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Data-Driven Modeling of Mechanical Properties of Fiber-Reinforced Concrete: A Critical Review

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Abstract

Fiber-reinforced concrete (FRC) is extensively used in diverse structural engineering applications, and its mechanical properties are crucial for designing and evaluating its performance. The compressive, flexural, splitting tensile, and shear strengths of FRCs are among the most important attributes, which have been discussed more extensively than other properties. The accurate prediction of these properties, which are required for design criteria, has been a challenge because of their high uncertainties. Statistical and empirical models have been extensively utilized. However, such models require extensive experimental work and can produce incorrect outcomes when there are complicated interactions among the qualities of concrete, the makeup of the blend, and the curing environment. To address this issue, machine learning (ML) methods have been increasingly applied in recent years to solve complex structural engineering problems. Predictive models can provide a strong solution for time-consuming numerical simulations and expensive experiments. This study explores the ML techniques applied in this context and provides a comprehensive analysis of artificial intelligence methods used for predicting the mechanical properties of FRCs. It also highlights the key observations, challenges, and future trends in this field. This study serves as a valuable resource for researchers in selecting accurate models that match their applications. It also encourages material engineers to become familiar with and employ ML methods to design FRC mixtures appropriately.

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References

  1. Banjara NK, Ramanjaneyulu K (2018) Experimental investigations and numerical simulations on the flexural fatigue behavior of plain and fiber-reinforced concrete. J Mater Civ Eng 30(8):04018151

    Google Scholar 

  2. Mohod MV (2012) Performance of steel fiber reinforced concrete. Int J Eng Sci 1(12):1–4

    Google Scholar 

  3. Murthy YI, Sharda A, Jain G (2012) Performance of glass fiber reinforced concrete. Int J Eng Innov Technol 1(6)

  4. Galao O, Bañón L, Baeza FJ, Carmona J, Garcés P (2016) Highly conductive carbon fiber reinforced concrete for icing prevention and curing. Materials 9(4):281

    Google Scholar 

  5. Kakooei S, Akil HM, Jamshidi M, Rouhi J (2012) The effects of polypropylene fibers on the properties of reinforced concrete structures. Constr Build Mater 27(1):73–77

    Google Scholar 

  6. Daneshfar M, Hassani A, Aliha M, Berto F (2017) Evaluating mechanical properties of macro-synthetic fiber-reinforced concrete with various types and contents. Strength Mater 49:618–626

    Google Scholar 

  7. Khan MI, Umair M, Shaker K, Basit A, Nawab Y, Kashif M (2020) Impact of waste fibers on the mechanical performance of concrete composites. J Textile Inst 111(11):1632–1640

    Google Scholar 

  8. Farooq MA, Fahad M, Ali B, El Ouni MH, Elhag AB et al (2022) Influence of nylon fibers recycled from the scrap brushes on the properties of concrete: valorization of plastic waste in concrete. Case Stud Constr Mater 16:e01089

    Google Scholar 

  9. Zhou H, Jia B, Huang H, Mou Y (2020) Experimental study on basic mechanical properties of basalt fiber reinforced concrete. Materials 13(6):1362

    Google Scholar 

  10. Shafighfard T, Demir E, Yildiz M (2019) Design of fiber-reinforced variable-stiffness composites for different open-hole geometries with fiber continuity and curvature constraints. Compos Struct 226:111280

    Google Scholar 

  11. Shafighfard T, Cender TA, Demir E (2021) Additive manufacturing of compliance optimized variable stiffness composites through short fiber alignment along curvilinear paths. Addit Manuf 37:101728

    Google Scholar 

  12. Shafighfard T, Mieloszyk M (2022) Experimental and numerical study of the additively manufactured carbon fibre reinforced polymers including fibre Bragg grating sensors. Compos Struct 299:116027

    Google Scholar 

  13. Jiang C, Fan K, Wu F, Chen D (2014) Experimental study on the mechanical properties and microstructure of chopped basalt fibre reinforced concrete. Mater Des 58:187–193

    Google Scholar 

  14. Pešić N, Živanović S, Garcia R, Papastergiou P (2016) Mechanical properties of concrete reinforced with recycled HDPE plastic fibres. Constr Build Mater 115:362–370

    Google Scholar 

  15. Abirami T, Loganaganandan M, Murali G, Fediuk R, Sreekrishna RV, Vignesh T, Januppriya G, Karthikeyan K (2019) Experimental research on impact response of novel steel fibrous concretes under falling mass impact. Constr Build Mater 222:447–457

    Google Scholar 

  16. Coughlin AM, Musselman E, Schokker AJ, Linzell D (2010) Behavior of portable fiber reinforced concrete vehicle barriers subject to blasts from contact charges. Int J Impact Eng 37(5):521–529

    Google Scholar 

  17. Kos Ž, Kroviakov S, Kryzhanovskyi V, Hedulian D (2022) Strength, frost resistance, and resistance to acid attacks on fiber-reinforced concrete for industrial floors and road pavements with steel and polypropylene fibers. Materials 15(23):8339

    Google Scholar 

  18. Slater E, Moni M, Alam MS (2012) Predicting the shear strength of steel fiber reinforced concrete beams. Constr Build Mater 26(1):423–436

    Google Scholar 

  19. Zhao J, Dun H (2014) A restoring force model for steel fiber reinforced concrete shear walls. Eng Struct 75:469–476

    Google Scholar 

  20. Khan M, Ali M (2016) Use of glass and nylon fibers in concrete for controlling early age micro cracking in bridge decks. Constr Build Mater 125:800–808

    Google Scholar 

  21. Zhao J, Meng X, Chen L, Liu G, Zhang Z, Xu Q (2021) Correlation between the mechanical properties and the fiber breaking morphology of fiber reinforced shotcrete (FRS). Compos Struct 277:114641

    Google Scholar 

  22. Blazy J, Blazy R (2021) Polypropylene fiber reinforced concrete and its application in creating architectural forms of public spaces. Case Stud Constr Mater 14:e00549

    Google Scholar 

  23. Yang J-M, Shin H-O, Yoo D-Y (2017) Benefits of using amorphous metallic fibers in concrete pavement for long-term performance. Arch Civ Mech Eng 17(4):750–760

    Google Scholar 

  24. Pranav S, Aggarwal S, Yang E-H, Sarkar AK, Singh AP, Lahoti M (2020) Alternative materials for wearing course of concrete pavements: a critical review. Constr Build Mater 236:117609

    Google Scholar 

  25. Grammatikos SA, Jones RG, Evernden M, Correia JR (2016) Thermal cycling effects on the durability of a pultruded GFRP material for off-shore civil engineering structures. Compos Struct 153:297–310

    Google Scholar 

  26. Zhang Z, Zhang H, Zhu K, Tang Z, Zhang H (2023) Deterioration mechanism on micro-structure of unsaturated polyester resin modified concrete for bridge deck pavement under salty freeze–thaw cycles. Constr Build Mater 368:130366

    Google Scholar 

  27. Zeng G, Geng P, Guo X, Li P, Wang Q, Ding T (2021) An anti-fault study of basalt fiber reinforced concrete in tunnels crossing a stick-slip fault. Soil Dyn Earthq Eng 148:106687

    Google Scholar 

  28. Avanaki MJ, Hoseini A, Vahdani S, de Santos C, de la Fuente A (2018) Seismic fragility curves for vulnerability assessment of steel fiber reinforced concrete segmental tunnel linings. Tunn Undergr Space Technol 78:259–274

    Google Scholar 

  29. Mirzahosseini H, Mirhosseini SM, Zeighami E (2023) Progressive collapse assessment of reinforced concrete (RC) buildings with high-performance fiber-reinforced cementitious composites (HPFRCC). Structures 49:139–151

    Google Scholar 

  30. Young BA, Hall A, Pilon L, Gupta P, Sant G (2019) Can the compressive strength of concrete be estimated from knowledge of the mixture proportions? New insights from statistical analysis and machine learning methods. Cem Concr Res 115:379–388

    Google Scholar 

  31. Duan J, Asteris PG, Nguyen H, Bui X-N, Moayedi H (2021) A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model. Eng Comput 37:3329–3346

    Google Scholar 

  32. Chou J-S, Chen L-Y, Liu C-Y (2023) Forensic-based investigation-optimized extreme gradient boosting system for predicting compressive strength of ready-mixed concrete. J Comput Des Eng 10(1):425–445

    Google Scholar 

  33. Ahmed HU, Mostafa RR, Mohammed A, Sihag P, Qadir A (2023) Support vector regression (SVR) and grey wolf optimization (GWO) to predict the compressive strength of GGBFS-based geopolymer concrete. Neural Comput Appl 35(3):2909–2926

    Google Scholar 

  34. Piro NS, Mohammed AS, Hamad SM (2023) Evaluate and predict the resist electric current and compressive strength of concrete modified with GGBS and steelmaking slag using mathematical models. J Sustain Metall 9(1):194–215

    Google Scholar 

  35. Kang M-C, Yoo D-Y, Gupta R (2021) Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete. Constr Build Mater 266:121117

    Google Scholar 

  36. Moein MM, Saradar A, Rahmati K, Mousavinejad SHG, Bristow J, Aramali V, Karakouzian M (2022) Predictive models for concrete properties using machine learning and deep learning approaches: a review. J Build Eng 63:105444

    Google Scholar 

  37. Chaabene WB, Flah M, Nehdi ML (2020) Machine learning prediction of mechanical properties of concrete: critical review. Constr Build Mater 260:119889

    Google Scholar 

  38. Ramezanianpour AA, Kazemian M, Sedighi S, Bahmanzadeh F, Amiri R, Ramezanianpour AM (2019) Study durability of mortars with natural pozzolans under carbonation. N Approaches Civ Eng 3(2):63–75

    Google Scholar 

  39. Ramezanianpour AA, Sedighi S, Kazemian M, Ramezanianpour AM (2020) Effect of micro silica and slag on the durability properties of mortars against accelerated carbonation and chloride ions attack. AUT J Civ Eng 4(4):411–422

    Google Scholar 

  40. Shi W, Najimi M, Shafei B (2020) Chloride penetration in shrinkage-compensating cement concretes. Cem Concr Compos 113:103656

    Google Scholar 

  41. Shi W, Najimi M, Shafei B (2020) Reinforcement corrosion and transport of water and chloride ions in shrinkage-compensating cement concretes. Cem Concr Res 135:106121

    Google Scholar 

  42. Mohammadhosseini H, Alyousef R, Hasanah N, Lim AS, Tahir MM, Alabduljabbar H, Mohamed AM (2020) Creep and drying shrinkage performance of concrete composite comprising waste polypropylene carpet fibres and palm oil fuel ash. J Build Eng 30:101250

    Google Scholar 

  43. Liew K, Akbar A (2020) The recent progress of recycled steel fiber reinforced concrete. Constr Build Mater 232:117232

    Google Scholar 

  44. Raza A, El Ouni MH, Ali L, Awais M, Ali B, Ahmad Z, Kahla NB (2022) Structural evaluation of recycled aggregate concrete circular columns having FRP rebars and synthetic fibers. Eng Struct 250:113392

    Google Scholar 

  45. Soufeiani L, Raman SN, Jumaat MZB, Alengaram UJ, Ghadyani G, Mendis P (2016) Influences of the volume fraction and shape of steel fibers on fiber-reinforced concrete subjected to dynamic loading—a review. Eng Struct 124:405–417

    Google Scholar 

  46. Wu Z, Shi C, He W, Wu L (2016) Effects of steel fiber content and shape on mechanical properties of ultra high performance concrete. Constr Build Mater 103:8–14

    Google Scholar 

  47. Abdallah S, Fan M, Rees DW (2018) Bonding mechanisms and strength of steel fiber-reinforced cementitious composites: overview. J Mater Civ Eng 30(3):04018001

    Google Scholar 

  48. Kim J-J, Yoo D-Y (2019) Effects of fiber shape and distance on the pullout behavior of steel fibers embedded in ultra-high-performance concrete. Cem Concr Compos 103:213–223

    Google Scholar 

  49. Wu Z, Shi C, Khayat KH (2019) Investigation of mechanical properties and shrinkage of ultra-high performance concrete: influence of steel fiber content and shape. Composites B 174:107021

    Google Scholar 

  50. Rezakhani R, Scott DA, Bousikhane F, Pathirage M, Moser RD, Green BH, Cusatis G (2021) Influence of steel fiber size, shape, and strength on the quasi-static properties of ultra-high performance concrete: experimental investigation and numerical modeling. Constr Build Mater 296:123532

    Google Scholar 

  51. Zhang K, Yuan Q, Huang T, Zuo S, Yao H (2023) Utilization of novel stranded steel fiber to enhance fiber–matrix interface of cementitious composites. Constr Build Mater 369:130525

    Google Scholar 

  52. González DC, Mena Á, Ruiz G, Ortega JJ, Poveda E, Mínguez J, Yu R, De La Rosa Á, Vicente MÁ (2023) Size effect of steel fiber-reinforced concrete cylinders under compressive fatigue loading: influence of the mesostructure. Int J Fatigue 167:107353

    Google Scholar 

  53. ACI (2017) ACI 544.2 R-17, Report on the measurement of fresh state properties and fiber dispersion of fiber-reinforced concrete, American Concrete Institute Farmington Hills.

  54. Nguyen T-T, Thai H-T, Ngo T (2023) Effect of steel fibers on the performance of an economical ultra-high strength concrete. Struct Concr 24(2):2327–2341

    Google Scholar 

  55. Zhang X, He F, Chen J, Yang C, Xu F (2023) Orientation of steel fibers in concrete attracted by magnetized rebar and its effects on bond behavior. Cem Concr Compos 138:104977

    Google Scholar 

  56. Sarraz A, Nakamura H, Miura T (2023) Mesoscale modelling of SFRC based on 3D RBSM considering the effects of fiber shape and orientation. Cem Concr Compos 139:105039

    Google Scholar 

  57. Jiang B, Wu S (2023) Resistance measurement for monitoring bending cracks in steel fiber concrete beams test. Alex Eng J 66:691–699

    Google Scholar 

  58. Zhu H, Li C, Gao D, Yang L, Cheng S (2019) Study on mechanical properties and strength relation between cube and cylinder specimens of steel fiber reinforced concrete. Adv Mech Eng 11(4):1687814019842423

    Google Scholar 

  59. Parashar K, Gupta A (2021) Investigation of the effect of bagasse ash, hooked steel fibers and glass fibers on the mechanical properties of concrete. Mater Today Proc 44:801–807

    Google Scholar 

  60. Lee Y, Kang S-T, Kim J-K (2010) Pullout behavior of inclined steel fiber in an ultra-high strength cementitious matrix. Constr Build Mater 24(10):2030–2041

    Google Scholar 

  61. Wille K, Naaman AE (2012) Pullout behavior of high-strength steel fibers embedded in ultra-high-performance concrete. ACI Mater J 109(4):479–488

    Google Scholar 

  62. Xu M, Hallinan B, Wille K (2016) Effect of loading rates on pullout behavior of high strength steel fibers embedded in ultra-high performance concrete. Cem Concr Compos 70:98–109

    Google Scholar 

  63. Naaman E, Reinhardt H-W (2006) Proposed classification of HPFRC composites based on their tensile response. Mater Struct 39:547–555

    Google Scholar 

  64. Pajkak M, Ponikiewski T (2013) Flexural behavior of self-compacting concrete reinforced with different types of steel fibers. Constr Build Mater 47:397–408

    Google Scholar 

  65. Teng T-L, Chu Y-A, Chang F-A, Chin H-S (2004) Calculating the elastic moduli of steel-fiber reinforced concrete using a dedicated empirical formula. Comput Mater Sci 31(3–4):337–346

    Google Scholar 

  66. Yang L, Lin X, Gravina RJ (2018) Evaluation of dynamic increase factor models for steel fibre reinforced concrete. Constr Build Mater 190:632–644

    Google Scholar 

  67. Zhao C, Wang Z, Zhu Z, Guo Q, Wu X, Zhao R (2023) Research on different types of fiber reinforced concrete in recent years: an overview. Constr Build Mater 365:130075

    Google Scholar 

  68. Guler S, Akbulut ZF (2022) Residual strength and toughness properties of 3D, 4D and 5D steel fiber-reinforced concrete exposed to high temperatures. Constr Build Mater 327:126945

    Google Scholar 

  69. Guler S, Akbulut ZF (2022) Effect of freeze–thaw cycles on strength and toughness properties of new generation 3D/4D/5D steel fiber-reinforced concrete. J Build Eng 51:104239

    Google Scholar 

  70. Gao D, Ding C, Pang Y, Chen G (2021) An inverse analysis method for multi-linear tensile stress–crack opening relationship of 3D/4D/5D steel fiber reinforced concrete. Constr Build Mater 309:125074

    Google Scholar 

  71. Gao D, Ding C, Pang Y, Yang L, Huang Y, Tang J (2021) Diverse angle–length–width model for 3D/4D/5D steel fiber reinforced concrete under tension. Constr Build Mater 266:121149

    Google Scholar 

  72. Ding C, Gao D, Guo A (2022) Analytical methods for stress–crack width relationship and residual flexural strengths of 3D/4D/5D steel fiber reinforced concrete. Constr Build Mater 346:128438

    Google Scholar 

  73. Nematollahi B, Sanjayan J, Chai JXH, Lu TM (2014) Properties of fresh and hardened glass fiber reinforced fly ash based geopolymer concrete. Key Eng Mater 594:629–633

    Google Scholar 

  74. Vignesh P, Krishnaraja A, Nandhini N (2014) Study on mechanical properties of geo polymer concrete using m-sand and glass fibers. Int J Innov Res Sci Eng Technol 3(2):110

    Google Scholar 

  75. Ruben N, Venkatesh C, Durga CSS, Chand MSR (2021) Comprehensive study on performance of glass fibers-based concrete. Innov Infrastruct Solut 6(2):112

    Google Scholar 

  76. Hilles MM, Ziara MM (2019) Mechanical behavior of high strength concrete reinforced with glass fiber. Eng Sci Technol Int J 22(3):920–928

    Google Scholar 

  77. Ahmad J, Zaid O, Aslam F, Shahzaib M, Ullah R, Alabduljabbar H, Khedher KM (2021) A study on the mechanical characteristics of glass and nylon fiber reinforced peach shell lightweight concrete. Materials 14(16):4488

    Google Scholar 

  78. Hsu LS, Hsu CT (1994) Stress–strain behavior of steel-fiber high-strength concrete under compression. Struct J 91(4):448–457

    MathSciNet  Google Scholar 

  79. Thomas J, Ramaswamy A (2007) Mechanical properties of steel fiber-reinforced concrete. J Mater Civ Eng 19(5):385–392

    Google Scholar 

  80. Sivakumar A, Santhanam M (2007) Mechanical properties of high strength concrete reinforced with metallic and non-metallic fibres. Cem Concr Compos 29(8):603–608

    Google Scholar 

  81. Mohammed H, Sherwani AFH, Faraj RH, Qadir HH, Younis KH (2021) Mechanical properties and ductility behavior of ultra-high performance fiber reinforced concretes: effect of low water-to-binder ratios and micro glass fibers. Ain Shams Eng J 12(2):1557–1567

    Google Scholar 

  82. Fu S-Y, Lauke B, Mäder E, Yue C-Y, Hu X (2000) Tensile properties of short-glass-fiber-and short-carbon-fiber-reinforced polypropylene composites. Composites A 31(10):1117–1125

    Google Scholar 

  83. Chandramouli K, Srinivasa R, Pannirselvam N, Seshadri S, Sravana P (2010) Strength properties of glass fiber concrete. ARPN J Eng Appl Sci 5(4):1–6

    Google Scholar 

  84. Moghadam MA, Izadifard RA (2020) Effects of steel and glass fibers on mechanical and durability properties of concrete exposed to high temperatures. Fire Saf J 113:102978

    Google Scholar 

  85. Zaid O, Ahmad J, Siddique MS, Aslam F, Alabduljabbar H, Khedher KM (2021) A step towards sustainable glass fiber reinforced concrete utilizing silica fume and waste coconut shell aggregate. Sci Rep 11(1):1–14

    Google Scholar 

  86. Dehghan A, Peterson K, Shvarzman A (2017) Recycled glass fiber reinforced polymer additions to Portland cement concrete. Constr Build Mater 146:238–250

    Google Scholar 

  87. Ramezani A, Modaresi S, Dashti P, GivKashi MR, Moodi F, Ramezanianpour AA (2023) Effects of different types of fibers on fresh and hardened properties of cement and geopolymer-based 3D printed mixtures: a review. Buildings 13(4):945

    Google Scholar 

  88. Abdullah MM, Jallo EK (2012) Mechanical properties of glass fiber reinforced concrete. Al-Rafidain Eng J 20(5):128–135

    Google Scholar 

  89. Yuan Z, Jia Y (2021) Mechanical properties and microstructure of glass fiber and polypropylene fiber reinforced concrete: an experimental study. Constr Build Mater 266:121048

    Google Scholar 

  90. Asokan P, Osmani M, Price AD (2010) Improvement of the mechanical properties of glass fibre reinforced plastic waste powder filled concrete. Constr Build Mater 24(4):448–460

    Google Scholar 

  91. Kizilkanat B, Kabay N, Akyüncü V, Chowdhury S, Akça AH (2015) Mechanical properties and fracture behavior of basalt and glass fiber reinforced concrete: an experimental study. Constr Build Mater 100:218–224

    Google Scholar 

  92. Palanikumar K, Ramesh M, Hemachandra Reddy K (2016) Experimental investigation on the mechanical properties of green hybrid sisal and glass fiber reinforced polymer composites. J Nat Fibers 13(3):321–331

    Google Scholar 

  93. Chen B, Liu J (2008) Damage in carbon fiber-reinforced concrete, monitored by both electrical resistance measurement and acoustic emission analysis. Constr Build Mater 22(11):2196–2201

    Google Scholar 

  94. Zhang J, Chevali VS, Wang H, Wang C-H (2020) Current status of carbon fibre and carbon fibre composites recycling. Composites B 193:108053

    Google Scholar 

  95. Pakdel E, Kashi S, Varley R, Wang X (2021) Recent progress in recycling carbon fibre reinforced composites and dry carbon fibre wastes. Resour Conserv Recycl 166:105340

    Google Scholar 

  96. Wang Z, Ma G, Ma Z, Zhang Y (2021) Flexural behavior of carbon fiber-reinforced concrete beams under impact loading. Cem Concr Compos 118:103910

    Google Scholar 

  97. Safiuddin M, Abdel-Sayed G, Hearn N (2021) Absorption and strength properties of short carbon fiber reinforced mortar composite. Buildings 11(7):300

    Google Scholar 

  98. Dopko M, Najimi M, Shafei B, Wang X, Taylor P, Phares B (2020) Strength and crack resistance of carbon microfiber reinforced concrete. ACI Mater J 117(2):11–23

    Google Scholar 

  99. Abdellatef M, Heras Murcia D, Hogancamp J, Matteo E, Stormont J, Taha MMR (2022) The significance of multi-size carbon fibers on the mechanical and fracture characteristics of fiber reinforced cement composites. Fibers 10(8):65

    Google Scholar 

  100. Askari SM, Khaloo A, Borhani MH, Masoule MST (2020) Performance of polypropylene fiber reinforced concrete-filled UPVC tube columns under axial compression. Constr Build Mater 231:117049

    Google Scholar 

  101. John VJ, Dharmar B (2021) Influence of basalt fibers on the mechanical behavior of concrete—a review. Struct Concr 22(1):491–502

    Google Scholar 

  102. Khan A-U-R, Aziz T, Fareed S, Xiao J (2020) Behaviour and residual strength prediction of recycled aggregates concrete exposed to elevated temperatures. Arab J Sci Eng 45:8241–8253

    Google Scholar 

  103. Khan M, Cao M, Xie C, Ali M (2022) Effectiveness of hybrid steel–basalt fiber reinforced concrete under compression. Case Stud Constr Mater 16:e00941

    Google Scholar 

  104. Wang X, Peng Z, Wu Z, Sun S (2019) High-performance composite bridge deck with prestressed basalt fiber-reinforced polymer shell and concrete. Eng Struct 201:109852

    Google Scholar 

  105. Aliha M, Reza Karimi H, Abedi M (2022) The role of mix design and short glass fiber content on mode-I cracking characteristics of polymer concrete. Constr Build Mater 317:126139

    Google Scholar 

  106. Ramesh B, Eswari S (2021) Mechanical behaviour of basalt fibre reinforced concrete: an experimental study. Mater Today Proc 43:2317–2322

    Google Scholar 

  107. Khanfour M-A, El Refai A (2017) Effect of freeze–thaw cycles on concrete reinforced with basalt-fiber reinforced polymers (BFRP) bars. Constr Build Mater 145:135–146

    Google Scholar 

  108. Zhang C, Wang Y, Zhang X, Ding Y, Xu P (2021) Mechanical properties and microstructure of basalt fiber-reinforced recycled concrete. J Clean Prod 278:123252

    Google Scholar 

  109. Yasir M, Amir N, Ahmad F, Ullah S, Jimenez M (2018) Effect of basalt fibers dispersion on steel fire protection performance of epoxy-based intumescent coatings. Prog Org Coat 122:229–238

    Google Scholar 

  110. Zheng Y, Zhang Y, Zhuo J, Zhang Y, Wan C (2022) A review of the mechanical properties and durability of basalt fiber-reinforced concrete. Constr Build Mater 359:129360

    Google Scholar 

  111. Mazur K, Jakubowska P, Romańska P, Kuciel S (2020) Green high density polyethylene (HDPE) reinforced with basalt fiber and agricultural fillers for technical applications. Composites B 202:108399

    Google Scholar 

  112. Shi X-H, Li X-L, Li Y-M, Li Z, Wang D-Y (2022) Flame-retardant strategy and mechanism of fiber reinforced polymeric composite: a review. Composites B 233:109663

    Google Scholar 

  113. Fu Q, Xu W, Bu M, Guo B, Niu D (2021) Effect and action mechanism of fibers on mechanical behavior of hybrid basalt–polypropylene fiber-reinforced concrete. Structures 34:3596–3610

    Google Scholar 

  114. Li D, Niu D, Fu Q, Luo D (2020) Fractal characteristics of pore structure of hybrid basalt–polypropylene fibre-reinforced concrete. Cem Concr Compos 109:103555

    Google Scholar 

  115. Shi F, Pham TM, Hao H, Hao Y (2020) Post-cracking behaviour of basalt and macro polypropylene hybrid fibre reinforced concrete with different compressive strengths. Constr Build Mater 262:120108

    Google Scholar 

  116. Khan M, Cao M, Chu S, Ali M (2022) Properties of hybrid steel-basalt fiber reinforced concrete exposed to different surrounding conditions. Constr Build Mater 322:126340

    Google Scholar 

  117. Liu B, Zhang X, Ye J, Liu X, Deng Z (2022) Mechanical properties of hybrid fiber reinforced coral concrete. Case Stud Constr Mater 16:e00865

    Google Scholar 

  118. Zhao C, Zhu Z, Guo Q, Zhan Y, Zhao R (2023) Research on fiber reinforced concrete and its performance prediction method and mix design method. Constr Build Mater 365:130033

    Google Scholar 

  119. Prathipati ST, Rao C (2020) A study on the uniaxial behavior of hybrid graded fiber reinforced concrete with glass and steel fibers. Mater Today Proc 32:764–770

    Google Scholar 

  120. Prathipati ST, Koniki S, Rao C, Kasagani H (2021) Assessment of fiber distribution characteristics in the hybrid fiber reinforced concrete—an experimental study. Mater Today Proc 38:2541–2548

    Google Scholar 

  121. Rai B, Singh NK (2021) Statistical and experimental study to evaluate the variability and reliability of impact strength of steel–polypropylene hybrid fiber reinforced concrete. J Build Eng 44:102937

    Google Scholar 

  122. Cui K, Xu L, Li L, Chi Y (2023) Mechanical performance of steel–polypropylene hybrid fiber reinforced concrete subject to uniaxial constant-amplitude cyclic compression: fatigue behavior and unified fatigue equation. Compos Struct 311:116795

    Google Scholar 

  123. Hsie M, Tu C, Song P (2008) Mechanical properties of polypropylene hybrid fiber-reinforced concrete. Mater Sci Eng A 494(1–2):153–157

    Google Scholar 

  124. Yang X, Wu T, Liu X (2022) Stress–strain model for lightweight aggregate concrete reinforced with carbon–polypropylene hybrid fibers. Polymers 14(9):1675

    Google Scholar 

  125. Song W, Yin J (2016) Hybrid effect evaluation of steel fiber and carbon fiber on the performance of the fiber reinforced concrete. Materials 9(8):704

    Google Scholar 

  126. Yuan Z, Jia Y (2021) Mechanical properties and microstructure of glass fiber and polypropylene fiber reinforced concrete: an experimental study. Constr Build Mater 266:121048

    Google Scholar 

  127. Kazemi F, Jankowski R (2023) Machine learning-based prediction of seismic limit-state capacity of steel moment-resisting frames considering soil–structure interaction. Comput Struct 274:106886

    Google Scholar 

  128. Bagherzadeh F, Shafighfard T (2022) Ensemble machine learning approach for evaluating the material characterization of carbon nanotube-reinforced cementitious composites. Case Stud Constr Mater 17:e01537

    Google Scholar 

  129. Adibimanesh B, Polesek-Karczewska S, Bagherzadeh F, Szczuko P, Shafighfard T (2023) Energy consumption optimization in wastewater treatment plants: machine learning for monitoring incineration of sewage sludge. Sustain Energy Technol Assess 56:103040

    Google Scholar 

  130. Kazemi F, Asgarkhani N, Jankowski R (2023) Predicting seismic response of SMRFs founded on different soil types using machine learning techniques. Eng Struct 274:114953

    Google Scholar 

  131. Omidi MF, Kazemi F, Abdelgader HS, Kurpińska M (2023) Machine learning-based prediction of preplaced aggregate concrete characteristics. Eng Appl Artif Intell 123:106387

    Google Scholar 

  132. Kazemi F, Asgarkhani N, Jankowski R (2023) Machine learning-based seismic response and performance assessment of reinforced concrete buildings. Arch Civ Mech Eng 23(2):94

    Google Scholar 

  133. Kazemi F, Asgarkhani N, Jankowski R (2023) Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures. Soil Dyn Earthq Eng 166:107761

    Google Scholar 

  134. Niendorf K, Raeymaekers B (2022) Using supervised machine learning methods to predict microfiber alignment and electrical conductivity of polymer matrix composite materials fabricated with ultrasound directed self-assembly and stereolithography. Comput Mater Sci 206:111233

    Google Scholar 

  135. Bagherzadeh F, Shafighfard T, Khan RMA, Szczuko P, Mieloszyk M (2023) Prediction of maximum tensile stress in plain-weave composite laminates with interacting holes via stacked machine learning algorithms: a comparative study. Mech Syst Signal Process 195:110315

    Google Scholar 

  136. Sandeep MS, Tiprak K, Kaewunruen S, Pheinsusom P, Pansuk W (2023) Shear strength prediction of reinforced concrete beams using machine learning. Structures 47:1196–1211

    Google Scholar 

  137. Barber JA, Thompson SG (2000) Analysis of cost data in randomized trials: an application of the non-parametric bootstrap. Stat Med 19(23):3219–3236

    Google Scholar 

  138. Dwivedi AK, Mallawaarachchi I, Alvarado LA (2017) Analysis of small sample size studies using nonparametric bootstrap test with pooled resampling method. Stat Med 36(14):2187–2205

    MathSciNet  Google Scholar 

  139. Efron B, Gong G (1983) A leisurely look at the bootstrap, the jackknife, and cross-validation. Am Stat 37(1):36–48

    MathSciNet  Google Scholar 

  140. Rodriguez-Galiano VF, Luque-Espinar JA, Chica-Olmo M, Mendes MP (2018) Feature selection approaches for predictive modelling of groundwater nitrate pollution: an evaluation of filters, embedded and wrapper methods. Sci Total Environ 624:661–672

    Google Scholar 

  141. Krogh A (2008) What are artificial neural networks? Nat Biotechnol 26(2):195–197

    Google Scholar 

  142. Jain K, Mao J, Mohiuddin KM (1996) Artificial neural networks: a tutorial. Computer 29(3):31–44

    Google Scholar 

  143. McClelland JL, Rumelhart DE, Group PR et al (1987) Parallel distributed processing: explorations in the microstructure of cognition: psychological and biological models, vol 2. MIT Press, Cambridge

  144. Taud H, Mas JF (2018) Multilayer perceptron (MLP). In: Geomatic approaches for modeling land change scenarios. Springer, Cham, pp 451–455

  145. Bagherzadeh F, Mehrani M-J, Basirifard M, Roostaei J (2021) Comparative study on total nitrogen prediction in wastewater treatment plant and effect of various feature selection methods on machine learning algorithms performance. J Water Process Eng 41:102033

    Google Scholar 

  146. Ray S, Rahman MM, Haque M, Hasan MW, Alam MM (2021) Performance evaluation of SVM and GBM in predicting compressive and splitting tensile strength of concrete prepared with ceramic waste and nylon fiber. J King Saud Univ Eng Sci 35(2):92–100

    Google Scholar 

  147. Ogutu JO, Piepho HP, Schulz-Streeck T (2011) A comparison of random forests, boosting and support vector machines for genomic selection. BMC Proc 5(3):1–5

    Google Scholar 

  148. Feng DC, Wang WJ, Mangalathu S, Taciroglu E (2021) Interpretable XGBoost-SHAP machine-learning model for shear strength prediction of squat RC walls. J Struct Eng 147(11):04021173

    Google Scholar 

  149. Liu G, Sun B (2023) Concrete compressive strength prediction using an explainable boosting machine model. Case Stud Constr Mater 18:e01845

    Google Scholar 

  150. Alabdullah AA, Iqbal M, Zahid M, Khan K, Amin MN, Jalal FE (2022) Prediction of rapid chloride penetration resistance of metakaolin based high strength concrete using light GBM and XGBoost models by incorporating SHAP analysis. Constr Build Mater 345:128296

    Google Scholar 

  151. Asgarkhani N, Kazemi F, Jankowski R (2023) Machine learning-based prediction of residual drift and seismic risk assessment of SMRFs considering soil–structure interaction. Comput Struct 283:107181

    Google Scholar 

  152. Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14:199–222

    MathSciNet  Google Scholar 

  153. Hariri-Ardebili MA, Pourkamali-Anaraki F (2018) Support vector machine based reliability analysis of concrete dams. Soil Dyn Earthq Eng 104:276–295

    Google Scholar 

  154. Saadat M, Bayat M (2022) Prediction of the unconfined compressive strength of stabilised soil by adaptive neuro fuzzy inference system (ANFIS) and non-linear regression (NLR). Geomech Geoeng 17(1):80–91

    Google Scholar 

  155. Owusu-Danquah JS, Bseiso A, Allena S, Duffy SF (2022) Artificial neural network algorithms to predict the bond strength of reinforced concrete: coupled effect of corrosion, concrete cover, and compressive strength. Constr Build Mater 350:128896

    Google Scholar 

  156. Zain MFM, Abd SM (2009) Multiple regression model for compressive strength prediction of high performance concrete. J Appl Sci 9(1):155–160

    Google Scholar 

  157. Pakzad SS, Roshan N, Ghalehnovi M (2023) Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete. Sci Rep 13(1):3646

    Google Scholar 

  158. Karahan O, Tanyildizi H, Atis CD (2008) An artificial neural network approach for prediction of long-term strength properties of steel fiber reinforced concrete containing fly ash. J Zhejiang Univ Sci A 9:1514–1523

    Google Scholar 

  159. 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(1):404–414

    Google Scholar 

  160. 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

    Google Scholar 

  161. Yang L, Qi C, Lin X, Li J, Dong X (2019) Prediction of dynamic increase factor for steel fibre reinforced concrete using a hybrid artificial intelligence model. Eng Struct 189:309–318

    Google Scholar 

  162. Huang Y, Zhang J, Ann FT, Ma G (2020) Intelligent mixture design of steel fibre reinforced concrete using a support vector regression and firefly algorithm based multi-objective optimization model. Constr Build Mater 260:120457

    Google Scholar 

  163. Li H, Lin J, Zhao D, Shi G, Wu H, Wei T, Li D, Zhang J (2022) A BFRC compressive strength prediction method via kernel extreme learning machine-genetic algorithm. Constr Build Mater 344:128076

    Google Scholar 

  164. Nguyen TT, Pham Duy H, Pham Thanh T, Vu HH (2020) Compressive strength evaluation of fiber reinforced high-strength self-compacting concrete with artificial intelligence. Adv Civ Eng 2020:1–12

    Google Scholar 

  165. Karimipour A, Jahangir H, Eidgahee DR (2021) A thorough study on the effect of red mud, granite, limestone and marble slurry powder on the strengths of steel fibres-reinforced self-consolidation concrete: experimental and numerical prediction. J Build Eng 44:103398

    Google Scholar 

  166. Chen H, Yang J, Chen X (2021) A convolution-based deep learning approach for estimating compressive strength of fiber reinforced concrete at elevated temperatures. Constr Build Mater 313:125437

    Google Scholar 

  167. Li S, Liew JR (2022) Experimental and data-driven analysis on compressive strength of steel fibre reinforced high strength concrete and mortar at elevated temperature. Constr Build Mater 341:127845

    Google Scholar 

  168. Gehlot T, Dave M, Solanki D (2022) Neural network model to predict compressive strength of steel fiber reinforced concrete elements incorporating supplementary cementitious materials. Mater Today Proc 62:6498–6506

    Google Scholar 

  169. Li H, Lin J, Lei X, Wei T (2022) Compressive strength prediction of basalt fiber reinforced concrete via random forest algorithm. Mater Today Commun 30:103117

    Google Scholar 

  170. Al-Hashem MN, Amin MN, Ahmad W, Khan K, Ahmad A, Ehsan S, Al-Ahmad QM, Qadir MG (2022) Data-driven techniques for evaluating the mechanical strength and raw material effects of steel fiber-reinforced concrete. Materials 15(19):6928

    Google Scholar 

  171. Kina C, Turk K, Tanyildizi H (2022) Estimation of strengths of hybrid FR-SCC by using deep-learning and support vector regression models. Struct Concr 23(5):3313–3330

    Google Scholar 

  172. Shafighfard T, Bagherzadeh F, Rizi RA, Yoo D-Y (2022) Data-driven compressive strength prediction of steel fiber reinforced concrete (SFRC) subjected to elevated temperatures using stacked machine learning algorithms. J Mater Res Technol 21:3777–3794

    Google Scholar 

  173. Amin MN, Khan K, Sufian M, Al-Ahmad QM, Deifalla AF, Alsharari F (2023) Predicting parameters and sensitivity assessment of nano-silica-based fiber-reinforced concrete: a sustainable construction material. J Mater Res Technol 23:3943–3960

    Google Scholar 

  174. Mai H-VT, Nguyen MH, Trinh SH, Ly H-B (2023) Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting concrete. Front Struct Civ Eng 17:1–22

    Google Scholar 

  175. Nguyen MH, Ly H-B et al (2023) Development of machine learning methods to predict the compressive strength of fiber-reinforced self-compacting concrete and sensitivity analysis. Constr Build Mater 367:130339

    Google Scholar 

  176. Mahesh R, Sathyan D (2022) Modelling the hardened properties of steel fiber reinforced concrete using ANN. Mater Today Proc 49:2081–2089

    Google Scholar 

  177. Sivasubramanian A, Krishna SA, Nair DH, Varma K, Radhakrishnan R, Sathyan D (2022) Experimental validation of compressive strength prediction using machine learning algorithm. Mater Today Proc 64:181–187

    Google Scholar 

  178. Xie J-H, Guo Y-C, Liu L-S, Xie Z-H (2015) Compressive and flexural behaviours of a new steel-fibre reinforced recycled aggregate concrete with crumb rubber. Constr Build Mater 79:263–272

    Google Scholar 

  179. Awolusi T, Oke O, Akinkurolere O, Sojobi A, Aluko O (2019) Performance comparison of neural network training algorithms in the modeling properties of steel fiber reinforced concrete. Heliyon 5(1):e01115

    Google Scholar 

  180. Li Y, Zhang Q, Kamiński P, Deifalla AF, Sufian M, Dyczko A et al (2022) Compressive strength of steel fiber-reinforced concrete employing supervised machine learning techniques. Materials 15(12):4209

    Google Scholar 

  181. Congro M, de Alencar Monteiro VM, Brandão AL, dos Santos BF, Roehl D, de Andrade Silva F (2021) Prediction of the residual flexural strength of fiber reinforced concrete using artificial neural networks. Constr Build Mater 303:124502

    Google Scholar 

  182. Sharma N, Thakur MS, Upadhya A, Sihag P (2021) Evaluating flexural strength of concrete with steel fibre by using machine learning techniques. Compos Mater Eng 3(3):201–220

    Google Scholar 

  183. Zheng D, Wu R, Sufian M, Kahla NB, Atig M, Deifalla AF, Accouche O, Azab M (2022) Flexural strength prediction of steel fiber-reinforced concrete using artificial intelligence. Materials 15(15):5194

    Google Scholar 

  184. Anjum M, Khan K, Ahmad W, Ahmad A, Amin MN, Nafees A (2022) New Shapley additive explanations (SHAP) approach to evaluate the raw materials interactions of steel-fiber-reinforced concrete. Materials 15(18):6261

    Google Scholar 

  185. Awolusi T, Oguntayo D, Oyejobi D, Agboola B, Akinkurolere O, Babalola O (2022) Performance evaluation of discontinuous coconut and steel fibers as reinforcement in concrete using the artificial neural network approach. Cogent Eng 9(1):2105035

    Google Scholar 

  186. Huang JS, Liew JX, Liew KM (2021) Data-driven machine learning approach for exploring and assessing mechanical properties of carbon nanotube-reinforced cement composites. Compos Struct 267:113917

    Google Scholar 

  187. Behnood A, Verian KP, Gharehveran MM (2015) Evaluation of the splitting tensile strength in plain and steel fiber-reinforced concrete based on the compressive strength. Constr Build Mater 98:519–529

    Google Scholar 

  188. Sultana N, Hossain SZ, Alam MS, Islam MS, Al Abtah MA (2020) Soft computing approaches for comparative prediction of the mechanical properties of jute fiber reinforced concrete. Adv Eng Softw 149:102887

    Google Scholar 

  189. Liu F, Ding W, Qiao Y, Wang L (2020) An artificial neural network model on tensile behavior of hybrid steel–PVA fiber reinforced concrete containing fly ash and slag power. Front Struct Civ Eng 14:1299–1315

    Google Scholar 

  190. Ikumi T, Galeote E, Pujadas P, de la Fuente A, López-Carreño R (2021) Neural network-aided prediction of post-cracking tensile strength of fibre-reinforced concrete. Comput Struct 256:106640

    Google Scholar 

  191. Liu F, Xu K, Ding W, Qiao Y, Wang L (2021) Microstructural characteristics and their impact on mechanical properties of steel–PVA fiber reinforced concrete. Cem Concr Compos 123:104196

    Google Scholar 

  192. Al-Musawi AA, Alwanas AA, Salih SQ, Ali ZH, Tran MT, Yaseen ZM (2020) Shear strength of SFRCB without stirrups simulation: implementation of hybrid artificial intelligence model. Eng Comput 36:1–11

    Google Scholar 

  193. Keshtegar B, Bagheri M, Yaseen ZM (2019) Shear strength of steel fiber-unconfined reinforced concrete beam simulation: application of novel intelligent model. Compos Struct 212:230–242

    Google Scholar 

  194. Kumar S, Barai S (2010) Neural networks modeling of shear strength of SFRC corbels without stirrups. Appl Soft Comput 10(1):135–148

    Google Scholar 

  195. Sarveghadi M, Gandomi AH, Bolandi H, Alavi AH (2019) Development of prediction models for shear strength of SFRCB using a machine learning approach. Neural Comput Appl 31:2085–2094

    Google Scholar 

  196. Hoang N-D (2019) Estimating punching shear capacity of steel fibre reinforced concrete slabs using sequential piecewise multiple linear regression and artificial neural network. Measurement 137:58–70

    Google Scholar 

  197. Manca M, Karrech A, Dight P, Ciancio D (2018) Image processing and machine learning to investigate fibre distribution on fibre-reinforced shotcrete round determinate panels. Constr Build Mater 190:870–880

    Google Scholar 

  198. Tong Z, Gao J, Wang Z, Wei Y, Dou H (2019) A new method for CF morphology distribution evaluation and CFRC property prediction using cascade deep learning. Constr Build Mater 222:829–838

    Google Scholar 

  199. Tong Z, Huo J, Wang Z (2020) High-throughput design of fiber reinforced cement-based composites using deep learning. Cem Concr Compos 113:103716

    Google Scholar 

  200. Yue J, Wang Y, Beskos D (2021) Uniaxial tension damage mechanics of steel fiber reinforced concrete using acoustic emission and machine learning crack mode classification. Cem Concr Compos 123:104205

    Google Scholar 

  201. Yang Z, Gao W, Chen L, Yuan C, Chen Q, Kong Q (2022) A novel electromechanical impedance-based method for non-destructive evaluation of concrete fiber content. Constr Build Mater 351:128972

    Google Scholar 

  202. Rezvan S, Moradi MJ, Dabiri H, Daneshvar K, Karakouzian M, Farhangi V (2023) Application of machine learning to predict the mechanical characteristics of concrete containing recycled plastic-based materials. Appl Sci 13(4):2033

    Google Scholar 

  203. Kumar A, Arora HC, Kumar K, Mohammed MA, Majumdar A, Khamaksorn A, Thinnukool O (2022) Prediction of FRCM–concrete bond strength with machine learning approach. Sustainability 14(2):845

    Google Scholar 

  204. Hemmatian A, Jalali M, Naderpour H, Nehdi ML (2023) Machine learning prediction of fiber pull-out and bond-slip in fiber-reinforced cementitious composites. J Build Eng 63:105474

    Google Scholar 

  205. Uddin MN, Mahamoudou F, Deng B-Y, Musa MME, Sob LWT (2023) Prediction of rheological parameters of 3D printed polypropylene fiber-reinforced concrete (3DP-PPRC) by machine learning. Mater Today Proc. https://doi.org/10.1016/j.matpr.2023.03.191

    Article  Google Scholar 

  206. Khokhar SA, Ahmed T, Khushnood RA, Ali SM (2021) A predictive mimicker of fracture behavior in fiber reinforced concrete using machine learning. Materials 14(24):7669

    Google Scholar 

  207. Dehestani A, Kazemi F, Abdi R, Nitka M (2022) Prediction of fracture toughness in fibre-reinforced concrete, mortar, and rocks using various machine learning techniques. Eng Fract Mech 276:108914

    Google Scholar 

  208. Cao Y, Zandi Y, Rahimi A, Petković D, Denić N, Stojanović J, Spasić B, Vujović V, Khadimallah MA, Assilzadeh H (2021) Evaluation and monitoring of impact resistance of fiber reinforced concrete by adaptive neuro fuzzy algorithm. Structures 34:3750–3756

    Google Scholar 

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Funding

This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea Government (MSIT) (No. 2021R1A2C4001503).

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Authors and Affiliations

  1. Faculty of Civil and Environmental Engineering, Gdańsk University of Technology, Ul. Narutowicza 11/12, 80-233, Gdańsk, Poland

    Farzin Kazemi

  2. Department of Civil, Environmental & Geomatic Engineering, University College London, 117 Gower Street, London, WC1E 6AP, UK

    Farzin Kazemi

  3. Institute of Fluid Flow Machinery, Polish Academy of Sciences, Generała Józefa Fiszera 14, 80-231, Gdańsk, Poland

    Torkan Shafighfard

  4. Department of Architecture and Architectural Engineering, Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, Republic of Korea

    Doo-Yeol Yoo

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  1. Farzin Kazemi
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Kazemi, F., Shafighfard, T. & Yoo, DY. Data-Driven Modeling of Mechanical Properties of Fiber-Reinforced Concrete: A Critical Review. Arch Computat Methods Eng 31, 2049–2078 (2024). https://doi.org/10.1007/s11831-023-10043-w

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