Computers and Concrete

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Predicting concrete's compressive strength through three hybrid swarm intelligent methods

  • Zhang Chengquan (Zhejiang Institute of Communications) ;
  • Hamidreza Aghajanirefah (Department of Civil Engineering, Faculty of Engineering, Qazvin Branch Islamic Azad University) ;
  • Kseniya I. Zykova (Department of Mathematics and Natural Sciences, Gulf University for Science and Technology, Mishref Campus) ;
  • Hossein Moayedi (Institute of Research and Development, Duy Tan University) ;
  • Binh Nguyen Le (Institute of Research and Development, Duy Tan University)
  • Received : 2022.11.09
  • Accepted : 2023.04.20
  • Published : 2023.08.25
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Abstract

One of the main design parameters traditionally utilized in projects of geotechnical engineering is the uniaxial compressive strength. The present paper employed three artificial intelligence methods, i.e., the stochastic fractal search (SFS), the multi-verse optimization (MVO), and the vortex search algorithm (VSA), in order to determine the compressive strength of concrete (CSC). For the same reason, 1030 concrete specimens were subjected to compressive strength tests. According to the obtained laboratory results, the fly ash, cement, water, slag, coarse aggregates, fine aggregates, and SP were subjected to tests as the input parameters of the model in order to decide the optimum input configuration for the estimation of the compressive strength. The performance was evaluated by employing three criteria, i.e., the root mean square error (RMSE), mean absolute error (MAE), and the determination coefficient (R2). The evaluation of the error criteria and the determination coefficient obtained from the above three techniques indicates that the SFS-MLP technique outperformed the MVO-MLP and VSA-MLP methods. The developed artificial neural network models exhibit higher amounts of errors and lower correlation coefficients in comparison with other models. Nonetheless, the use of the stochastic fractal search algorithm has resulted in considerable enhancement in precision and accuracy of the evaluations conducted through the artificial neural network and has enhanced its performance. According to the results, the utilized SFS-MLP technique showed a better performance in the estimation of the compressive strength of concrete (R2=0.99932 and 0.99942, and RMSE=0.32611 and 0.24922). The novelty of our study is the use of a large dataset composed of 1030 entries and optimization of the learning scheme of the neural prediction model via a data distribution of a 20:80 testing-to-training ratio.

Keywords

References

  1. Abasi, A.K., Khader, A.T., Al-Betar, M.A., Naim, S., Makhadmeh, S.N. and Alyasseri, Z.A.A. (2020), "Link-based multi-verse optimizer for text documents clustering", Appl. Soft Comput., 87, 106002. https://doi.org/10.1016/j.asoc.2019.106002.
  2. ACI (1995), 318-95: Building Code Requirements for Structural Concrete, American Concrete Institute, Farmington Hills, MI, USA.
  3. Adnan, R.M., Dai, H.L., Kuriqi, A., Kisi, O. and Zounemat-Kermani, M. (2023a), "Improving drought modeling based on new heuristic machine learning methods", Ain Shams Eng. J., 14(10), 102168. https://doi.org/10.1016/j.asej.2023.102168.
  4. Adnan, R.M., Dai, H.L., Mostafa, R.R., Islam, A.R.M.T., Kisi, O., Elbeltagi, A. and Zounemat-Kermani, M. (2023b), "Application of novel binary optimized machine learning models for monthly streamflow prediction", Appl. Water Sci., 13(5), 110. https://doi.org/10.1007/s13201-023-01913-6.
  5. Adnan, R.M., Mostafa, R.R., Dai, H.L., Heddam, S., Kuriqi, A. and Kisi, O. (2023c), "Pan evaporation estimation by relevance vector machine tuned with new metaheuristic algorithms using limited climatic data", Eng. Appl. Comput. Fluid Mech., 17(1), 2192258. https://doi.org/10.1080/19942060.2023.2192258.
  6. Al-Salloum, Y.A., Shah, A.A., Abbas, H., Alsayed, S.H., Almusallam, T.H. and Al-Haddad, M.S. (2012), "Prediction of compressive strength of concrete using neural networks", Comput. Concrete, 10(2), 197-217. https://doi.org/10.12989/cac.2012.10.2.197.
  7. Armaghani, D.J., Hatzigeorgiou, G.D., Karamani, C., Skentou, A., Zoumpoulaki, I. and Asteris, P.G. (2019), "Soft computing-based techniques for concrete beams shear strength", Proc. Struct. Integr., 17, 924-933. https://doi.org/10.1016/j.prostr.2019.08.123. 
  8. Ashteyat, A.M. and Ismeik, M. (2018), "Predicting residual compressive strength of self-compacted concrete under various temperatures and relative humidity conditions by artificial neural networks", Comput. Concrete, 21(1), 47-54. https://doi.org/10.12989/cac.2018.21.1.047.
  9. Asteris, P.G., Ashrafian, A. and Rezaie-Balf, M. (2019), "Prediction of the compressive strength of self-compacting concrete using surrogate models", Comput. Concrete, 24(2), 137-150. https://doi.org/10.12989/cac.2019.24.2.137.
  10. Asteris, P.G., Mamou, A., Hajihassani, M., Hasanipanah, M., Koopialipoor, M., Le, T.T., Kardani, N., Armaghani, D.J. (2021), "Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks", Transp. Geotech., 29, 100588. https://doi.org/10.1016/j.trgeo.2021.100588.
  11. Asteris, P.G. and Mokos, V.G. (2020), "Concrete compressive strength using artificial neural networks", Neural Comput. Appl., 32(15), 11807-11826. https://doi.org/10.1007/s00521-019-04663-2.
  12. Atici, U. (2011), "Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network", Expert Syst. Appl., 38(8), 9609-9618. https://doi.org/10.1016/j.eswa.2011.01.156.
  13. Barrow, J.D., Davies, P.C.W. and Harper Jr, C.L. (2004), Science and Ultimate Reality: Quantum Theory, Cosmology, and Complexity, Cambridge University Press, Cambridge, UK.
  14. Basyigit, C., Akkurt, I., Kilincarslan, S. and Beycioglu, A. (2010), "Prediction of compressive strength of heavyweight concrete by ANN and FL models", Neural Comput. Appl., 19(4), 507-513. https://doi.org/10.1007/s00521-009-0292-9.
  15. 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.
  16. Bui, D.T., Moayedi, H., Kalantar, B., Osouli, A., Pradhan, B., Nguyen, H. and Rashid, A.S.A. (2019), "Harris hawks optimization: A novel swarm intelligence technique for spatial assessment of landslide susceptibility", Sensors, 19, 3590.
  17. 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., 2016, 1. https://doi.org/10.1155/2016/7648467.
  18. Dogan, B. and O lmez, T. (2015), "A new metaheuristic for numerical function optimization: Vortex Search algorithm", Informat. Sci., 293, 125-145. https://doi.org/10.1016/j.ins.2014.08.053.
  19. Dua, D. and Graff, C. (2017), "UCI machine learning repository", University of California, School of Information and Computer Science, Irvine, CA, USA. http://archive.ics.uci.edu/ml.
  20. Dutta, S., Samui, P. and Kim, D. (2018), "Comparison of machine learning techniques to predict compressive strength of concrete", Comput. Concrete, 21(4), 463-470. https://doi.org/10.12989/cac.2018.21.4.463.
  21. Faris, H., Hassonah, M.A., Al-Zoubi, A.M., Mirjalili, S. and Aljarah, I. (2018), "A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture", Neural Comput. Appl., 30(8), 2355-2369. https://doi.org/10.1007/s00521-016-2818-2.
  22. Gazder, U., Al-Amoudi, O.S.B., Khan, S.M.S. and Maslehuddin, M. (2017), "Predicting compressive strength of bended cement concrete with ANNs", Comput. Concrete, 20(6), 627-634. https://doi.org/10.12989/cac.2017.20.6.627.
  23. Golafshani, E.M. and Pazouki, G. (2018), "Predicting the compressive strength of self-compacting concrete containing fly ash using a hybrid artificial intelligence method", Comput. Concrete, 22(4), 419-437. https://doi.org/10.12989/cac.2018.22.4.419.
  24. Gu, Y.T., Xu, Y.X., Moayedi, H., Zhao, J.W. and Le, B.N. (2022), "Slope stability prediction using ANFIS models optimized with metaheuristic science", Geomech. Eng., 31(4), https://doi.org/10.12989/gae.2022.31.4.339.
  25. Haykin, S. (1999), "Neural networks: A guided tour", Soft Comput. Intell. Syst. Theory Appl., 71, 1.
  26. Ikram, R.M.A., Dehrashid, A.A., Zhang, B., Chen, Z., Le, B.N. and Moayedi, H. (2023a), "A novel swarm intelligence: Cuckoo optimization algorithm (COA), and SailFish optimizer (SFO), in landslide susceptibility assessment", Stoch. Environ. Res. Risk Assess., 37(5), 1717-1743. https://doi.org/10.1007/s00477-022-02361-5.
  27. Ikram, R.M.A., Hazarika, B.B., Gupta, D., Heddam, S. and Kisi, O. (2023b), "Streamflow prediction in mountainous region using new machine learning and data preprocessing methods: A case study", Neural Comput. Appl., 35(12), 9053-9070. https://doi.org/10.1007/s00521-022-08163-8.
  28. Keshavarz, Z. and Torkian, H. (2018), "Application of ANN and ANFIS models in determining compressive strength of concrete", J. Soft Comput. Civil Eng., 2(1), 62-70. https://doi.org/10.22115/scce.2018.51114.
  29. Khademi, F. and Behfarnia, K. (2016), "Evaluation of concrete compressive strength using artificial neural network and multiple linear regression models", Int. J. Optim. Civil Eng., 6(3), 423-432.
  30. Khalilpourazari, S., Naderi, B. and Khalilpourazary, S. (2020), "Multi-objective stochastic fractal search: A powerful algorithm for solving complex multi-objective optimization problems", Soft Comput., 24(4), 3037-3066. https://doi.org/10.1007/s00500-019-04080-6.
  31. Kostic, S. and Vasovic, D. (2015), "Prediction model for compressive strength of basic concrete mixture using artificial neural networks", Neural Comput. Appl., 26(5), 1005-1024. https://doi.org/10.1007/s00521-014-1763-1.
  32. Lu, S., Koopialipoor, M., Asteris, P.G., Bahri, M. and Armaghani, D.J. (2020), "A novel feature selection approach based on tree models for evaluating the punching shear capacity of steel fiber-reinforced concrete flat slabs", Mater., 13(17), 3902. https://doi.org/10.3390/ma13173902.
  33. Mehta, P., Kumar, M. and Paulo, J.M. (2014), Concrete: Microstructure, Properties, and Materials, McGraw-Hill Education, New York, NY, USA.
  34. Mirjalili, S., Mirjalili, S.M. and Hatamlou, A. (2016), "Multi-verse optimizer: A nature-inspired algorithm for global optimization", Neural Comput. Appl., 27(2), 495-513. https://doi.org/10.1007/s00521-015-1870-7.
  35. Moayedi, H., Gor, M., Lyu, Z. and Bui, D.T. (2020), "Herding Behaviors of grasshopper and Harris hawk for hybridizing the neural network in predicting the soil compression coefficient", Measure., 152, 107389. https://doi.org/10.1016/j.measurement.2019.107389.
  36. Moayedi, H. and Mosallanezhad, M. (2017), "Uplift resistance of belled and multi-belled piles in loose sand", Measure., 109, 346-353. https://doi.org/10.1016/j.measurement.2017.06.001.
  37. Moayedi, H., Nguyen, H. and Kok Foong, L. (2021), "Nonlinear evolutionary swarm intelligence of grasshopper optimization algorithm and gray wolf optimization for weight adjustment of neural network", Eng. Comput., 37, 1265-1275. https://doi.org/10.1007/s00366-019-00882-2.
  38. Mosallanezhad, M. and Moayedi, H. (2017), "Developing hybrid artificial neural network model for predicting uplift resistance of screw piles", Arab. J. Geosci., 10(22), 479. https://doi.org/10.1007/s12517-017-3285-5.
  39. Mosbah, H. and El-Hawary, M.E. (2018), "Optimized neural network parameters using stochastic fractal technique to compensate Kalman filter for power system-tracking-state estimation", IEEE Trans. Neural Network. Learn. Syst., 30(2), 379-388. https://doi.org/10.1109/TNNLS.2018.2839101.
  40. Neville, A.M. (1995), Properties of Concrete, Longman London, London, UK.
  41. Oreta, A.W.C. and Ongpeng, J. (2011), "Modeling the confined compressive strength of hybrid circular concrete columns using neural networks", Comput. Concrete, 8(5), 597-616. https://doi.org/10.12989/cac.2011.8.5.597.
  42. 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.
  43. Rashid, K. and Rashid, T. (2017), "Fuzzy logic model for the prediction of concrete compressive strength by incorporating green foundry sand", Comput. Concrete, 19(6), 617-623. https://doi.org/10.12989/cac.2017.19.6.617.
  44. Sadowski, L., Nikoo, M. and Nikoo, M. (2018), "Concrete compressive strength prediction using the imperialist competitive algorithm", Comput. Concrete, 22(4), 355-363. https://doi.org/10.12989/cac.2018.22.4.355.
  45. Salimi, H. (2015), "Stochastic fractal search: A powerful metaheuristic algorithm", Knowled. Based Syst., 75, 1-18. https://doi.org/10.1016/j.knosys.2014.07.025.
  46. Shabani, S., Varamesh, S., Moayedi, H. and Le Van, B. (2023), "Modeling the susceptibility of an uneven-aged broad-leaved forest to snowstorm damage using spatially explicit machine learning", Environ. Sci. Pollut. Res., 30(12), 34203-34213. https://doi.org/10.1007/s11356-022-24660-8.
  47. Tahwia, A.M., Heniegal, A., Elgamal, M.S. and Tayeh, B.A. (2021), "The prediction of compressive strength and nondestructive tests of sustainable concrete by using artificial neural networks", Comput. Concrete, 27(1), 21-28. https://doi.org/10.12989/cac.2021.27.1.021.
  48. Tang, C.W., Lin, Y. and Kuo, S.F. (2007), "Investigation on correlation between pulse velocity and compressive strength of concrete using ANNs", Comput. Concrete, 4(6), 477-497. https://doi.org/10.12989/cac.2007.4.6.477.
  49. Tien Bui, D., Abdullahi, M.M., Ghareh, S., Moayedi, H. and Nguyen, H. (2021), "Fine-tuning of neural computing using whale optimization algorithm for predicting compressive strength of concrete", Eng. Comput., 37, 701-712. https://doi.org/10.1007/s00366-019-00850-w.
  50. Tsai, H.C. (2016), "Modeling concrete strength with high-order neural networks", Neural Comput. Appl., 27(8), 2465-2473. https://doi.org/10.1007/s00521-015-2017-6.
  51. Tsai, H.C. and Liao, M.C. (2019), "Knowledge-based learning for modeling concrete compressive strength using genetic programming", Comput. Concrete, 23(4), 255-265. https://doi.org/10.12989/cac.2019.23.4.255.
  52. Wu, D., Liu, S., Moayedi, H., Cifci, M.A. and Le, B.N. (2022), "ANN-Incorporated satin bowerbird optimizer for predicting uniaxial compressive strength of concrete", Steel Compos. Struct., 45(2), 281-291. https://doi.org/10.12989/scs.2022.45.2.281.
  53. Wu, L., Wang, Z., Ma, D., Zhang, J., Wu, G., Wen, S., Zha, M. and Wu, L. (2022a), "A Continuous damage statistical constitutive model for sandstone and mudstone based on triaxial compression tests", Rock Mech. Rock Eng., 55(8), 4963-4978. https://doi.org/10.1007/s00603-022-02924-6.
  54. Wu, L.Y., Ma, D., Wang, Z. and Zhang, J.W. (2022b), "Prediction and prevention of mining-induced water inrush from rock strata separation space by 3D similarity simulation testing: a case study of Yuan Zigou coal mine, China", Geomech. Geophys. Geo-Energy Geo-Resour., 8(6), 202. https://doi.org/10.1007/s40948-022-00518-8.
  55. Wu, L., Ma, D., Wang, Z., Zhang, J., Zhang, B., Li, J., Liao, J. and Tong, J. (2023), "A deep CNN-based constitutive model for describing of statics characteristics of rock materials", Eng. Fract. Mech., 279, 109054. https://doi.org/10.1016/j.engfracmech.2023.109054.
  56. Xue, X. (2018), "Evaluation of concrete compressive strength based on an improved PSO-LSSVM model", Comput. Concrete, 21 (5), 505-511. https://doi.org/10.12989/cac.2018.21.5.505.
  57. Xue, X. and Zhou, H. (2018), "Neuro-fuzzy based approach for estimation of concrete compressive strength", Comput. Concrete, 21(6), 697-703. https://doi.org/10.12989/cac.2018.21.6.697.
  58. Yaprak, H., Karaci, A. and Demir, I. (2013), "Prediction of the effect of varying cure conditions and w/c ratio on the compressive strength of concrete using artificial neural networks", Neural Comput. Appl., 22(1), 133-141. https://doi.org/10.1007/s00521-011-0671-x.
  59. Zhao, Y., Moayedi, H., Bahiraei, M., Foong, L.K. (2020), "Employing TLBO and SCE for optimal prediction of the compressive strength of concrete", Smart. Struct. Syst., 26(6), 753-763. https://doi.org/10.12989/sss.2020.26.6.753.
  60. Zhou, G., Moayedi, H., Foong, L.K. (2021), "Teaching-learning-based metaheuristic scheme for modifying neural computing in appraising energy performance of building", Eng. Comput., 37, 3037-3048. https://doi.org/10.1007/s00366-020-00981-5.