Smart Structures and Systems

  • 제26권2호
  • /
  • Pages.241-251
  • /
  • 2020
  • /
  • 1738-1584(pISSN)
  • /
  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Swarm-based hybridizations of neural network for predicting the concrete strength

  • Ma, Xinyan (China Airport Planning & Design Institute Co., Ltd.) ;
  • Foong, Loke Kok (Department for Management of Science and Technology Development, Ton Duc Thang University) ;
  • Morasaei, Armin (Department of Civil Engineering, K.N. Toosi University of Technology) ;
  • Ghabussi, Aria (Department of Civil Engineering, Central Tehran Branch, Islamic Azad University) ;
  • Lyu, Zongjie (Institute of Research and Development, Duy Tan University)
  • 투고 : 2019.12.08
  • 심사 : 2020.05.29
  • 발행 : 2020.08.25
⟨ 이전 논문 다음 논문 ⟩

초록

Due to the undeniable importance of approximating the concrete compressive strength (CSC) in civil engineering, this paper focuses on presenting four novel optimizations of multi-layer perceptron (MLP) neural network, namely artificial bee colony (ABC-MLP), grasshopper optimization algorithm (GOA-MLP), shuffled frog leaping algorithm (SFLA-MLP), and salp swarm algorithm (SSA-MLP) for predicting this crucial parameter. The used dataset consists of 103 rows of information concerning seven influential parameters (cement, slag, water, fly ash, superplasticizer, fine aggregate, and coarse aggregate). In this work, the best-fitted complexity of each ensemble is determined by a population-based sensitivity analysis. The GOA distinguished its self by the least complexity (population size = 50) and emerged as the second time-effective optimizer. Referring to the prediction results, all tested algorithms are able to construct reliable networks. However, the SSA (Correlation = 0.9652 and Error = 1.3939) and GOA (Correlation = 0.9629 and Error = 1.3922) performed more accurately than ABC (Correlation = 0.7060 and Error = 4.0161) and SFLA (Correlation = 0.8890 and Error = 2.5480). Therefore, the SSA-MLP and GOA-MLP can be promising alternatives to laboratorial and traditional CSC evaluative methods.

키워드

참고문헌

  1. Abbassi, R., Abbassi, A., Heidari, A.A. and Mirjalili, S. (2019), "An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models", Energy Convers. Manage., 179, 362-372. https://doi.org/10.1016/j.enconman.2018.10.069
  2. Abrams, D.A. (1927), "Water-cement ratio as a basis of concrete quality", J. Proceedings, 23(2), 452-457.
  3. Ahmed, S., Mafarja, M., Faris, H. and Aljarah, I. (2018), "Feature selection using salp swarm algorithm with chaos", Proceedings of the 2nd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, New York, NY, USA, March, pp. 65-69. https://doi.org/10.1145/3206185.3206198
  4. Akande, K.O., Owolabi, T.O., Twaha, S. and Olatunji, S.O. (2014), "Performance comparison of SVM and ANN in predicting compressive strength of concrete", IOSR J. Comput. Eng., 16(5), 88-94.
  5. Akin, O. and Sahin, M. (2017), "Active neuro-adaptive vibration suppression of a smart beam", Smart. Struct. Syst., Int. J., 20(6), 657-668. https://doi.org/10.12989/sss.2017.20.6.657
  6. Aljarah, I., Ala'M, A.Z., Faris, H., Hassonah, M.A., Mirjalili, S. and Saadeh, H (2018), "Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm", Cognitive Computat., 10(3), 478-495. https://doi.org/10.1007/s12559-017-9542-9
  7. Alsarraf, J., Moayedi, H., Rashid, A.S.A., Muazu, M.A. and Shahsavar, A. (2019), "Application of PSO-ANN modelling for predicting the exergetic performance of a building integrated photovoltaic/thermal system", Eng. Comput, 35, 1-14. https://doi.org/10.1007/s00366-019-00721-4
  8. 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
  9. Altun, F., Kisim, O. and Aydin, K. (2008), "Predicting the compressive strength of steel fiber added lightweight concrete using neural network", Computat. Mater. Sci., 42(2), 259-265. https://doi.org/10.1016/j.commatsci.2007.07.011
  10. Atici, U. (2011), "Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network", Expert Syst. Applicat., 38(8), 9609-9618. https://doi.org/10.1016/j.eswa.2011.01.156
  11. 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
  12. 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
  13. Bui, D.T., Ghareh, S., Moayedi, H. and Nguyen, H. (2019a), "Fine-tuning of neural computing using whale optimization algorithm for predicting compressive strength of concrete", Eng. Comput., 1-12. https://doi.org/10.1007/s00366-019-00850-w
  14. Bui, D., Moayedi, H., Gor, M., Jaafari, A. and Foong, L.K. (2019b), "Predicting slope stability failure through machine learning paradigms", ISPRS Int. J. Geo-Inform., 8(9), 395. https://doi.org/10.3390/ijgi8090395
  15. Bui, D.T., Moayedi, H., Kalantar, B., Osouli, A., Pradhan, B., Nguyen, H. and Rashid, A.S.A. (2019c), "A novel swarm intelligence-Harris hawks optimization for spatial assessment of landslide susceptibility", Sensors, 19(16), 3590. https://doi.org/10.3390/s19163590
  16. Bui, X.N., Moayedi, H. and Rashid, A.S.A. (2019d), "Developing a predictive method based on optimized M5Rules-GA predicting heating load of an energy-efficient building system", Eng. Comput., 36, 931-940. https://doi.org/10.1007/s00366-019-00739-8
  17. hahnasir, E.S., Zandi, Y., Shariati, M., Dehghani, E., Toghroli, A., Mohamad, E.T., Shariati, A., Safa, M., Wakil, K. and Khorami, M. (2018), "Application of support vector machine with firefly algorithm for investigation of the factors affecting the shear strength of angle shear connectors", Smart Struct. Syst., Int. J., 22(4), 413-424. https://doi.org/10.12989/sss.2018.22.4.413
  18. Chen, W., Panahi, M., Tsangaratos, P., Shahabi, H., Ilia, I., Panahi, S., Li, S., Jaafari, A. and Ahmad, B.B. (2019), "Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility", Catena, 172, 212-231. https://doi.org/10.1016/j.catena.2018.08.025
  19. Cheng, M.Y., Prayogo, D. and Wu, Y.W. (2013), "Novel genetic algorithm-based evolutionary support vector machine for optimizing high-performance concrete mixture", J. Comput. Civil Eng., 28(4), 06014003. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000347
  20. de Almeida Neto, M.A., Fagundes, R.D.A.D.A. and Bastos-Filho, C.J. (2018), "Optimizing support vector regression with swarm intelligence for estimating the concrete compression strength", Proceedings of International Conference on Machine Learning and Data Mining in Pattern Recognition, July, pp. 126-137.
  21. Eusuff, M.M. and Lansey, K.E. (2003), "Optimization of water distribution network design using the shuffled frog leaping algorithm", J. Water Resour. Plann. Manage., 129(3), 210-225. https://doi.org/10.1061/(ASCE)0733-9496(2003)129:3(210)
  22. Fallahian, M., Khoshnoudian, F. and Talaei, S. (2018), "Application of couple sparse coding ensemble on structural damage detection", Smart Struct. Syst., Int. J., 21(1), 1-14. https://doi.org/10.12989/sss.2018.21.1.001
  23. Gao, W., Alsarraf, J., Moayedi, H., Shahsavar, A. and Nguyen, H. (2019), "Comprehensive preference learning and feature validity for designing energy-efficient residential buildings using machine learning paradigms", Appl. Soft Comput., 105748. https://doi.org/10.1016/j.asoc.2019.105748
  24. Guo, Z., Moayedi, H., Foong, L.K. and Bahiraei, M. (2020), "Optimal modification of heating, ventilation, and air conditioning system performances in residential buildings using the integration of metaheuristic optimization and neural computing", Energy Build., 214, 109866. https://doi.org/10.1016/j.enbuild.2020.109866
  25. Hecht-Nielsen, R. (1992), Neural Networks for Perception, Elsevier, pp. 65-93.
  26. Hornik, K (1991), "Approximation capabilities of multilayer feedforward networks", Neural Networks, 4(2), 251-257. https://doi.org/10.1016/0893-6080(91)90009-T
  27. Karaboga, D. (2005), "An idea based on honey bee swarm for numerical optimization", Technical report-tr06; Erciyes University, Engineering Faculty, Computer ….
  28. Karaboga, D. and Basturk, B. (2007), "Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems", Proceedings of International Fuzzy Systems Association World Congress, pp. 789-798. https://doi.org/10.1007/978-3-540-72950-1_77
  29. Karaboga, D., Akay, B. and Ozturk, C. (2007), "Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks", Proceedings of International Conference on Modeling Decisions for Artificial Intelligence, pp. 318-329. https://doi.org/10.1007/978-3-540-73729-2_30
  30. Keshavarz, Z. and Torkian, H. (2018), "Application of ANN and ANFIS models in determining compressive strength of concrete", Soft Comput. Civil Eng., 2(1), 62-70. https://doi.org/10.22115/SCCE.2018.51114
  31. Kheder, G.F., Al Gabban, A.M. and Abid, S.M. (2003), "Mathematical model for the prediction of cement compressive strength at the ages of 7 and 28 days within 24 hours", Mater. Struct., 36(10), 693. https://doi.org/10.1007/BF02479504
  32. Kisi, O. (2007), "Streamflow forecasting using different artificial neural network algorithms", J. Hydrol. Eng., 12(5), 532-539. https://doi.org/10.1061/(ASCE)1084-0699(2007)12:5(532)
  33. Liping, Z., Weiwei, W., Yi, H., Yefeng, X. and Yixian, C. (2012), "Application of shuffled frog leaping algorithm to an uncapacitated SLLS problem", AASRI Procedia, 1, 226-231. https://doi.org/10.1016/j.aasri.2012.06.035
  34. Liu, L., Moayedi, H., Rashid, A.S.A., Rahman, S.S.A. and Nguyen, H (2019), "Optimizing an ANN model with genetic algorithm (GA) predicting load-settlement behaviours of ecofriendly raft-pile foundation (ERP) system", Eng. Comput., 35, 1-13. https://doi.org/10.1007/s00366-019-00767-4
  35. Mafarja, M., Aljarah, I., Faris, H., Hammouri, A.I., Ala'M, A.Z. and Mirjalili, S. (2019), "Binary grasshopper optimisation algorithm approaches for feature selection problems", Expert Syst. Applicat., 117, 267-286. https://doi.org/10.1016/j.eswa.2018.09.015
  36. Mandal, S., Shilpa, M. and Rajeshwari, R. (2019), "Compressive Strength Prediction of High-Strength Concrete Using Regression and ANN Models", In: Sustainable Construction and Building Materials, Springer, Singapore, pp. 459-469.
  37. 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
  38. Mehrabi, M., Pradhan, B., Moayedi, H. and Alamri, A. (2020), "Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four Stateof-the-art Metaheuristic Techniques", Sensors, 20(6), 1723. https://doi.org/10.3390/s20061723
  39. Mehta, P.K. (1986), Concrete. Structure, properties and materials, National Academy of Sciences, Washington, DC, USA.
  40. Mirjalili, S., Gandomi, A.H., Mirjalili, S.Z., Saremi, S., Faris, H. and Mirjalili, S.M. (2017), "Salp Swarm Algorithm: A bioinspired optimizer for engineering design problems", Adv. Eng. Software, 114, 163-191. https://doi.org/10.1016/j.advengsoft.2017.07.002
  41. Mirjalili, S.Z., Mirjalili, S., Saremi, S., Faris, H. and Aljarah, I. (2018), "Grasshopper optimization algorithm for multi-objective optimization problems", Appl. Intel., 48(4), 805-820. https://doi.org/10.1007/s10489-017-1019-8
  42. Moayedi, H. and Hayati, S. (2018a), "Artificial intelligence design charts for predicting friction capacity of driven pile in clay", Neural Comput. Applicat., 31, 1-17. https://doi.org/10.1007/s00521-018-3555-5
  43. 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
  44. Moayedi, H. and Rezaei, A. (2017), "An artificial neural network approach for under-reamed piles subjected to uplift forces in dry sand", Neural Comput. Applicat., 31(2), 327-336. https://doi.org/10.1007/s00521-017-2990-z
  45. Moayedi, H., Mosallanezhad, M. and Nazir, R. (2017), "Evaluation of maintained load test (MLT) and pile driving analyzer (PDA) in measuring bearing capacity of driven reinforced concrete piles", Soil Mech. Found. Eng., 54(3), 150-154. https://doi.org/10.1007/s11204-017-9449-1
  46. Moayedi, H., Mehrabi, M., Mosallanezhad, M., Rashid, A.S.A. and Pradhan, B. (2018), "Modification of landslide susceptibility mapping using optimized PSO-ANN technique", Eng. Comput., 35(3), 967-984. https://doi.org/10.1007/s00366-018-0644-0
  47. Moayedi, H., Aghel, B., Vaferi, B., Foong, L.K. and Bui, D.T. (2019a), "The feasibility of Levenberg-Marquardt algorithm combined with imperialist competitive computational method predicting drag reduction in crude oil pipelines", J. Petrol. Sci. Eng., 185, 106634. https://doi.org/10.1016/j.petrol.2019.106634
  48. Moayedi, H., Tien Bui, D., Kalantar, B. and Kok Foong, L. (2019b), "Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure", Appl. Sci., 9(21), 4638. https://doi.org/10.3390/app9214638
  49. Moayedi, H., Kalantar, B., Foong, L.K., Tien Bui, D. and Motevalli, A. (2019c), "Application of three metaheuristic techniques in simulation of concrete slump", Appl. Sci., 9(20), 4340. https://doi.org/10.3390/app9204340
  50. Moayedi, H., Osouli, A., Bui, D.T., Kok Foong, L., Nguyen, H. and Kalantar, B. (2019d), "Two novel neural-evolutionary predictive techniques of dragonfly algorithm (DA) and biogeography-based optimization (BBO) for landslide susceptibility analysis", Geomat. Natural Hazards Risk, 10(1), 2429-2453. https://doi.org/10.1080/19475705.2019.1699608
  51. Moayedi, H., Tien Bui, D., Dounis, A. and Ngo, P.T.T. (2019e), "A novel application of league championship optimization (LCA): hybridizing fuzzy logic for soil compression coefficient analysis", Appl. Sci., 10(1), 67. https://doi.org/10.3390/app10010067
  52. Moayedi, H., Tien Bui, D., Gor, M., Pradhan, B. and Jaafari, A. (2019f), "The feasibility of three prediction techniques of the artificial neural network, adaptive neuro-fuzzy inference system, and hybrid particle swarm optimization for assessing the safety factor of cohesive slopes", ISPRS Int. J. Geo-Inform., 8(9), 391. https://doi.org/10.3390/ijgi8090391
  53. 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", Measurement, 152, 107389. https://doi.org/10.1016/j.measurement.2019.107389
  54. Nehdi, M., Djebbar, Y. and Khan, A. (2001), "Neural network model for preformed-foam cellular concrete", ACI Mater. J., 98(5), 402-409.
  55. Nguyen, H., Bui, X.N., Bui, H.B. and Mai, N.L. (2018), "A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine, Vietnam", Neural Comput. Applicat., 32(8), 3939-3955. https://doi.org/10.1007/s00521-018-3717-5
  56. Nguyen, H., Mehrabi, M., Kalantar, B., Moayedi, H. and Abdullahi, M.A.M. (2019a), "Potential of hybrid evolutionary approaches for assessment of geo-hazard landslide susceptibility mapping", Geomat. Natural Hazards Risk, 10(1), 1667-1693. https://doi.org/10.1080/19475705.2019.1607782
  57. Nguyen, H., Moayedi, H., Jusoh, W.A.W. and Sharifi, A. (2019b), "Proposing a novel predictive technique using M5Rules-PSO model estimating cooling load in energy-efficient building system", Eng. Comput., 35, 1-11. https://doi.org/10.1007/s00366-019-00735-y
  58. Nguyen, T.N., Yu, Y., Li, J., Gowripalan, N. and Sirivivatnanon, V. (2019c), "Elastic modulus of ASR-affected concrete An evaluation using Artificial Neural Network", Comput. Concrete, Int. J., 24(6), 541-553. https://doi.org/10.12989/cac.2019.24.6.541
  59. Park, K., Kim, S. and Torbol, M. (2016), "Operational modal analysis of reinforced concrete bridges using autoregressive model", Smart. Struct. Syst., Int. J., 17(6), 1017-1030. https://doi.org/10.12989/sss.2016.17.6.1017
  60. Prayogo, D. (2018), "Metaheuristic-based machine learning system for prediction of compressive strength based on concrete mixture properties and early-age strength test results", Civil Eng. Dimens., 20(1), 21-29. https://doi.org/10.9744/ced.20.1.21-29
  61. Qiao, W., Moayedi, H. and Foong, L.K. (2020), "Nature-inspired hybrid techniques of IWO, DA, ES, GA, and ICA, validated through a k-fold validation process predicting monthly natural gas consumption", Energy Build., 110023. https://doi.org/10.1016/j.enbuild.2020.110023
  62. 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
  63. Sadowski, L., Nikoo, M. and Nikoo, M. (2018), "Concrete compressive strength prediction using the imperialist competitive algorithm", Comput. Concrete, Int. J., 22(4), 355-363. https://doi.org/10.12989/cac.2018.22.4.355
  64. Saremi, S., Mirjalili, S. and Lewis, A. (2017), "Grasshopper optimisation algorithm: theory and application", Adv. Eng. Software, 105, 30-47. https://doi.org/10.1016/j.advengsoft.2017.01.004
  65. Sayed, G.I., Khoriba, G. and Haggag, M.H. (2018), "A novel chaotic salp swarm algorithm for global optimization and feature selection", Appl. Intel., 48(10), 3462-3481. https://doi.org/10.1007/s10489-018-1158-6
  66. Wang, B., Moayedi, H., Nguyen, H., Foong, L.K. and Rashid, A.S.A. (2019), "Feasibility of a novel predictive technique based on artificial neural network optimized with particle swarm optimization estimating pullout bearing capacity of helical piles", Eng. Comput., 36, 1-10. https://doi.org/10.1007/s00366-019-00764-7
  67. Xi, W., Li, G., Moayedi, H. and Nguyen, H. (2019), "A particlebased optimization of artificial neural network for earthquakeinduced landslide assessment in Ludian county, China", Geomat. Natural Hazards Risk, 10(1), 1750-1771. https://doi.org/10.1080/19475705.2019.1615005
  68. Yaseen, Z.M., Deo, R.C., Hilal, A., Abd, A.M., Bueno, L.C., Salcedo-Sanz, S. and Nehdi, M.L. (2018), "Predicting compressive strength of lightweight foamed concrete using extreme learning machine model", Adv. Eng. Software, 115, 112-125. https://doi.org/10.1080/19475705.2019.1615005
  69. Yeh, I.-C. (2007), "Modeling slump flow of concrete using second-order regressions and artificial neural networks", Cement Concrete Compos., 29(6), 474-480. https://doi.org/10.1016/j.cemconcomp.2007.02.001
  70. Yu, Y., Li, Y. and Li, J. (2015), "Nonparametric modeling of magnetorheological elastomer base isolator based on artificial neural network optimized by ant colony algorithm", J. Intel. Mater. Syst. Struct., 26(14), 1789-1798. https://doi.org/10.1177/1045389X15577649
  71. Yu, Y., Zhang, C., Gu, X. and Cui, Y. (2019), "Expansion prediction of alkali aggregate reactivity-affected concrete structures using a hybrid soft computing method", Neural Comput. Applicat., 31(12), 8641-8660. https://doi.org/10.1007/s00521-018-3679-7
  72. Zhang, X., Zhang, Y., Shi, Y., Zhao, L. and Zou, C. (2012), "Power control algorithm in cognitive radio system based on modified Shuffled Frog Leaping Algorithm", AEU-Int. J. Electron. Commun., 66(6), 448-454. https://doi.org/10.1016/j.aeue.2011.10.004
  73. Zhou, G., Moayedi, H., Bahiraei, M. and Lyu, Z. (2020), "Employing artificial bee colony and particle swarm techniques for optimizing a neural network in prediction of heating and cooling loads of residential buildings", J. Cleaner Product., 254, 120082. https://doi.org/10.1016/j.jclepro.2020.120082

피인용 문헌

  1. Employing TLBO and SCE for optimal prediction of the compressive strength of concrete vol.26, pp.6, 2020, https://doi.org/10.12989/sss.2020.26.6.753
  2. Frequency characteristics and sensitivity analysis of a size-dependent laminated nanoshell vol.10, pp.2, 2020, https://doi.org/10.12989/anr.2021.10.2.175
  3. Predicting the splitting tensile strength of concrete using an equilibrium optimization model vol.39, pp.1, 2020, https://doi.org/10.12989/scs.2021.39.1.081
  4. Identification and evaluation of cracks in electrostatically actuated resonant gas sensors using Harris Hawk / Nelder Mead and perturbation methods vol.28, pp.1, 2020, https://doi.org/10.12989/sss.2021.28.1.121