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Slime mold and four other nature-inspired optimization algorithms in analyzing the concrete compressive strength

  • Yinghao Zhao (School of Civil Engineering and Engineering Management, Guangzhou Maritime University) ;
  • Hossein Moayedi (Institute of Research and Development, Duy Tan University) ;
  • Loke Kok Foong (Institute of Research and Development, Duy Tan University) ;
  • Quynh T. Thi (Institute of Research and Development, Duy Tan University)
  • Received : 2020.09.09
  • Accepted : 2023.12.28
  • Published : 2024.01.25
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Abstract

The use of five optimization techniques for the prediction of a strength-based concrete mixture's best-fit model is examined in this work. Five optimization techniques are utilized for this purpose: Slime Mold Algorithm (SMA), Black Hole Algorithm (BHA), Multi-Verse Optimizer (MVO), Vortex Search (VS), and Whale Optimization Algorithm (WOA). MATLAB employs a hybrid learning strategy to train an artificial neural network that combines least square estimation with backpropagation. Thus, 72 samples are utilized as training datasets and 31 as testing datasets, totaling 103. The multi-layer perceptron (MLP) is used to analyze all data, and results are verified by comparison. For training datasets in the best-fit models of SMA-MLP, BHA-MLP, MVO-MLP, VS-MLP, and WOA-MLP, the statistical indices of coefficient of determination (R2) in training phase are 0.9603, 0.9679, 0.9827, 0.9841 and 0.9770, and in testing phase are 0.9567, 0.9552, 0.9594, 0.9888 and 0.9695 respectively. In addition, the best-fit structures for training for SMA, BHA, MVO, VS, and WOA (all combined with multilayer perceptron, MLP) are achieved when the term population size was modified to 450, 500, 250, 150, and 500, respectively. Among all the suggested options, VS could offer a stronger prediction network for training MLP.

Keywords

Acknowledgement

This study was supported by Guangdong Provincial Key Laboratory of Green Construction and Intelligent Operation & Maintenance for Off- shore Infrastructure, and supported by Special Projects in Key Fields of Higher Education in Guangdong Province (No. 2023ZDZX4044).

References

  1. Beekman, M. and Latty, T. (2015), "Brainless but Multi-Headed: Decision Making by the Acellular Slime Mould Physarum polycephalum", J. Molecular Biol., 427(23), 3734-3743. https://doi.org/10.1016/j.jmb.2015.07.007
  2. 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.
  3. Cao, B., Zhao, J., Lv, Z., Gu, Y., Yang, P. and Halgamuge, S.K. (2020), "Multiobjective evolution of fuzzy rough neural network via distributed parallelism for stock prediction", IEEE Transact. Fuzzy Syst., 28(5), 939-952. https://doi.org/10.1109/TFUZZ.2020.2972207
  4. Cemalgil, S., Gul, E., Onat, O. and Aruntas, H.Y. (2022), "A novel prediction model for durability properties of concrete modified with steel fiber and Silica Fume by using Hybridized GRELM", Constr. Build. Mater., 341, 127856. https://doi.org/10.1016/j.conbuildmat.2022.127856
  5. Cemalgil, S., Onat, O. and Aruntas, H.Y. (2023), "An Estimation Proposal for Engineering Properties of Modified Concrete by using Standalone and Hybrid GRELM", Iran. J. Sci. Technol. Transact. Civil Eng., 47(3), 1357-1377. https://doi.org/10.1007/s40996-022-01005-6
  6. Chandwani, V., Agrawal, V. and Nagar, R. (2015), "Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks", Expert Syst. Applicat., 42(2), 885-893. http://doi.org/10.14716/ijtech.v6i2.213
  7. Chatterjee, A., Saha, J. and Mukherjee, J. (2022), "Clustering with multi-layered perceptron", Pattern Recogn. Lett., 155, 92-99. https://doi.org/10.1016/j.patrec.2022.02.009
  8. Chen, H., Heidari, A.A., Chen, H., Wang, M., Pan, Z. and Gandomi, A.H. (2020a), "Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies", Future Generat. Comput. Syst., 111, 175-198. https://doi.org/10.1016/j.future.2020.04.008
  9. Chen, H., Jiao, S., Wang, M., Heidari, A.A. and Zhao, X. (2020b), "Parameters identification of photovoltaic cells and modules using diversification-enriched Harris hawks optimization with chaotic drifts", J. Cleaner Product., 244, 118778. https://doi.org/10.1016/j.jclepro.2019.118778
  10. Chen, L., Chen, Z., Xie, Z., Wei, L., Hua, J., Huang, L. and Yap, P.-S. (2023), "Recent developments on natural fiber concrete: A review of properties, sustainability, applications, barriers, and opportunities", Develop. Built Environ., 16, 100255. https://doi.org/10.1016/j.dibe.2023.100255
  11. Dai, W. (2021), "Safety Evaluation of Traffic System with Historical Data Based on Markov Process and Deep-Reinforcement Learning", J. Computat. Methods Eng. Applicat., 1-14. https://ojs.sgsci.org/journals/jcmea/article/view/141
  12. Dai, W. (2022), "Evaluation and Improvement of Carrying Capacity of a Traffic System", Innov. Appl. Eng. Technol., 1-9. https://doi.org/10.58195/iaet.v1i1.001
  13. Dai, W. (2023), "Design of Traffic Improvement Plan for Line 1 Baijiahu Station of Nanjing Metro", Innov. Appl. Eng. Technol. https://doi.org/10.58195/iaet.v2i1.133
  14. Dogan, B. and Olmez, T. (2015), "A new metaheuristic for numerical function optimization: Vortex Search algorithm", Infor. Sci., 293, 125-145. https://doi.org/10.1016/j.ins.2014.08.053
  15. Dogan, B. and Yuksel, A. (2015), "Analog filter group delay optimization using the Vortex Search algorithm", Proceedings of the 23nd Signal Processing and Communications Applications Conference (SIU), pp. 288-291.
  16. Gardner, M.W. and Dorling, S. (1998), "Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences", Atmosph. Environ., 32(14-15), 2627-2636. https://doi.org/10.1016/S1352-2310(97)00447-0
  17. Greco, C., Pace, P., Basagni, S. and Fortino, G. (2021), "Jamming detection at the edge of drone networks using multi-layer perceptrons and decision trees", Appl. Soft Comput., 111, 107806. https://doi.org/10.1016/j.asoc.2021.107806
  18. Guo, M., Huang, H., Zhang, W., Xue, C. and Huang, M. (2022), "Assessment of RC frame capacity subjected to a loss of corner column", J. Struct. Eng., 148(9), 04022122. https://doi.org/10.1061/(ASCE)ST.1943-541X.0003423
  19. Han, Y., Shao, S., Fang, B., Shi, T., Zhang, B., Wang, X. and Zhao, X. (2023), "Chloride ion penetration resistance of matrix and interfacial transition zone of multi-walled carbon nanotube-reinforced concrete", J. Build. Eng., 72, 106587. https://doi.org/10.1016/j.jobe.2023.106587
  20. Hashim, F.A., Houssein, E.H., Mabrouk, M.S., Al-Atabany, W. and Mirjalili, S. (2019), "Henry gas solubility optimization: A novel physics-based algorithm", Future Generat. Comput. Syst., 101, 646-667. https://doi.org/10.1016/j.future.2019.07.015
  21. Hashim, F.A., Houssein, E.H., Hussain, K., Mabrouk, M.S. and Al-Atabany, W. (2020), "A modified Henry gas solubility optimization for solving motif discovery problem", Neural Comput. Applicat., 32(14), 10759-10771. https://doi.org/10.1007/s00521-019-04611-0
  22. He, H., Shuang, E., Ai, L., Wang, X., Yao, J., He, C. and Cheng, B. (2023), "Exploiting machine learning for controlled synthesis of carbon dots-based corrosion inhibitors", J. Cleaner Product., 419, 138210. https://doi.org/10.1016/j.jclepro.2023.138210
  23. Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M. and Chen, H. (2019), "Harris hawks optimization: Algorithm and applications", Future Generat. Comput. Syst., 97, 849-872. https://doi.org/10.1016/j.future.2019.02.028
  24. Hu, D., Li, Y., Yang, X., Liang, X., Zhang, K. and Liang, X. (2023), "Experiment and Application of NATM Tunnel Deformation Monitoring Based on 3D Laser Scanning", Structural Control and Health Monitoring, 2023, 3341788. https://doi.org/10.1155/2023/3341788
  25. Huang, B. and Wang, J. (2020), "Deep-reinforcement-learning-based capacity scheduling for PV-battery storage system", IEEE Transact. Smart Grid, 12(3), 2272-2283. https://doi.org/10.1109/TSG.2020.3047890
  26. Huang, B. and Wang, J. (2022), "Applications of physics-informed neural networks in power systems-a review", IEEE Transact. Power Syst., 38(1), 572-588. https://doi.org/10.1109/TPWRS.2022.3162473
  27. Huang, H., Yuan, Y., Zhang, W. and Li, M. (2021a), "Seismic behavior of a replaceable artificial controllable plastic hinge for precast concrete beam-column joint", Eng. Struct., 245, 112848. https://doi.org/10.1016/j.engstruct.2021.112848
  28. Huang, H., Yuan, Y., Zhang, W. and Zhu, L. (2021b), "Property assessment of high-performance concrete containing three types of fibers", Int. J. Concrete Struct. Mater., 15(1), 39. https://doi.org/10.1186/s40069-021-00476-7
  29. Huang, H., Guo, M., Zhang, W. and Huang, M. (2022a), "Seismic Behavior of Strengthened RC Columns under Combined Loadings", J. Bridge Eng., 27(6), 05022005. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001871
  30. Huang, H., Li, M., Yuan, Y. and Bai, H. (2022b), "Theoretical analysis on the lateral drift of precast concrete frame with replaceable artificial controllable plastic hinges", J. Build. Eng., 62, 105386. https://doi.org/10.1016/j.jobe.2022.105386
  31. Huang, H., Li, M., Zhang, W. and Yuan, Y. (2022c), "Seismic behavior of a friction-type artificial plastic hinge for the precast beam-column connection", Arch. Civil Mech. Eng., 22(4), 201. https://doi.org/10.1007/s43452-022-00526-1
  32. Jiang, S., Zhao, C., Zhu, Y., Wang, C. and Du, Y. (2022), "A practical and economical ultra-wideband base station placement approach for indoor autonomous driving systems", J. Adv. Transport. https://doi.org/10.1155/2022/3815306
  33. Kaveh, A. and Bakhshpoori, T. (2013), "Optimum design of space trusses using cuckoo search algorithm with levy flights". http://doi.org/10.22099/IJSTC.2013.945
  34. Kumar, S., Datta, D. and Singh, S.K. (2015), "Black hole algorithm and its applications", In: Computational intelligence applications in modeling and control, pp. 147-170. https://doi.org/10.1007/978-3-319-11017-2_7
  35. Latty, T. and Beekman, M. (2010), "Food quality and the risk of light exposure affect patch-choice decisions in the slime mold Physarum polycephalum", Ecology, 91(1), 22-27. https://doi.org/10.1890/09-0358.1
  36. Li, S., Chen, H., Wang, M., Heidari, A.A. and Mirjalili, S. (2020), "Slime mould algorithm: A new method for stochastic optimization", Future Generat. Comput. Syst., 111, 300-323. https://doi.org/10.1016/j.future.2020.03.055
  37. Li, J., Chen, M. and Li, Z. (2022), "Improved soil-structure interaction model considering time-lag effect", Comput. Geotech., 148, 104835. https://doi.org/10.1016/j.compgeo.2022.104835
  38. Li, Z., Gao, M., Lei, Z., Tong, L., Sun, J., Wang, Y., Wang, X. and Jiang, X. (2023a), "Ternary cementless composite based on red mud, ultra-fine fly ash, and GGBS: Synergistic utilization and geopolymerization mechanism", Case Stud. Constr. Mater., 19, e02410. https://doi.org/10.1016/j.cscm.2023.e02410
  39. Li, Z., Kong, Y. and Jiang, C. (2023b), "A Transfer Double Deep Q Network Based DDoS Detection Method for Internet of Vehicles", IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/TVT.2022.3233880
  40. Lim, C.-H., Yoon, Y.-S. and Kim, J.-H. (2004), "Genetic algorithm in mix proportioning of high-performance concrete", Cement Concrete Res., 34(3), 409-420. https://doi.org/10.1016/j.cemconres.2003.08.018
  41. Liu, C., Cui, J., Zhang, Z., Liu, H., Huang, X. and Zhang, C. (2021), "The role of TBM asymmetric tail-grouting on surface settlement in coarse-grained soils of urban area: Field tests and FEA modelling", Tunnell. Undergr. Space Technol., 111, 103857. https://doi.org/10.1016/j.tust.2021.103857
  42. Liu, C., Peng, Z., Cui, J., Huang, X., Li, Y. and Chen, W. (2023), "Development of crack and damage in shield tunnel lining under seismic loading: Refined 3D finite element modeling and analyses", Thin-Wall. Struct., 185, 110647. https://doi.org/10.1016/j.tws.2023.110647
  43. McCulloch, W.S. and Pitts, W. (1943), "A logical calculus of the ideas immanent in nervous activity", Bull. Mathe. Biophys., 5(4), 115-133. https://doi.org/10.1007/BF02478259
  44. Mehrabi, M. and Moayedi, H. (2021), "Landslide susceptibility mapping using artificial neural network tuned by metaheuristic algorithms", Environ. Earth Sci., 80, 1-20. https://doi.org/10.1007/s12665-021-10098-7
  45. Mirjalili, S. and Lewis, A. (2016), "The whale optimization algorithm", Adv. Eng. Software, 95, 51-67. https://doi.org/10.1016/j.advengsoft.2016.01.008
  46. Mirjalili, S., Mirjalili, S.M. and Hatamlou, A. (2016), "Multi-verse optimizer: a nature-inspired algorithm for global optimization", Neural Comput. Applicat., 27(2), 495-513. https://doi.org/10.1007/s00521-015-1870-7
  47. Moayedi, H. and Hayati, S. (2018a), "Applicability of a CPT-Based Neural Network Solution in Predicting Load-Settlement Responses of Bored Pile", Int. J. Geomech., 18(6), 06018009. http://doi.org/10.1061/(ASCE)GM.1943-5622.0001125
  48. Moayedi, H. and Hayati, S. (2018b), "Modelling and optimization of ultimate bearing capacity of strip footing near a slope by soft computing methods", Appl. Soft Comput., 66, 208-219. https://doi.org/10.1016/j.asoc.2018.02.027
  49. Moayedi, H. and Jahed Armaghani, D. (2018), "Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil", Eng. Comput., 34(2), 347-356. http://doi.org/10.1007/s00366-017-0545-7
  50. Moayedi, H. and Mosavi, A. (2021), "Hybridizing neural network with multi-verse, black hole, and shuffled complex evolution optimizer algorithms predicting the dissolved oxygen". https://doi.org/10.20944/preprints202101.0464.v1
  51. Moayedi, H., Huat, B.B., Ali, T.A.M., Asadi, A., Moayedi, F. and Mokhberi, M. (2011), "Preventing landslides in times of rainfall: case study and FEM analyses", Disaster Prevent. and Manage.: Int. J., 20(2), 115-124. http://doi.org/10.1108/09653561111126067
  52. Moayedi, H., Kalantar, B., Foong, L.K., Tien Bui, D. and Motevalli, A. (2019), "Application of three metaheuristic techniques in simulation of concrete slump", Appl. Sci., 9(20), 4340. http://doi.org/10.3390/app9204340
  53. Moayedi, H., Abdullahi, M.a.M., Nguyen, H. and Rashid, A.S.A. (2021a), "Comparison of dragonfly algorithm and Harris hawks optimization evolutionary data mining techniques for the assessment of bearing capacity of footings over two-layer foundation soils", Eng. Comput., 37(1), 437-447. http://doi.org/10.1007/s00366-019-00834-w
  54. Moayedi, H., Osouli, A., Nguyen, H. and Rashid, A.S.A. (2021b), "A novel Harris hawks' optimization and k-fold cross-validation predicting slope stability", Eng. Comput., 37(1), 369-379. http://doi.org/10.1007/s00366-019-00828-8
  55. Moayedi, H., Foong, L.K. and Le, B.N. (2023), "Three intelligent computational models to predict the high-performance concrete mixture", Neural Comput. Applicat., 1-20. https://doi.org/10.1007/s00521-023-09233-1
  56. Nasiri, J. and Khiyabani, F.M. (2018), "A whale optimization algorithm (WOA) approach for clustering", Cogent Mathe. Statist., 5(1), 1483565. https://doi.org/10.1080/25742558.2018.1483565
  57. Nazir, R., Moayedi, H., Pratikso, A. and Mosallanezhad, M. (2015), "The uplift load capacity of an enlarged base pier embedded in dry sand", Arab. J. Geosci., 8, 7285-7296. https://doi.org/10.1007/s12517-014-1721-3
  58. Ren, Z., Zeng, H., Zeng, X., Chen, X. and Wang, X. (2023), "Effect of nanographite conductive concrete mixed with magnetite sand excited by different alkali activators and their combinations on the properties of conductive concrete", Buildings, 13(7), 1630. https://doi.org/10.3390/buildings13071630
  59. Shehabeldeen, T.A., Elaziz, M.A., Elsheikh, A.H., Hassan, O.F., Yin, Y., Ji, X., Shen, X. and Zhou, J. (2020), "A novel method for predicting tensile strength of friction stir welded AA6061 aluminium alloy joints based on hybrid random vector functional link and henry gas solubility optimization", IEEE Access, 8, 79896-79907. https://doi.org/10.1109/ACCESS.2020.2990137
  60. Shi, T., Liu, Y., Hu, Z., Cen, M., Zeng, C., Xu, J. and Zhao, Z. (2022), "Deformation performance and fracture toughness of carbon nanofiber-modified cement-based materials", ACI Materials Journal, 119(5), 119-128. https://doi.org/10.14359/51735976
  61. Shi, M.-L., Lv, L. and Xu, L. (2023), "A multi-fidelity surrogate model based on extreme support vector regression: fusing different fidelity data for engineering design", Eng. Computat., 40(2), 473-493. https://doi.org/10.1108/EC-10-2021-0583
  62. Singh, A., Wang, Y., Zhou, Y., Sun, J., Xu, X., Li, Y., Liu, Z., Chen, J. and Wang, X. (2023), "Utilization of antimony tailings in fiber-reinforced 3D printed concrete: A sustainable approach for construction materials", Constr. Build. Mater., 408, 133689. https://doi.org/10.1016/j.conbuildmat.2023.133689
  63. Sun, L., Wang, C., Zhang, C., Yang, Z., Li, C. and Qiao, P. (2022), "Experimental investigation on the bond performance of sea sand coral concrete with FRP bar reinforcement for marine environments", Adv. Struct. Eng., 26(3), 533-546. https://doi.org/10.1177/13694332221131153
  64. Tien Bui, D., Abdullahi, M.a.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(1), 701-712. https://doi.org/10.1007/s00366-019-00850-w
  65. Trivedi, I.N., Jangir, P., Kumar, A., Jangir, N. and Totlani, R. (2018), Advances in computer and computational sciences, Springer, pp. 53-60.
  66. Wang, M., Yang, X. and Wang, W. (2022), "Establishing a 3D aggregates database from X-ray CT scans of bulk concrete", Constr. Build. Mater., 315, 125740. https://doi.org/10.1016/j.conbuildmat.2021.125740
  67. Wenjun, D., Fatahizadeh, M., Touchaei, H.G., Moayedi, H. and Foong, L.K. (2023), "Application of six neural network-based solutions on bearing capacity of shallow footing on double-layer soils", Steel Compos. Struct., Int. J., 49(2), 231-244. https://doi.org/10.12989/scs.2023.49.2.231
  68. Yildiz, B.S., Yildiz, A.R., Pholdee, N., Bureerat, S., Sait, S.M. and Patel, V. (2020), "The Henry gas solubility optimization algorithm for optimum structural design of automobile brake components", Mater. Test., 62(3), 261-264. https://doi.org/10.3139/120.111479
  69. Zhang, Y. and Zhang, H. (2023), "Enhancing Robot Path Planning through a Twin-Reinforced Chimp Optimization Algorithm and Evolutionary Programming Algorithm", IEEE Access. https://doi.org/10.1109/ACCESS.2023.3337602
  70. Zhang, Z., Li, W. and Yang, J. (2021), "Analysis of stochastic process to model safety risk in construction industry", J. Civil Eng. Manage., 27(2), 87-99. https://doi.org/10.3846/jcem.2021.14108
  71. Zhang, Y., Gono, R. and Jasinski, M. (2023), "An Improvement in Dynamic Behavior of Single Phase PM Brushless DC Motor Using Deep Neural Network and Mixture of Experts", IEEE Access. https://doi.org/10.1109/ACCESS.2023.3289409
  72. Zhang, Y., Abdullah, S., Ullah, I. and Ghani, F. (2024), "A new approach to neural network via double hierarchy linguistic information: Application in robot selection", Eng. Applicat. Artif. Intell., 129, 107581. https://doi.org/10.1016/j.engappai.2023.107581
  73. Zhao, Y., Dai, W., Wang, Z. and Ragab, A.E. (2023), "Application of computer simulation to model transient vibration responses of GPLs reinforced doubly curved concrete panel under instantaneous heating", Mater. Today Commun., 107949. https://doi.org/10.1016/j.mtcomm.2023.107949
  74. Zhou, S., Lu, C., Zhu, X. and Li, F. (2021), "Preparation and Characterization of High-Strength Geopolymer Based on BH-1 Lunar Soil Simulant with Low Alkali Content", Engineering, 7(11), 1631-1645. https://doi.org/10.1016/j.eng.2020.10.016
  75. Zhou, F., Jiang, H., Huang, L., Hu, Y., Xie, Z., Zeng, Z., Liu, M., Wang, B. and Zhou, X. (2023a), "Early shrinkage modeling of complex internally confined concrete based on capillary tension theory", Buildings, 13(9), 2201.
  76. Zhou, F., Li, W., Hu, Y., Huang, L., Xie, Z., Yang, J., Wu, D. and Chen, Z. (2023b), "Moisture diffusion coefficient of concrete under different conditions", Buildings, 13(10), 2421. https://doi.org/10.3390/buildings13102421
  77. Zhu, X., Gao, Y., Dai, Z., Corr, D.J. and Shah, S.P. (2018), "Effect of interfacial transition zone on the Young's modulus of carbon nanofiber reinforced cement concrete", Cement Concrete Res., 107, 49-63. https://doi.org/10.1016/j.jobe.2022.105248