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Comparison of extreme learning machine and deep learning model in the estimation of the fresh properties of hybrid fiber-reinforced SCC

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Abstract

This paper studied the estimation of fresh properties of hybrid fiber-reinforced self-compacting concrete (HR-SCC) mixtures with different types and combinations of fibers by using two different prediction method named as the methodologies of extreme learning machine and long short-term memory (LSTM). For this purpose, 48 mixtures, which were designed as single, binary, ternary and quaternary fiber-reinforced SCC with macro-steel fiber, two micro-steel fibers having different aspect ratio, polypropylene (PP) and polyvinylalcohol (PVA), were used. Slump flow, t50 and J-ring tests for designed mixtures were conducted to measure the fresh properties of fiber-reinforced SCC mixtures as per EFNARC. The experimental results were analyzed by Anova method. In the devised prediction model, the amounts of cement, fly ash, silica fume, blast furnace slag, limestone powder, aggregate, water, high-range water-reducer admixture (HRWA) and the fiber ratios were selected as inputs, while the slump flow, t50 and the J-ring were selected as outputs. Based on the Anova analysis’ results, the macro-steel fiber was the most important parameter for the results of slump-flow diameter and t50, while the most important parameter for the results of J-ring was fly ash. Furthermore, it was found that the use of more than 0.20% by volume of 6/0.16 micro-steel fiber positively influenced the fresh properties of SCC mixtures with hybrid fiber. On the other hand, the inclusion of steel fiber instead of synthetic fiber into SCC mixture as micro-fiber was more advantageous in terms of workability of mixtures as result of hydrophobic nature of steel fibers. This study found that extreme learning machine model estimated the slump flow, t50 and J-ring with 99.71%, 81% and 94.21% accuracy, respectively, while deep learning model found the same experimental results with 99.18%, 77.4% and 84.8% accuracy, respectively. It can be emphasized from this study that the extreme learning machine model had a better prediction ability than the deep learning model.

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References

  1. Boukendakdji O, Kenai S, Kadri EH, Rouis F (2009) Effect of slag on the rheology of fresh self-compacted concrete. Constr Build Mater 23:2593–2598. https://doi.org/10.1016/j.conbuildmat.2009.02.029

    Article  Google Scholar 

  2. Zeyad AM (2020) Effect of fibers types on fresh properties and flexural toughness of self-compacting concrete. J Mater Res Technol 9:4147–4158. https://doi.org/10.1016/j.jmrt.2020.02.042

    Article  Google Scholar 

  3. Turk K, Kina C, Oztekin E (2020) Effect of macro and micro fiber volume on the flexural performance of hybrid fiber reinforced SCC. Adv Concrete Constr 10:257–269. https://doi.org/10.12989/acc.2020.10.3.257

    Article  Google Scholar 

  4. Yoo DY, Kim SW, Park JJ (2017) Comparative flexural behavior of ultra-high-performance concrete reinforced with hybrid straight steel fibers. Constr Build Mater 132:219–229. https://doi.org/10.1016/j.conbuildmat.2016.11.104

    Article  Google Scholar 

  5. Abadel A, Abbas H, Almusallam T et al (2016) Discussion: Mechanical properties of hybrid fibre-reinforced concrete—analytical modelling and experimental behaviour. Mag Concrete Res 68:1183–1186. https://doi.org/10.1680/jmacr.16.00243

    Article  Google Scholar 

  6. Katzer J, Domski J (2012) Quality and mechanical properties of engineered steel fibres used as reinforcement for concrete. Constr Build Mater 34:243–248. https://doi.org/10.1016/j.conbuildmat.2012.02.058

    Article  Google Scholar 

  7. Ding X, Li C, Han B et al (2018) Effects of different deformed steel-fibers on preparation and fundamental properties of self-compacting SFRC. Constr Build Mater 168:471–481. https://doi.org/10.1016/j.conbuildmat.2018.02.162

    Article  Google Scholar 

  8. Khaloo A, Raisi EM, Hosseini P, Tahsiri H (2014) Mechanical performance of self-compacting concrete reinforced with steel fibers. Constr Build Mater 51:179–186. https://doi.org/10.1016/j.conbuildmat.2013.10.054

    Article  Google Scholar 

  9. Khayat KH, Roussel Y (2000) Testing and performance of fiber-reinforced, selfconsolidating concrete. Mater Struct Materiaux et Constructions 33:391–397. https://doi.org/10.1007/bf02479648

    Article  Google Scholar 

  10. Zeyad AM, Saba AM (2018) Influence of pulverized fly ash on the properties of self-compacting fiber reinforced concrete. Sci J King Faisal Univ 19:55–68

    Google Scholar 

  11. He Prof XB, Yan Prof B, Gu JY, Shen Q (2014) Combined impacts of polypropylene fibres on workability, strength and permeability of SCC. Mag Concrete Res 66:127–140. https://doi.org/10.1680/macr.13.00239

    Article  Google Scholar 

  12. Khayat KH, Kassimi F, Ghoddousi P (2014) Mixture design and testing of fiber-reinforced self-consolidating concrete. ACI Mater J 111:143–152. https://doi.org/10.14359/51686722

    Article  Google Scholar 

  13. Yakhlaf M, Safiuddin M, Soudki KA (2013) Properties of freshly mixed carbon fibre reinforced self-consolidating concrete. Constr Build Mater 46:224–231. https://doi.org/10.1016/j.conbuildmat.2013.04.017

    Article  Google Scholar 

  14. Dhonde HB, Mo YL, Hsu TTC, Vogel J (2007) Fresh and hardened properties of self-consolidating fiber-reinforced concrete. ACI Mater J 104:491–500. https://doi.org/10.14359/18905

    Article  Google Scholar 

  15. Saha P, Debnath P, Thomas P (2020) Prediction of fresh and hardened properties of self-compacting concrete using support vector regression approach. Neural Comput Appl 32:7995–8010. https://doi.org/10.1007/s00521-019-04267-w

    Article  Google Scholar 

  16. Sonebi M, Cevik A, Grünewald S, Walraven J (2016) Modelling the fresh properties of self-compacting concrete using support vector machine approach. Constr Build Mater 106:55–64. https://doi.org/10.1016/j.conbuildmat.2015.12.035

    Article  Google Scholar 

  17. Uysal M, 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 27:404–414. https://doi.org/10.1016/j.conbuildmat.2011.07.028

    Article  Google Scholar 

  18. Mashhadban H, Kutanaei SS, Sayarinejad MA (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

    Article  Google Scholar 

  19. Tavakoli HR, Omran OL, Kutanaei SS, Shiade MF (2014) Prediction of energy absorption capability in fiber reinforced self-compacting concrete containing nano-silica particles using artificial neural network. Latin Am J Solids Struct 11:966–979. https://doi.org/10.1590/S1679-78252014000600004

    Article  Google Scholar 

  20. Ghanbari A, Karihaloo BL (2009) Prediction of the plastic viscosity of self-compacting steel fibre reinforced concrete. Cem Concrete Res 39:1209–1216. https://doi.org/10.1016/j.cemconres.2009.08.018

    Article  Google Scholar 

  21. Asteris PG, Kolovos KG (2019) Self-compacting concrete strength prediction using surrogate models. Neural Comput Appl 31:409–424. https://doi.org/10.1007/s00521-017-3007-7

    Article  Google Scholar 

  22. Belalia Douma O, Boukhatem B, Ghrici M, Tagnit-Hamou A (2017) Prediction of properties of self-compacting concrete containing fly ash using artificial neural network. Neural Comput Appl 28:707–718. https://doi.org/10.1007/s00521-016-2368-7

    Article  Google Scholar 

  23. Benaicha M, Jalbaud O, Roguiez X et al (2015) Prediction of self-compacting concrete homogeneity by ultrasonic velocity. Alex Eng J 54:1181–1191. https://doi.org/10.1016/j.aej.2015.08.002

    Article  Google Scholar 

  24. Yang L, An X (2020) Estimating the workability of self-compacting concrete in different mixing conditions based on deep learning. Comput Concrete 25:433–445. https://doi.org/10.12989/cac.2020.25.5.433

    Article  Google Scholar 

  25. Ding Z, An X (2018) Deep learning approach for estimating workability of self-compacting concrete from mixing image sequences. Adv Mater Sci Eng. https://doi.org/10.1155/2018/6387930

    Article  Google Scholar 

  26. Yao G, Wei F, Yang Y, Sun Y (2019) Deep-learning-based Bughole detection for concrete surface image. Adv Civ Eng. https://doi.org/10.1155/2019/8582963

    Article  Google Scholar 

  27. Zhou S, Sheng W, Wei X et al (2019) Fast image analysis on pore structure of concrete based on deep learning. Kuei Suan Jen Hsueh Pao J Chin Ceram Soc 47:653–663. https://doi.org/10.14062/j.issn.0454-5648.2019.05.10

    Article  Google Scholar 

  28. Silva WRL da, Lucena DS de (2018) Concrete cracks detection based on deep learning image classification. In: Proceedings. p 489

  29. Jang K, Kim N, An YK (2019) Deep learning-based autonomous concrete crack evaluation through hybrid image scanning. Struct Health Monit 18:1722–1737. https://doi.org/10.1177/1475921718821719

    Article  Google Scholar 

  30. Dorafshan S, Thomas RJ, Maguire M (2018) Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete. Constr Build Mater 186:1031–1045. https://doi.org/10.1016/j.conbuildmat.2018.08.011

    Article  Google Scholar 

  31. Yokoyama S, Matsumoto T (2017) Development of an automatic detector of cracks in concrete using machine learning. Proced Eng 171:1250–1255. https://doi.org/10.1016/j.proeng.2017.01.418

    Article  Google Scholar 

  32. Kina C (2019) Yüksek performanslı karma lif takviyeli beton geliştirilmesi. Inonu University

  33. Atalay E (2020) Karma lif takviyeli kendiliğinden yerleşen betonun yüksek sıcaklığa direncinin araştırılması. İnönü Üniversitesi

  34. Donmez I (2020) Dörtlü sistemlerde mineral katkı ikameli karma lifli kendiliğinden yerleşen betonun işlenebilirlik ve mühendislik özelliklerinin incelenmesi. İnönü Üniversitesi

  35. Turk K, Oztekin E, Kina C (2019) Self-compacting concrete with blended short and long fibres: experimental investigation on the role of fibre blend proportion. Eur J Environ Civ Eng. https://doi.org/10.1080/19648189.2019.1686069

    Article  Google Scholar 

  36. Bin HG, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501. https://doi.org/10.1016/j.neucom.2005.12.126

    Article  Google Scholar 

  37. Ding S, Zhao H, Zhang Y et al (2015) Extreme learning machine: algorithm, theory and applications. Artif Intell Rev 44:103–115. https://doi.org/10.1007/s10462-013-9405-z

    Article  Google Scholar 

  38. Alcin OF, Sengur A, Ghofrani S, Ince MC (2014) GA-SELM: Greedy algorithms for sparse extreme learning machine. Meas J Int Meas Confed 55:126–132. https://doi.org/10.1016/j.measurement.2014.04.012

    Article  Google Scholar 

  39. Alçin ÖF, Şengür A, Ince MC (2015) Forward–backward pursuit based sparse extreme learning machine. J Fac Eng Archit Gazi Univ 30:111

    Google Scholar 

  40. Schmidhuber SH, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  41. Gers FA, Schraudolph NN, Schmidhuber J (2003) Learning precise timing with LSTM recurrent networks. J Mach Learn Res 3:115–143. https://doi.org/10.1162/153244303768966139

    Article  MathSciNet  MATH  Google Scholar 

  42. Sagheer A, Kotb M (2019) Time series forecasting of petroleum production using deep LSTM recurrent networks. Neurocomputing 323:203–213. https://doi.org/10.1016/j.neucom.2018.09.082

    Article  Google Scholar 

  43. Fischer T, Krauss C (2018) Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res 270:654–669. https://doi.org/10.1016/j.ejor.2017.11.054

    Article  MathSciNet  MATH  Google Scholar 

  44. Kara A (2019) Uzun-Kısa Süreli Bellek Ağı Kullanarak Global Güneş Işınımı Zaman Serileri Tahmini. Gazi Üniv Fen Bilimleri Dergisi Part C Tasarım Teknol 7:882–892. https://doi.org/10.29109/gujsc.571831

    Article  Google Scholar 

  45. EFNARC (2002) Specification and guidelines for self-compacting concrete

  46. Akcay B, Tasdemir MA (2012) Mechanical behaviour and fibre dispersion of hybrid steel fibre reinforced self-compacting concrete. Constr Build Mater 28:287–293. https://doi.org/10.1016/j.conbuildmat.2011.08.044

    Article  Google Scholar 

  47. Yu R, Spiesz P, Brouwers HJH (2014) Mix design and properties assessment of ultra-high performance fibre reinforced concrete (UHPFRC). Cem Concrete Res 56:29–39. https://doi.org/10.1016/j.cemconres.2013.11.002

    Article  Google Scholar 

  48. Oztekin E, Kina C, Turk K (2018) Effect of micro fiber content on workability of self-compacting concrete. In: 13th International congress on advances in civil engineering. pp 1–7

  49. Sahmaran M, Yurtseven A, Ozgur Yaman I (2005) Workability of hybrid fiber reinforced self-compacting concrete. Build Environ 40:1672–1677. https://doi.org/10.1016/j.buildenv.2004.12.014

    Article  Google Scholar 

  50. Yang E, Li VC (2006) A micromechanical model for fiber cement optimization and component tailoring. In: 10th International inorganic-bonded fiber composites conferences (IIBFCC). pp 1–13

  51. Elemam WE, Abdelraheem AH, Mahdy MG, Tahwia AM (2020) Optimizing fresh properties and compressive strength of self-consolidating concrete. Constr Build Mater. https://doi.org/10.1016/j.conbuildmat.2020.118781

    Article  Google Scholar 

  52. Tanyildizi H, Şahin M (2017) Taguchi optimization approach for the polypropylene fiber reinforced concrete strengthening with polymer after high temperature. Struct Multidiscip Optim 55:529–534. https://doi.org/10.1007/s00158-016-1517-z

    Article  Google Scholar 

  53. Mohammed MK, Al-Hadithi AI, Mohammed MH (2019) Production and optimization of eco-efficient self compacting concrete SCC with limestone and PET. Constr Build Mater 197:734–746. https://doi.org/10.1016/j.conbuildmat.2018.11.189

    Article  Google Scholar 

  54. Benghazi Z, Zeghichi L, Djellali A, Hafdallah A (2020) Predictive modeling and multi-response optimization of physical and mechanical properties of SCC based on Sand’s particle size distribution. Arab J Sci Eng 45:8503–8514. https://doi.org/10.1007/s13369-020-04774-2

    Article  Google Scholar 

  55. Tabatabaeian M, Khaloo A, Joshaghani A, Hajibandeh E (2017) Experimental investigation on effects of hybrid fibers on rheological, mechanical, and durability properties of high-strength SCC. Constr Build Mater 147:497–509. https://doi.org/10.1016/j.conbuildmat.2017.04.181

    Article  Google Scholar 

  56. Safari J, Mirzaei M, Rooholamini H, Hassani A (2018) Effect of rice husk ash and macro-synthetic fibre on the properties of self-compacting concrete. Constr Build Mater 175:371–380. https://doi.org/10.1016/j.conbuildmat.2018.04.207

    Article  Google Scholar 

  57. Gupta N, Siddique R (2020) Durability characteristics of self-compacting concrete made with copper slag. Constr Build Mater. https://doi.org/10.1016/j.conbuildmat.2020.118580

    Article  Google Scholar 

  58. Atis CD, Tanyildizi H, Karahan O (2009) Statistical analysis for strength properties of polypropylene-fibre-reinforced fly ash concrete. Mag Concrete Res 61:557–566. https://doi.org/10.1680/macr.2007.00033

    Article  Google Scholar 

  59. Tanyıldızı H (2018) Long-term microstructure and mechanical properties of polymer–phosphazene concrete exposed to freeze–thaw. Constr Build Mater 187:1121–1129. https://doi.org/10.1016/j.conbuildmat.2018.08.068

    Article  Google Scholar 

  60. Schankoski RA, de Matos PR, Pilar R et al (2020) Rheological properties and surface finish quality of eco-friendly self-compacting concretes containing quarry waste powders. J Clean Prod. https://doi.org/10.1016/j.jclepro.2020.120508

    Article  Google Scholar 

  61. Revilla-Cuesta V, Ortega-López V, Skaf M, Manso JM (2020) Effect of fine recycled concrete aggregate on the mechanical behavior of self-compacting concrete. Constr Build Mater. https://doi.org/10.1016/j.conbuildmat.2020.120671

    Article  Google Scholar 

  62. Tanyıldızı H, Asiltürk E (2018) High temperature resistance of polymer–phosphazene concrete for 365 days. Constr Build Mater 174:741–748. https://doi.org/10.1016/j.conbuildmat.2018.04.078

    Article  Google Scholar 

  63. Tanyildizi H, Coşkun A, Somunkiran I (2008) An experimental investigation of bond and compressive strength of concrete with mineral admixtures at high temperatures. Arab J Sci Eng 33:443–449

    Google Scholar 

  64. Haido JH, Tayeh BA, Majeed SS, Karpuzcu M (2020) Effect of high temperature on the mechanical properties of basalt fibre self-compacting concrete as an overlay material. Constr Build Mater. https://doi.org/10.1016/j.conbuildmat.2020.121725

    Article  Google Scholar 

  65. Sua-iam G, Makul N (2015) Rheological and mechanical properties of cement-fly ash self-consolidating concrete incorporating high volumes of alumina-based material as fine aggregate. Constr Build Mater 95:736–747. https://doi.org/10.1016/j.conbuildmat.2015.07.180

    Article  Google Scholar 

  66. Khotbehsara MM, Mohseni E, Yazdi MA et al (2015) Effect of nano-CuO and fly ash on the properties of self-compacting mortar. Constr Build Mater 94:758–766. https://doi.org/10.1016/j.conbuildmat.2015.07.063

    Article  Google Scholar 

  67. Ngo NT, Le HA, Pham T (2021) Integration of support vector regression and grey wolf optimization for estimating the ultimate bearing capacity in concrete-filled steel tube columns. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05605-z

    Article  Google Scholar 

  68. Mohammed A, Burhan L, Ghafor K et al (2020) Artificial neural network (ANN), M5P-tree, and regression analyses to predict the early age compression strength of concrete modified with DBC-21 and VK-98 polymers. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05525-y

    Article  Google Scholar 

  69. Ben Seghier MEA, Ouaer H, Ghriga MA et al (2020) Hybrid soft computational approaches for modeling the maximum ultimate bond strength between the corroded steel reinforcement and surrounding concrete. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05466-6

    Article  Google Scholar 

  70. Ashrafian A, Taheri AMJHF (2019) Modeling the slump flow of self-compacting concrete incorporating Metakaolin using soft computing techniques. J Struct Constr Eng 6:5–20

    Google Scholar 

  71. Sonebi M, Grünewald S, Cevik A, Walraven J (2016) Modelling fresh properties of self-compacting concrete using neural network technique. Comput. Concrete. 18(4):903–921. https://doi.org/10.12989/cac.2016.18.4.903

    Article  Google Scholar 

  72. Naseri F, Jafari F, Mohseni E et al (2017) Experimental observations and SVM-based prediction of properties of polypropylene fibres reinforced self-compacting composites incorporating nano-CuO. Constr Build Mater 143:589–598. https://doi.org/10.1016/j.conbuildmat.2017.03.124

    Article  Google Scholar 

  73. Sathyan D, Anand KB, Prakash AJ, Premjith B (2018) Modeling the fresh and hardened stage properties of self-compacting concrete using random kitchen sink algorithm. Int J Concrete Struct Mater. https://doi.org/10.1186/s40069-018-0246-7

    Article  Google Scholar 

  74. Azimi-Pour M, Eskandari-Naddaf H, Pakzad A (2020) Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete. Constr Build Mater. https://doi.org/10.1016/j.conbuildmat.2019.117021

    Article  Google Scholar 

  75. Al Qadi ANS, Haddad M (2017) Predicting contour slump flow of self-compacting concrete using bentonite as filler. Adv Ecol Environ Res 451–460

  76. Uzun M, Uzun M (2019) Prediction of bending strength of self-leveling glass fiber reinforced concrete. Int J Intell Syst Appl Eng 7:7–12. https://doi.org/10.18201/ijisae.2019751246

    Article  Google Scholar 

  77. Gencel O, Ozel C, Koksal F et al (2013) Fuzzy logic model for prediction of properties of fiber reinforced self-compacting concrete. Medziagotyra 19:203–215. https://doi.org/10.5755/j01.ms.19.2.4439

    Article  Google Scholar 

  78. Niaraki RJFR (2017) Prediction of mechanical and fresh properties of self-consolidating concrete (SCC) using multi-objective genetic algorithm (MOGA). J Struct Eng Geo-Tech 7:1–13

    Google Scholar 

  79. Tanyildizi H, Şengür A, Akbulut Y, Şahin M (2020) Deep learning model for estimating the mechanical properties of concrete containing silica fume exposed to high temperatures. Front Struct Civ Eng. https://doi.org/10.1007/s11709-020-0646-z

    Article  Google Scholar 

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Acknowledgements

The financial support for the experimental part of this study was provided by Scientific Research Projects Committee of Inonu University, Turkey (Project no: FDK-2017-865, FYL-2017-844, FYL-2017-889). Their support was gratefully acknowledged.

Funding

The financial support for the experimental part of this study was funded by Scientific Research Projects Committee of Inonu University, Turkey (Project no: FDK-2017-865, FYL-2017-844, FYL-2017-889).

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Authors and Affiliations

  1. Department of Civil Engineering, Malatya Turgut Ozal University, Faculty of Engineering and Natural Sciences, Malatya, Turkey

    Ceren Kina

  2. Department of Civil Engineering, Inonu University, Engineering Faculty, Malatya, Turkey

    Kazim Turk, Esma Atalay & Izzeddin Donmez

  3. Department of Civil Engineering, Firat University, Technology Faculty, Elazig, Turkey

    Harun Tanyildizi

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  1. Ceren Kina
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All authors contributed to the study conception and design. CK, KT, EA, ID and HT performed material preparation, data collection and analysis. All authors read and approved the final manuscript.

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Correspondence to Ceren Kina.

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Kina, C., Turk, K., Atalay, E. et al. Comparison of extreme learning machine and deep learning model in the estimation of the fresh properties of hybrid fiber-reinforced SCC. Neural Comput & Applic 33, 11641–11659 (2021). https://doi.org/10.1007/s00521-021-05836-8

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