Advances in concrete construction

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EDNN based prediction of strength and durability properties of HPC using fibres & copper slag

  • Gupta, Mohit (Civil Engineering Department, Delhi Technological University, Shahbad Daulatpur) ;
  • Raj, Ritu (Civil Engineering Department, Delhi Technological University, Shahbad Daulatpur) ;
  • Sahu, Anil Kumar (Civil Engineering Department, Delhi Technological University, Shahbad Daulatpur)
  • Received : 2021.11.07
  • Accepted : 2022.10.06
  • Published : 2022.09.25
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Abstract

For producing cement and concrete, the construction field has been encouraged by the usage of industrial soil waste (or) secondary materials since it decreases the utilization of natural resources. Simultaneously, for ensuring the quality, the analyses of the strength along with durability properties of that sort of cement and concrete are required. The prediction of strength along with other properties of High-Performance Concrete (HPC) by optimization and machine learning algorithms are focused by already available research methods. However, an error and accuracy issue are possessed. Therefore, the Enhanced Deep Neural Network (EDNN) based strength along with durability prediction of HPC was utilized by this research method. Initially, the data is gathered in the proposed work. Then, the data's pre-processing is done by the elimination of missing data along with normalization. Next, from the pre-processed data, the features are extracted. Hence, the data input to the EDNN algorithm which predicts the strength along with durability properties of the specific mixing input designs. Using the Switched Multi-Objective Jellyfish Optimization (SMOJO) algorithm, the weight value is initialized in the EDNN. The Gaussian radial function is utilized as the activation function. The proposed EDNN's performance is examined with the already available algorithms in the experimental analysis. Based on the RMSE, MAE, MAPE, and R2 metrics, the performance of the proposed EDNN is compared to the existing DNN, CNN, ANN, and SVM methods. Further, according to the metrices, the proposed EDNN performs better. Moreover, the effectiveness of proposed EDNN is examined based on the accuracy, precision, recall, and F-Measure metrics. With the already-existing algorithms i.e., JO, GWO, PSO, and GA, the fitness for the proposed SMOJO algorithm is also examined. The proposed SMOJO algorithm achieves a higher fitness value than the already available algorithm.

Keywords

Acknowledgement

The research described in this paper is supported by Civil engineering laboratory of the Department of Civil Engineering for the availability of the software MATLAB and the library of Delhi Technological University for accessing the research papers.

References

  1. Abellan-Garcia, J. (2021), "Artificial neural network model for strength prediction of ultra-high-performance concrete", ACI Mater. J., 118(4), 3-14. https://doi.org/10.14359/51732710
  2. Afroughsabet, V. and Ozbakkaloglu, T. (2015), "Mechanical and durability properties of high-strength concrete containing steel and polypropylene fibers", Constr. Build. Mater., 94, 73-82. https://doi.org/10.1016/j.conbuildmat.2015.06.051
  3. Ali, B., Yilmaz, E., Tahir, A.R., Gamaoun, F., El Ouni, M.H. and Murtaza Rizvi, S.M. (2021), "The Durability of High-Strength Concrete Containing Waste Tire Steel Fiber and Coal Fly Ash", Adv. Mater. Sci. Eng., 2021, Article ID 7329685. https://doi.org/10.1155/2021/7329685
  4. Ali, B., Fahad, M., Ullah, S., Ahmed, H., Alyousef, R. and Deifalla, A. (2022), "Development of Ductile and Durable High Strength Concrete (HSC) through Interactive Incorporation of Coir Waste and Silica Fume", Materials, 15(7), 2616. https://doi.org/10.3390/ma15072616
  5. Alyousef, R., Ali, B., Mohammed, A., Kurda, R., Alabduljabbar, H. and Riaz, S. (2021), "Evaluation of Mechanical and Permeability Characteristics of Microfiber-Reinforced Recycled Aggregate Concrete with Different Potential Waste Mineral Admixtures", Materials, 14(20), 5933. https://doi.org/10.3390/ma14205933
  6. Asteris, P.G., Naseri, H., Hajihassani, M., Kharghani, M. and Chalioris, C.E. (2021), "On the mechanical characteristics of fiber reinforced polymer concrete", Adv. Concrete Constr., Int. J., 12(4), 271-282. https://doi.org/10.12989/acc.2021.12.4.271
  7. 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
  8. 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
  9. Dadmand, B., Pourbaba, M., Sadaghian, H. and Mirmiran, A. (2020), "Effectiveness of steel fibers in ultra-high-performance fiber-reinforced concrete construction", Adv. Concrete Constr, Int. J., 10(3), 195-209. https://doi.org/10.12989/acc.2020.10.3.195
  10. Deng, F., Xu, L., Chi, Y., Wu, F. and Chen, Q. (2020), "Effect of steel polypropylene hybrid fiber and coarse aggregate inclusion on the stress strain behavior of ultra-high performance concrete under uniaxial compression", Compos. Struct., 252, 1-18. https://doi.org/10.1016/j.compstruct.2020.112685
  11. Eisa, A.S., Shehab, H.K., El-Awady, K.A. and Nawar, M.T. (2021), "Improving the flexural toughness behavior of R.C beams using micro/nano silica and steel fibers", Adv. Concrete Constr., Int. J., 11(1), 45-58. https://doi.org/10.12989/acc.2021.11.1.045
  12. Federowicz, K., Techman, M., Sanytsky, M. and Sikora, P. (2021), "Modification of lightweight aggregate concretes with silica nanoparticles a review", Materials, 14(15), 1-23. https://doi.org/10.3390/ma14154242
  13. Fediuk, R., Smoliakov, A. and Muraviov, A. (2017), "Mechanical properties of fiber-reinforced concrete using composite binders", Hindawi Adv. Mater. Sci. Eng. https://doi.org/10.1155/2017/2316347
  14. Fediuk, R., Mosaberpanah, M.A. and Lesovik, V. (2020), "Development of fiber reinforced self-compacting concrete (FRSCC): Towards an efficient utilization of quaternary composite binders and fibres", Adv. Concrete Constr., Int. J., 9(4), 387-395. https://doi.org/10.12989/acc.2020.9.4.387
  15. Fu, Q., Bu, M., Xu, W., Chen, L., Li, D., He, J., Kou, H. and Li, H. (2020), "Comparative analysis of dynamic constitutive response of hybrid fibre-reinforced concrete with different matrix strengths", Int. J. Impact Eng., 148, 103763. https://doi.org/10.1016/j.ijimpeng.2020.103763
  16. Golafshani, E.M., Behnood, A. and Arashpour, M. (2020), "Predicting the compressive strength of normal and highperformance concretes using ANN and ANFIS hybridized with grey Wolf Optimizer", Constr. Build. Mater., 232, 1-14. https://doi.org/10.1016/j.conbuildmat.2019.117266
  17. Hussain, I., Ali, B., Rashid, M.U., Amir, M.T., Riaz, S. and Ali, A. (2021), "Engineering properties of factory manufactured paving blocks utilizing steel slag as cement replacement", Case Studies Constr. Mater., 15, e00755. https://doi.org/10.1016/j.cscm.2021.e00755
  18. Iftikhar, B., Alih, S.C., Vafaei, M., Elkotb, M.A., Shutaywi, M., Javed, M.F., Deebani, W., Khan, M.I. and Aslam, F. (2022), "Predictive modeling of compressive strength of sustainable rice husk ash concrete: Ensemble learner optimization and comparison", J. Cleaner Product., 348, 131285. https://doi.org/10.1016/j.jclepro.2022.131285
  19. Khan, K.K.Q. and Changhode, B.B. (2021), "Partial replacement of fly ash based geopolymer concrete with copper slag as fine aggregate", Int. Res. J. Eng. Technol., 8(5), 4016-4021.
  20. Khan, M., Cao, M. and Ali, M. (2018), "Effect of basalt fibers on mechanical properties of calcium carbonate whisker-steel fiber reinforced concrete", Constr. Build. Mater., 192, 742-753. https://doi.org/10.1016/j.conbuildmat.2018.10.159
  21. Kumar, S. and Rai, B. (2021), "Durability performance and microstructure of binary blended high-performance concrete", Innov. Infrastr. Solut., 6(3), 1-17. https://doi.org/10.1007/s41062-021-00525-w
  22. Le-Duc, T., Nguyen, Q.H. and Nguyen-Xuan, H. (2020), "Balancing composite motion optimization", Inform. Sci., 520, 250-270. https://doi.org/10.1016/j.ins.2020.02.013
  23. Maharishi, A., Singh, S.P. and Gupta, L.K. (2020), "Strength and durability studies on slag cement concrete made with copper slag as fine aggregates", Mater. Today Proceed., 38, 2639-2648. https://doi.org/10.1016/j.matpr.2020.08.2322214-7853
  24. Marani, A., Jamali, A. and Nehdi, M.L. (2020), "Predicting ultrahigh- performance concrete compressive strength using tabular generative adversarial networks", Materials, 13(21), 1-24. https://doi.org/10.3390/ma13214757
  25. Mazloom, M., Karimpanah, H. and Karamloo, M. (2020), "Fracture behavior of monotype and hybrid fiber reinforced self-compacting concrete at different temperatures", Adv. Concrete Constr., Int. J., 9(4), 375-386. https://doi.org/10.12989/acc.2020.9.4.375
  26. Perumal, R. and Prabakaran, V. (2020), "Estimating the compressive strength of HPFRC containing metallic fibers using statistical methods and ANNs", Adv. Concrete Constr., Int. J., 10(6), 479-488. https://doi.org/10.12989/acc.2020.10.6.479
  27. Raj, A., Sathyan, D. and Mini, K.M. (2021), "Performance evaluation of natural fiber reinforced high volume fly ash foam concrete cladding", Adv. Concrete Constr., Int. J., 11(2), 151-161. https://doi.org/10.12989/acc.2021.11.2.151
  28. Rasheed, M.F., Rahim, A., Irfan-ul-Hassan, M., Ali, B. and Ali, N. (2022), "Sulfur concrete made with waste marble and slag powders: 100% recycled and waterless concrete", Environ. Sci. Pollut. Res., 1-15. https://doi.org/10.1007/s11356-022-20456-y
  29. Sankar, B. and Ramadoss, P. (2021), "Review on fiber hybridization in ternary blended high-performance Concrete", Mater. Today Proceed., 45, 4919-4924. https://doi.org/10.1016/j.matpr.2021.01.3662214-7853
  30. Sebastin, S. (2021), "Combined effect of copper slag and hybrid fiber on compressive strength in high strength concrete", Int. J. Adv. Civil Eng. Technol., 6(1), 27-64.
  31. Sharafati, A., Naderpour, H., Salih, S.Q., Onyari, E. and Yaseen, Z.M. (2021), "Simulation of foamed concrete compressive strength prediction using adaptive neuro-fuzzy inference system optimized by nature-inspired algorithms", Frontiers Struct. Civil Eng., 15(1), 61-79. https://doi.org/10.1007/s11709-020-0684-6
  32. Singh, N.K. and Rai, B. (2018), "A review of fiber synergy in hybrid fiber reinforced concrete", J. Appl. Eng. Sci., 8(21), 41-50. https://doi.org/10.2478/jaes-2018-0017
  33. Tayeh, B.A., Yousif, S.T., Abu Bakar, B.H., Al-Tayeb, M.M., Abdul-Razzak, A.A. and Haido, J.H. (2021), "Dynamic response of reinforced concrete members incorporating steel fibers with different aspect ratios", Adv. Concrete Constr., Int. J., 11(2), 89-98. https://doi.org/10.12989/acc.2021.11.2.089
  34. Teng, S., Afroughsabet, V. and Ostertag, C.P. (2018), "Flexural behavior and durability properties of high performance hybridfiber- reinforced concrete", Constr. Build. Mater., 182, 504-515. https://doi.org/10.1016/j.conbuildmat.2018.06.158
  35. Turk, K., Kina, C. and Oztekin, E. (2020), "Effect of macro and micro fiber volume on the flexural performance of hybrid fiber reinforced SCC", Adv. Concrete Constr., Int. J., 10(3), 257-269. https://doi.org/10.12989/acc.2020.10.3.257
  36. Wu, Y. and Zhou, Y. (2022), "Hybrid machine learning model and Shapley additive explanations for compressive strength of sustainable concrete", Constr. Build. Mater., 330, 127298. https://doi.org/10.1016/j.conbuildmat.2022.127298
  37. Yan, X., Gao, Y., Luo, Y., Bi, Y. and Xie, Y. (2021), "Effect of different steel fiber types on mechanical properties of ultra-high performance concrete", Mater. Sci. Eng., 1167(1), 1-9. https://doi.org/10.1088/1757-899X/1167/1/012001
  38. Yu, Y., Li, W., Li, J. and Nguyen, T.N. (2018), "A novel optimised self-learning method for compressive strength prediction of high performance concrete", Constr. Build. Mater., 184, 229-247. https://doi.org/10.1016/j.conbuildmat.2018.06.219
  39. Zahiri, F. and Eskandari-Naddaf, H. (2018), "Optimizing the compressive strength of concrete containing micro-silica, nanosilica and polypropylene fibers using extreme vertices mixture design", Frontiers Struct. Civil Eng., 13(4), 821-830. https://doi.org/10.1007/s11709-019-0518-6
  40. Zhang, P., Wan, J., Wang, K. and Li, Q. (2017), "Influence of nano-SiO2 on properties of fresh and hardened high performance concrete : A state-of-the-art review", Constr. Build. Mater., 148, 648-658. https://doi.org/10.1016/j.conbuildmat.2017.05.059
  41. Zhang, Z., Paul, S.C., Panda, B., Huang, Y., Garg, A., Zhang, Y., Garg, A. and Zhang, W. (2020), "Assessment of flexural and splitting strength of steel fiber reinforced concrete using automated neural network search", Adv. Concrete Constr., Int. J., 10(1), 81-92. https://doi.org/10.12989/acc.2020.10.1.081
  42. Zhang, J.W., Li, S.J. and Peng, H.J. (2021), "Experimental investigation of multiscale hybrid fibres on the mechanical properties of high-performance concrete", Constr. Build. Mater, 299, 123895. https://doi.org/10.1016/j.conbuildmat.2021.123895