DOI QR코드

DOI QR Code

Concrete compressive strength prediction using the imperialist competitive algorithm

  • Sadowski, Lukasz (Faculty of Civil Engineering, Wroclaw University of Science and Technology) ;
  • Nikoo, Mehdi (Young Researchers and Elite Club, Ahvaz Branch, Islamic Azad University) ;
  • Nikoo, Mohammad (SAMA Technical and Vocational Training College, Islamic Azad University)
  • 투고 : 2018.06.22
  • 심사 : 2018.09.26
  • 발행 : 2018.10.25

초록

In the following paper, a socio-political heuristic search approach, named the imperialist competitive algorithm (ICA) has been used to improve the efficiency of the multi-layer perceptron artificial neural network (ANN) for predicting the compressive strength of concrete. 173 concrete samples have been investigated. For this purpose the values of slump flow, the weight of aggregate and cement, the maximum size of aggregate and the water-cement ratio have been used as the inputs. The compressive strength of concrete has been used as the output in the hybrid ICA-ANN model. Results have been compared with the multiple-linear regression model (MLR), the genetic algorithm (GA) and particle swarm optimization (PSO). The results indicate the superiority and high accuracy of the hybrid ICA-ANN model in predicting the compressive strength of concrete when compared to the other methods.

키워드

참고문헌

  1. Al-Zharani, T.M., Demirboga, R., Khushefati, W.H. and Taylan, O. (2016), "Measurement and prediction of correction factors for very high core compressive strength by using the adaptive neuro-fuzzy techniques", Constr. Build. Mater., 122, 320-331. https://doi.org/10.1016/j.conbuildmat.2016.06.019
  2. Alexandridis, A., Triantis, D., Stavrakasa, I. and Stergiopoulos, C. (2012), "A neural network approach for compressive strength prediction in cement-based materials through the study of pressure-stimulated electrical signals", Constr. Build. Mater., 30, 294-300. https://doi.org/10.1016/j.conbuildmat.2011.11.036
  3. Alshihri, M.M. Azmy, A.M. and El-Bisy, M.S. (2009), "Neural networks for predicting compressive strength of structural light weight concrete", Constr. Build. Mater., 23(6), 2214-2219. https://doi.org/10.1016/j.conbuildmat.2008.12.003
  4. Armaghani, D.J., Hasanipanah, M., Amnieh, H.B. and Mohamad, E.T. (2018), "Feasibility of ICA in approximating ground vibration resulting from mine blasting", Neur. Comput. Appl., 29(9), 457-465. https://doi.org/10.1007/s00521-016-2577-0
  5. Asteris, P.G., Kolovos, K.G., Douvika, M.G. and Roinos, K. (2016), "Prediction of self-compacting concrete strength using artificial neural networks", Eur. J. Environ. Civil Eng., 20(sup1), s102-s122. https://doi.org/10.1080/19648189.2016.1246693
  6. Atashpaz-Gargari, E. and Lucas, C. (2007), "Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition", IEEE Congress on Evolutionary Computation, Singapore.
  7. Bachir, R., Mohammed, A.M.S. and Habib, T. (2017), "Using artificial neural networks approach to estimate compressive strength for rubberized concrete", Periodica Polytechnica Civil Eng., https://doi.org/10.3311/PPci.11928.
  8. Behnood, A. and Golafshani, E.M. (2018), "Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves", J. Clean. Prod., 202, 54-64 https://doi.org/10.1016/j.jclepro.2018.08.065
  9. Behnood, A., Behnood, V., Gharehveran, M.M. and Alyamac, K.E. (2017), "Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithm", Constr. Build. Mater., 142, 199-207. https://doi.org/10.1016/j.conbuildmat.2017.03.061
  10. 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
  11. Bui, D.K., Nguyen, T., Chou, J.S., Nguyen-Xuan, H. and Ngo, T.D. (2018), "A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete", Constr. Build. Mater., 180, 320-333. https://doi.org/10.1016/j.conbuildmat.2018.05.201
  12. Cheng, M.Y., Firdausi, P.M. and Prayogo, D. (2014), "Highperformance 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
  13. Chithra, S., Kumar, S.S., Chinnaraju, K. and Ashmita, F.A. (2016), "A comparative study on the compressive strength prediction models for high performance concrete containing nano silica and copper slag using regression analysis and artificial neural networks", Constr. Build. Mater., 114, 528-535 https://doi.org/10.1016/j.conbuildmat.2016.03.214
  14. Chopra, P., Sharma, R.K. and Kumar, M. (2016), "Prediction of compressive strength of concrete using artificial neural network and genetic programming", Adv. Mater. Sci. Eng., Article ID 7648467, 10.
  15. Dorofki, M., Elshafie, A.H., Jaafar, O., Karim, O.A. and Mastura, S. (2012), "Comparison of artificial neural network transfer functions abilities to simulate extreme runoff data", Int. Proc. Chem. Biolog Environ. Eng., 33, 39-44.
  16. Gavin, J.B., Holger, R.M. and Graeme, C.D. (2005), "Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a river", J. Hydro., 301(1-4), 93-107. https://doi.org/10.1016/j.jhydrol.2004.06.020
  17. Ghafari, E., Bandarabadi, M., Costa, H. and Julio, E. (2015), "Prediction of fresh and hardened state properties of UHPC: comparative study of statistical mixture design and an artificial neural network model", J. Mater. Civil Eng., 27(11), 04015017. https://doi.org/10.1061/(ASCE)MT.1943-5533.0001270
  18. Hadianfard, M.A. and Jafari, S. (2016), "Prediction of lightweight aggregate concrete compressive strength using ultrasonic pulse velocity test through gene expression programming", Scientia Iranica, Tran. C, Chem. Chem. Eng., 23(6), 2506.
  19. Hajihassani, M., Armaghani, D.J., Marto, A. and Mohamad, E.T. (2015), "Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm", Bull. Eng. Geol. Environ., 74(3), 873-886. https://doi.org/10.1007/s10064-014-0657-x
  20. Heidari, A., Hashempour, M. and Tavakoli, D. (2017), "Using of backpropagation neural network in estimating of compressive strength of waste concrete", Soft Comput. Civil Eng., 1(1), 54-64.
  21. Hola, J., Bien, J., Sadowski, L. and Schabowicz, K. (2015), "Nondestructive and semi-destructive diagnostics of concrete structures in assessment of their durability", Bull. Polish Acad. Sci. Tech. Sci., 63(1), 87-96.
  22. Ji, T., Yang, Y., Fu, M. Y., Chen, B.C. and Wu, H.C. (2017), "Optimum design of reactive powder concrete mixture proportion based on artificial neural and harmony search algorithm", ACI Mater. J., 114(1), 41.
  23. Kao, C.Y., Shen, C.H., Jan, J.C. and Hung, S.L. (2018), "A computer-aided approach to pozzolanic concrete mix design", Adv. Civil Eng., Article ID 4398017, 15.
  24. Kashani, A.R., Gandomi, A.H. and Mousavi, M. (2016), "Imperialistic competitive algorithm: a metaheuristic algorithm for locating the critical slip surface in 2-dimensional soil slopes", Geosci. Front., 7(1), 83-89. https://doi.org/10.1016/j.gsf.2014.11.005
  25. Kaveh, A. (2017), Applications of Metaheuristic Optimization Algorithms in Civil Engineering, Springer, Switzerland.
  26. Khademi, F., Jamal, S.M., Deshpande, N. and Londhe, S. (2016), "Predicting strength of recycled aggregate concrete using artificial neural network, adaptive neuro-fuzzy inference system and multiple linear regression", Int. J. Sustain. Built Environ., 5(2), 355-369. https://doi.org/10.1016/j.ijsbe.2016.09.003
  27. Kiani, B., Gandomi, A.H., Sajedi, S. and Liang, R.Y. (2016), "New formulation of compressive strength of preformed-foam cellular concrete: an evolutionary approach", J. Mater. Civil Eng., 28(10), 04016092. https://doi.org/10.1061/(ASCE)MT.1943-5533.0001602
  28. Kostic, S. and Vasovic, D. (2015), "Prediction model for compressive strength of basic concrete mixture using artificial neural networks", Neur. Comput. Appl., 26(5), 1005-1024. https://doi.org/10.1007/s00521-014-1763-1
  29. Liang, C., Qian, C., Chen, H. and Kang, W. (2018), "Prediction of compressive strength of concrete in wet-dry environment by BP artificial neural networks", Adv. Mater. Sci. Eng., Article ID 6204942, 11.
  30. Maheri, M.R. and Talezadeh, M. (2018), "An enhanced imperialist competitive algorithm for optimum design of skeletal structures", Swarm Evol. Comput., 40, 24-36. https://doi.org/10.1016/j.swevo.2017.12.001
  31. Mashhadban, H., Kutanaei, S.S. and Sayarinejad, M.A. (2016), "Prediction and modeling of mechanical properties in fiber reinforced self-compacting concrete using particle swarm optimization algorithm and artificial neural network", Constr. Build. Mater., 119, 277-287. https://doi.org/10.1016/j.conbuildmat.2016.05.034
  32. Moayedi, H. and Armaghani, D.J. (2018), "Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil", Eng. Comput., 34(2), 347-356. https://doi.org/10.1007/s00366-017-0545-7
  33. Nazari-Shirkouhi, S., Eivazya, H., Ghodsi, R., Rezaiea, K. and Atashpaz-Gargari, E. (2010), "Solving the integrated product mix-outsourcing problem using the imperialist competitive algorithm", Exp. Syst. Appl., 37(12), 7615-7626. https://doi.org/10.1016/j.eswa.2010.04.081
  34. Nikoo, M., Torabian Moghadam, F. and Sadowski, L. (2015), "Prediction of concrete compressive strength by evolutionary artificial neural networks", Adv. Mater. Sci. Eng., Article ID 849126, 8.
  35. Ongpeng, J.M.C., Oreta, A.W.C. and Hirose, S. (2018), "Investigation on the sensitivity of ultrasonic test applied to reinforced concrete beams using neural network", Appl. Sci., 8(3), 405. https://doi.org/10.3390/app8030405
  36. Paul, S.C., Panda, B. and Garg, A. (2018), "A novel approach in modelling of concrete made with recycled aggregates", Measure., 115, 64-72.
  37. Qu, D., Cai, X. and Chang, W. (2018), "Evaluating the effects of steel fibers on mechanical properties of ultra-high performance concrete using artificial neural networks", Appl. Sci., 8(7), 1120. https://doi.org/10.3390/app8071120
  38. Rebouh, R., Boukhatem, B., Ghrici, M. and Tagnit-Hamou, A. (2017), "A practical hybrid NNGA system for predicting the compressive strength of concrete containing natural pozzolan using an evolutionary structure", Constr. Build. Mater., 149, 778-789. https://doi.org/10.1016/j.conbuildmat.2017.05.165
  39. Sadowski, L. and Nikoo, M. (2014), "Corrosion current density prediction in reinforced concrete by imperialist competitive algorithm", Neur. Comput. Appl., 25(7-8), 1627-1638. https://doi.org/10.1007/s00521-014-1645-6
  40. Sadowski, Ł., Nikoo, M. and Nikoo, M. (2017), "Hybrid metaheuristic-neural assessment of the adhesion in existing cement composites", Coat., 7(4), 49. https://doi.org/10.3390/coatings7040049
  41. Saridemir, M., Topcu, I.B., O zcan, F. and Severcan, M.H. (2009), "Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic", Constr. Build. Mater., 23(3), 1279-1286. https://doi.org/10.1016/j.conbuildmat.2008.07.021
  42. Sheikholeslami, R., Khalili, B.G., Sadollah, A. and Kim, J. (2016), "Optimization of reinforced concrete retaining walls via hybrid firefly algorithm with upper bound strategy", KSCE J. Civil Eng., 20(6), 2428-2438. https://doi.org/10.1007/s12205-015-1163-9
  43. Tanyildizi, H. (2018), "Prediction of the strength properties of carbon fiber-reinforced lightweight concrete exposed to the high temperature using artificial neural network and support vector machine", Adv. Civil Eng., Article ID 5140610, 10.
  44. Tashayo, B., Behzadafshar, K., Tehrani, M.S., Banayem, H.A., Hashemi, M.H. and Nezhad, S.S.T. (2018), "Feasibility of imperialist competitive algorithm to predict the surface settlement induced by tunneling", Eng. Comput., 1-7.
  45. Tsai, H.C. (2010), "Predicting strengths of concrete-type specimens using hybrid multilayer perceptrons with centerunified particle swarm optimization", Exp. Syst. Appl., 37(2), 1104-1112. https://doi.org/10.1016/j.eswa.2009.06.093
  46. Tsai, H.C. (2016), "Modeling concrete strength with high-order neural networks", Neur Comput. Appl., 27(8), 2465-2473. https://doi.org/10.1007/s00521-015-2017-6
  47. Uysal, M. and Tanyildizi, H. (2012), "Estimation of compressive strength of self compacting concrete containing polypropylene fiber and mineral additives exposed to high temperature using artificial neural network", Constr. Build. Mater., 27(1), 404-414. https://doi.org/10.1016/j.conbuildmat.2011.07.028
  48. 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

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