for reinforced concrete beams without stirrups", J. Comput. Civil Eng., 30(1), 04014111. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000450
  12. Giustolisi, O. and Savic, D.A. (2006), "A symbolic data-driven technique based on evolutionary polynomial regression", J. Hydroinf., 8(3), 207-222. https://doi.org/10.2166/hydro.2006.020b
  13. Gullu, H. (2012), "Prediction of peak ground acceleration by genetic expression programming and regression: A comparison using likelihood based measure", Eng. Geol., 141-142, 92-113. https://doi.org/10.1016/j.enggeo.2012.05.010
  14. Gullu, H. (2013), "On the prediction of shear wave velocity at local site of strong ground motion stations an application using artificial intelligence", B. Earthq. Eng., 11(4), 969-997. https://doi.org/10.1007/s10518-013-9425-8
  15. Gullu, H. (2014), "Function finding via genetic expression programming for strength and elastic properties of clay treated with bottom Ash", Eng. Appl. Artif. Intel., 35, 143-157. https://doi.org/10.1016/j.engappai.2014.06.020
  16. Gullu, H. (2015), "Unconfined compressive strength and freezethaw resistance of fine-grained soil stabilised with bottom ash, lime and superplasticizer", Road Mater. Pavement, 16(3), 608-634. https://doi.org/10.1080/14680629.2015.1021369
  17. Gullu, H. (2016), "Comparison of rheological models for jet grout cement mixtures with various stabilizers", Constr. Build. Mater., 127, 220-236. https://doi.org/10.1016/j.conbuildmat.2016.09.129
  18. Gullu, H. (2017a), "A new prediction method to rheological behavior of grout with bottom ash for jet grouting columns", Soil. Found., 57(3) 384-396. https://doi.org/10.1016/j.sandf.2017.05.006
  19. Gullu, H. (2017b), "A novel approach to prediction of rheological characteristics of jet grout cement mixtures via genetic expression programming", Neural Comput. Appl., 28, 407-420. https://doi.org/10.1007/s00521-016-2360-2
  20. Gullu, H. and Fedakar, H.I. (2017c), "On the prediction of unconfined compressive strength of silty soil stabilized with bottom ash, jute and steel fibers via artificial intelligence", Geomech. Eng., 12(3), 441-464. https://doi.org/10.12989/gae.2017.12.3.441
  21. Gullu, H. and Girisken, S. (2013), "Performance of fine-grained soil treated with industrial wastewater sludge", Environ. Earth Sci., 70, 777-788. https://doi.org/10.1007/s12665-012-2167-0
  22. Gullu, H., Canakci, H. and Al Zangana, I.F. (2017d), "Use of cement based grout with glass powder for deep mixing", Constr. Build. Mater., 137, 12-20. https://doi.org/10.1016/j.conbuildmat.2017.01.070
  23. Gullu, H., Cevik, A., Al-Ezzi, K.M. and Gulsan, M.E. (2019), "On the rheology of using geopolymer for grouting: A comparative study with cement-based grout included fly ash and cold bonded fly ash", Constr. Build. Mater., 196, 594-610. https://doi.org/10.1016/j.conbuildmat.2018.11.140
  24. Hossain, K.M.A., Lachemi, M. and Easa, S.M. (2006), "Artificial neural network model for the strength prediction of fully restrained RC slabs subjected to membrane action", Comput. Concrete, 3(6), 439-454. https://doi.org/10.12989/cac.2006.3.6.439
  25. Hossain, M.S., Ong, Z.C., Ismail, Z., Noroozi, S. and Khoo, S.Y. (2017), "Artificial neural networks for vibration based inverse parametric identifications: A review", Appl. Soft Comput., 52, 203-219. https://doi.org/10.1016/j.asoc.2016.12.014
  26. Ismail, A. and Jeng, D.S. (2011), "Modelling load-settlement behaviour of piles using high-order neural network (HON-PILE model)", Eng. Appl. Artif. Intell., 24(5), 813-821. https://doi.org/10.1016/j.engappai.2011.02.008
  27. Koza, J.R. (1992), Genetic Programming: On the Programming of Computers by Means of Natural Selection, A Bradford Book, The MIT Press.
  28. Mehrjoo, M., Khaji, N., Moharrami, H. and Bahreininejad, A. (2008), "Damage detection of truss bridge joints using Artificial Neural Networks", Exp. Syst. Appl., 35(3), 1122-1131. https://doi.org/10.1016/j.eswa.2007.08.008
  29. Mousavi, S.M., Aminian, P., Gandomi, A.H., Alavi, A.H. and Bolandi, H. (2012), "A new predictive model for compressive strength of HPC using gene expression programming", Adv. Eng. Softw., 45(1), 105-114. https://doi.org/10.1016/j.advengsoft.2011.09.014
  30. Muhammad, K., Mohammad, N. and Rehman, F. (2015), "Modeling shotcrete mix design using artificial neural network", Comput. Concrete, 15(2), 167-181. https://doi.org/10.12989/cac.2015.15.2.167
  31. Nagaraj, T. and Banu, Z. (1996), "Generalization of Abrams' law", Cement Concrete Res., 26(6), 933-942. https://doi.org/10.1016/0008-8846(96)00065-8
  32. Olofintoye, O., Otieno, F. and Adeyemo, J. (2016), "Real-time optimal water allocation for daily hydropower generation from the Vanderkloof dam, South Africa", Appl. Soft Comput., 47, 119-129. https://doi.org/10.1016/j.asoc.2016.05.018
  33. Oltean, M. and Dumitrescu, D. (2002), "Multi expression programming", Technical Report, UBB-01-2002, Babes-Bolyai University, Cluj-Napoca, Romania.
  34. Oluokun, F.A. (1994), "Fly ash concrete mix design and the watercement ratio law", ACI Mater. J., 91(4), 362-371.
  35. Ongpeng, J., Soberano, M., Oreta, A. and Hirose, S. (2017), "Artificial neural network model using ultrasonic test results to predict compressive stress in concrete", Comput. Concrete, 19(1), 59-68. https://doi.org/10.12989/cac.2017.19.1.059
  36. Ozbay, E., Oztas, A. and Baykasoglu, A. (2010), "Cost optimization of high strength concretes by soft computing techniques", Comput. Concrete, 7(3), 221-237. https://doi.org/10.12989/cac.2010.7.3.221
  37. Parichatprecha, R. and Nimityongskul, P. (2009), "An integrated approach for optimum design of HPC mix proportion using genetic algorithm and artificial neural networks", Comput. Concrete, 6(3), 253-268. https://doi.org/10.12989/cac.2009.6.3.253
  38. Patel, K.A., Chaudhary, S. and Nagpal, A.K. (2017), "Neural network based approach for rapid prediction of deflections in RC beams considering cracking", Comput. Concrete, 19(3), 293-303. https://doi.org/10.12989/cac.2017.19.3.293
  39. Peng, C.H., Yeh, I.C. and Lien, L.C. (2009), "Modeling strength of high-performance concrete using genetic operation trees with pruning techniques", Comput. Concrete, 6(3), 203-223. https://doi.org/10.12989/cac.2009.6.3.203
  40. Popovics, S. (1990), "Analysis of the concrete strength versus water-cement ratio relationship", ACI Mater. J., 87(5), 517-529.
  41. Saha, P., Prasad, M.L.V. and RathishKumar, P. (2017), "Predicting strength of SCC using artificial neural network and multivariable regression analysis", Comput. Concrete, 20(1), 31-38. https://doi.org/10.12989/CAC.2017.20.1.031
  42. Sonebi, M., Grunewald, S., Cevik, A. and Walraven, J. (2016), "Modelling fresh properties of self-compacting concrete using neural network technique", Comput. Concrete, 18(4), 903-921. https://doi.org/10.12989/cac.2016.18.6.903
  43. Tran, D.H., Ng, A.W.M. and Perera, B.J.C. (2007), "Neural networks deterioration models for serviceability condition of buried storm water pipes", Eng. Appl. Artif. Intell., 20(8), 1144-1151. https://doi.org/10.1016/j.engappai.2007.02.005
  44. Tsai, H.C. (2009), "Hybrid high order neural networks", Appl. Soft Comput., 9, 874-881. https://doi.org/10.1016/j.asoc.2008.11.007
  45. Tsai, H.C. (2010), "Predicting strengths of concrete-type specimens using hybrid multilayer perceptrons with centerunified particle swarm optimization", Exp. Syst. Appl., 37, 1104-1112. https://doi.org/10.1016/j.eswa.2009.06.093
  46. Tsai, H.C. (2011), "Using weighted genetic programming to program squat wall strengths and tune associated formulas", Eng. Appl. Artif. Intell. 24, 526-533. https://doi.org/10.1016/j.engappai.2010.08.010
  47. Tsai, H.C. (2016), "Modeling concrete strength with high order neural networks", Neural Comput. Appl., 27, 2465-2473. https://doi.org/10.1007/s00521-015-2017-6
  48. Tsai, H.C. and Lin, Y.H. (2011), "Predicting high-strength concrete parameters using weighted genetic programming", Eng. Comput., 27(4) 347-355.
  49. 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
  50. Yeh, I.C. and Lien, L.C. (2009), "Knowledge discovery of concrete material using genetic operation trees", Exp. Syst. Appl., 36(3), 5807-5812. https://doi.org/10.1016/j.eswa.2008.07.004

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