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A novel formulation for predicting the shear strength of RC walls using meta-heuristic algorithms

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Abstract

Reinforced concrete (RC) shear walls play a pivotal role in resisting seismic and lateral loads within structural frameworks. A thorough examination of the existing literature was undertaken, covering a range of experimental and theoretical studies related to the design of RC shear walls. It was emphasized that comprehending shear failure behavior and precisely predicting the shear strength of RC walls holds considerable significance. To address this, the study proposes two models that integrate the support vector regression method with meta-heuristic optimization algorithms (Bat and GOA), utilizing 228 sets of experimental data. In identifying the parameters influencing the shear strength of RC shear walls, the study focused on eight influential factors. The comparison of the two proposed models in the current research with existing models and experimental data demonstrated their commendable accuracy, surpassing the performance of suggested empirical formulations. The prediction errors associated with the proposed models, when compared to experimental data, were notably low. An innovative approach was introduced in the research, presenting a novel method for predicting shear strength using the support vector regression method and the Bat optimization algorithm. A notable advantage of this formulation lies in its capacity to predict the shear strength across various configurations, including squat, cylindrical, and thin RC shear walls. Unlike some existing equations for predicting shear strength, this formulation exhibits no limitations. Through a comparative analysis with established equations, the computational framework’s results suggest its successful applicability in building codes and construction practices. The proposed method contributes to the accurate prediction of shear strength in diverse RC shear wall configurations, offering a valuable tool for structural engineering applications.

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References

  1. Irwin AW, Ord AEC (1976) Cyclic load tests on shear wall coupling beams. Proc Inst Civ Eng. https://doi.org/10.1680/iicep.1976.3442

    Article  Google Scholar 

  2. Barda F, Hanson JM, Corley WG (1977) Shear strength of low-rise walls with boundary elements. Lehigh University, Bethlehem. https://doi.org/10.14359/17697

    Book  Google Scholar 

  3. Saito H, Kikuchi R, Kanechika M, Okamoto K (1989) Experimental study of the effect of concrete strength on shear wall behavior

  4. Salonikios TN, Kappos AJ (1999) Cyclic load behavior of low-slenderness reinforced. ACI Struct J 96:649–660

    Google Scholar 

  5. Matsui T, Kabeyasawa T, Koto A, Kuramoto H, Nagashima I (2004) Shaking table test and analysis of reinforced concrete walls. In: Proceedings of the 13th world conference on earthquake engineering

  6. Gulec CK, Whittaker AS, Stojadinovic B (2008) Shear strength of squat rectangular reinforced concrete walls. ACI Struct J 105:488

    Google Scholar 

  7. Wood SL (1990) Shear strength of low-rise reinforced concrete walls. ACI Struct J 87:99–107. https://doi.org/10.14359/2951

    Article  Google Scholar 

  8. Xiang W (2009) Seismic performance of RC structural squat walls with limited transverse reinforcement

  9. Carrillo J, Alcocer SM (2013) Shear strength of reinforced concrete walls for seismic design of low-rise housing. ACI Struct J 110:415

    Google Scholar 

  10. Park H-G, Baek J-W, Lee J-H, Shin H-M (2015) Cyclic loading tests for shear strength of low-rise reinforced concrete walls with grade 550 MPa bars. ACI Struct J 112:299–310

    Google Scholar 

  11. Kassem W (2015) Shear strength of squat walls: a strut-and-tie model and closed-form design formula. Eng Struct 84:430–438. https://doi.org/10.1016/j.engstruct.2014.11.027

    Article  Google Scholar 

  12. Ngo NT, Le HA, Pham TPT (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 

  13. Naderpour H, Parsa P, Mirrashid M (2021) Innovative approach for moment capacity estimation of spirally reinforced concrete columns using swarm intelligence-based algorithms and neural network. Pract Period Struct Des Constr 26:04021043. https://doi.org/10.1061/(asce)sc.1943-5576.0000612

    Article  Google Scholar 

  14. Zhang J, Wang Y (2021) Evaluating the bond strength of FRP-to-concrete composite joints using metaheuristic-optimized least-squares support vector regression. Neural Comput Appl 33:3621–3635. https://doi.org/10.1007/s00521-020-05191-0

    Article  Google Scholar 

  15. Khademi A, Behfarnia K, Šipoš TK (2021) The use of machine learning models in estimating the compressive strength of recycled brick aggregate concrete. Comput Eng Phys Model 4:1–25

    Google Scholar 

  16. Naderpour H, Mirrashid M, Parsa P (2021) Failure mode prediction of reinforced concrete columns using machine learning methods. Eng Struct. https://doi.org/10.1016/j.engstruct.2021.113263

    Article  Google Scholar 

  17. Nayak SC, Nayak SK, Panda SK (2021) Assessing compressive strength of concrete with extreme learning machine. J Soft Comput Civ Eng 5:68–85. https://doi.org/10.22115/SCCE.2021.286525.1320

    Article  Google Scholar 

  18. Yang Y, Cho IH (2021) Multiple target machine learning prediction of capacity curves of reinforced concrete shear walls. J Soft Comput Civ Eng 5:90–113. https://doi.org/10.22115/SCCE.2021.314998.1381

    Article  Google Scholar 

  19. Ly HB, Nguyen MH, Pham BT (2021) Metaheuristic optimization of Levenberg–Marquardt-based artificial neural network using particle swarm optimization for prediction of foamed concrete compressive strength. Neural Comput Appl 33:17331–17351. https://doi.org/10.1007/s00521-021-06321-y

    Article  Google Scholar 

  20. Chen XL, Fu JP, Yao JL, Gan JF (2018) Prediction of shear strength for squat RC walls using a hybrid ANN–PSO model. Eng Comput 34:367–383. https://doi.org/10.1007/s00366-017-0547-5

    Article  Google Scholar 

  21. Parsa P, Naderpour H (2021) Shear strength estimation of reinforced concrete walls using support vector regression improved by teaching–learning-based optimization, particle swarm optimization, and Harris Hawks optimization algorithms. J Build Eng 44:102593. https://doi.org/10.1016/j.jobe.2021.102593

    Article  Google Scholar 

  22. Seif ElDin HM, Aly N, Galal K (2019) In-plane shear strength equation for fully grouted reinforced masonry shear walls. Eng Struct 190:319–332. https://doi.org/10.1016/j.engstruct.2019.03.079

    Article  Google Scholar 

  23. Arafa A, Farghaly AS, Benmokrane B (2018) Evaluation of flexural and shear stiffness of concrete squat walls reinforced with glass fiber-reinforced polymer bars. ACI Struct J 115:211–221. https://doi.org/10.14359/51700987

    Article  Google Scholar 

  24. Carrillo J, Oyarzo-Vera C, Blandón C (2019) Damage assessment of squat, thin and lightly-reinforced concrete walls by the Park & Ang damage index. J Build Eng 26:100921. https://doi.org/10.1016/j.jobe.2019.100921

    Article  Google Scholar 

  25. Huang Z, Shen J, Lin H, Song X, Yao Y (2020) Shear behavior of concrete shear walls with CFRP grids under lateral cyclic loading. Eng Struct 211:110422. https://doi.org/10.1016/j.engstruct.2020.110422

    Article  Google Scholar 

  26. Massone LM, Ostoic DF (2020) Shear strength estimation of masonry walls using a panel model. Eng Struct 204:109900. https://doi.org/10.1016/j.engstruct.2019.109900

    Article  Google Scholar 

  27. Marzok A, Lavan O, Dancygier AN (2020) Predictions of moment and deflection capacities of RC shear walls by different analytical models. Structures 26:105–127. https://doi.org/10.1016/j.istruc.2020.03.059

    Article  Google Scholar 

  28. Hung CC, Hsieh PL (2020) Comparative study on shear failure behavior of squat high-strength steel reinforced concrete shear walls with various high-strength concrete materials. Structures 23:56–68. https://doi.org/10.1016/j.istruc.2019.11.002

    Article  Google Scholar 

  29. Vapnik VN (1995) The nature of statistical learning theory. Springer, New York. https://doi.org/10.1007/978-1-4757-2440-0

    Book  Google Scholar 

  30. Karmy JP, Maldonado S (2019) Hierarchical time series forecasting via support vector regression in the European Travel Retail Industry. Expert Syst Appl 137:59–73. https://doi.org/10.1016/j.eswa.2019.06.060

    Article  Google Scholar 

  31. Pal M, Deswal S (2011) Support vector regression based shear strength modelling of deep beams. Comput Struct 89:1430–1439. https://doi.org/10.1016/j.compstruc.2011.03.005

    Article  Google Scholar 

  32. 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 230:117021. https://doi.org/10.1016/j.conbuildmat.2019.117021

    Article  Google Scholar 

  33. Bennett KP, Mangasarian OL (1992) Robust linear programming discrimination of two linearly inseparable sets. Optim Methods Softw 1:23–34. https://doi.org/10.1080/10556789208805504

    Article  Google Scholar 

  34. Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47. https://doi.org/10.1016/j.advengsoft.2017.01.004

    Article  Google Scholar 

  35. Yang XS (2010) A new metaheuristic Bat-inspired algorithm. In: Studies in computational intelligence. Springer, pp 65–74. https://doi.org/10.1007/978-3-642-12538-6_6

  36. Yildizdan G, Baykan ÖK (2020) A novel modified bat algorithm hybridizing by differential evolution algorithm. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2019.112949

    Article  Google Scholar 

  37. Yang XS, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput (Swansea, Wales) 29:464–483. https://doi.org/10.1108/02644401211235834

    Article  Google Scholar 

  38. Altringham JD (1996) BATS: biology and behaviour. Oxford University Press, New York, p 262

    Google Scholar 

  39. Gandomi AH, Yang XS, Alavi AH, Talatahari S (2013) Bat algorithm for constrained optimization tasks. Neural Comput Appl 22:1239–1255. https://doi.org/10.1007/s00521-012-1028-9

    Article  Google Scholar 

  40. Oesterle RG, Fiorato AE, Aristizabal-Ochoa JD, Corley WG (1980) Hysteretic response of reinforced concrete structural walls. ACI Spec Publ 63:243–273

    Google Scholar 

  41. Oesterle RG, Fiorato AE, Corley WG (1980) Reinforcement details for earthquake-resistant structural walls. Portland Cement Association, Washington

    Google Scholar 

  42. Shiu KN, Daniel JI, Aristizabal-Ochoa JD, Fiorato AE, Corley WG (1981) Earthquake resistant structural walls: test of walls with and without openings. STIN 82:21451

    Google Scholar 

  43. Jiang HJ, Lu XL (1999) Calculating method for the hysteretic characteristics of shear walls dissipating energy along the vertical direction. J Tongji Univ 6

  44. Hube MA, María HS, López M (2017) Experimental campaign of thin reinforced concrete shear walls for low-rise constructions. In: 16th world conference on earthquake

  45. Alarcon C, Hube MA, De la Llera JC (2014) Effect of axial loads in the seismic behavior of reinforced concrete walls with unconfined wall boundaries. Eng Struct 73:13–23

    Google Scholar 

  46. Segura CL Jr, Wallace JW (2018) Seismic performance limitations and detailing of slender reinforced concrete walls. ACI Struct J 115:849–859

    Google Scholar 

  47. Dragan D, Plumier A, Degée H (2018) Experimental study regarding shear behavior of concrete walls reinforced by multiple steel profiles. In: High tech concrete: where technology and engineering meet. Springer, pp 1077–1084

  48. Ganesan N, Indira PV, Seena P (2015) Reverse cyclic tests on high performance cement concrete shear walls with barbells. In: Advances in structural engineering: materials. Springer, pp 2309–2321

  49. Mansur MA, Balendra T, H’ng SC (1992) Tests on reinforced concrete low-rise shear walls under cyclic loading. Concr Shear Earthq 95

  50. Barda F, Hanson JM, Corley WG (1977) Shear strength of low-rise walls with boundary elements. https://doi.org/10.14359/17697

  51. Liu G, Song Y, Qu F (2010) Post-fire cyclic behavior of reinforced concrete shear walls. J Cent South Univ Technol 17:1103–1108

    Google Scholar 

  52. Quiroz LG, Maruyama Y, Zavala C (2013) Cyclic behavior of thin RC Peruvian shear walls: Full-scale experimental investigation and numerical simulation. Eng Struct 52:153–167

    Google Scholar 

  53. Thomsen JH, Wallace JW (1995) Displacement based design of reinforced concrete structural walls: an experimental investigation of walls with rectangular and t-shaped cross-sections: a dissertation

  54. Tran TA, Wallace JW (2012) Experimental study of nonlinear flexural and shear deformations of reinforced concrete structural walls. In: Proceedings, 15th world conference on earthquake engineering, Lisbon, Portland

  55. Layssi H, Mitchell D (2012) Experiments on seismic retrofit and repair of reinforced concrete shear walls. In: Proceedings of the 6th international conference on FRP composites in civil engineering, pp 13–15

  56. Ghorbani-Renani I, Velev N, Tremblay R, Palermo D, Massicotte B, Léger P (2009) Modeling and testing influence of scaling effects on inelastic response of shear walls. ACI Struct J 106:358

    Google Scholar 

  57. Ghazizadeh S, Cruz-Noguez CA, Li Y (2019) Numerical study of hybrid GFRP-steel reinforced concrete shear walls and SFRC walls. Eng Struct 180:700–712

    Google Scholar 

  58. Athanasopoulou A (2010) Shear strength and drift capacity of reinforced concrete and high-performance fiber reinforced concrete low-rise walls subjected to displacement reversals (2010)

  59. Tupper B (1999) Seismic response of reinforced concrete walls with steel boundary elements

  60. Carrillo J, Lizarazo JM, Bonett R (2015) Effect of lightweight and low-strength concrete on seismic performance of thin lightly-reinforced shear walls. Eng Struct 93:61–69

    Google Scholar 

  61. Yuan W, Zhao J, Sun Y, Zeng L (2018) Experimental study on seismic behavior of concrete walls reinforced by PC strands. Eng Struct 175:577–590

    Google Scholar 

  62. Umemura H, Takizawa H (1982) Dynamic response of reinforced concrete buildings. IABSE, Zurich

    Google Scholar 

  63. Greifenhagen C, Lestuzzi P (2005) Static cyclic tests on lightly reinforced concrete shear walls. Eng Struct 27:1703–1712

    Google Scholar 

  64. Looi DTW, Su RKL, Cheng B, Tsang HH (2017) Effects of axial load on seismic performance of reinforced concrete walls with short shear span. Eng Struct 151:312–326

    Google Scholar 

  65. Lopes MS (2001) Experimental shear-dominated response of RC walls. Part II: discussion of results and design implications. Eng Struct 23:564–574

    Google Scholar 

  66. Ghazizadeh S, Cruz-Noguez CA (2018) Damage-resistant reinforced concrete low-rise walls with hybrid GFRP-steel reinforcement and steel fibers. J Compos Constr 22:4018002

    Google Scholar 

  67. Yanez FV, Park R, Paulay T (1991) Seismic behaviour of reinforced concrete structural walls with irregular openings. In: Pacific conference on earthquake engineering, pp 3303–3308

  68. M Bouchon, N Orbovic, N Foure (2004) Tests on reinforced concrete low-rise shear walls under static cyclic loading. In: Proceedings of the 13th world conference on earthquake engineering, p 10

  69. Lefas ID, Kotsovos MD, Ambraseys NN (1990) Behavior of reinforced concrete structural walls. Strength, deformation characteristics, and failure mechanism. ACI Struct J 87:23–31. https://doi.org/10.14359/2911

    Article  Google Scholar 

  70. Lefas ID, Kotsovos MD (1990) Strength and deformation characteristics of reinforced concrete walls under load reversals. ACI Struct J. https://doi.org/10.14359/2994

    Article  Google Scholar 

  71. Pilakoutas K, Elnashai AS (1995) Cyclic behavior of reinforced concrete cantilever walls, part I: experimental results. ACI Struct J 92:271–281

    Google Scholar 

  72. Gupta A, Rangan BV (1998) High-strength concrete (HSC) structural walls. Struct J 95:194–204

    Google Scholar 

  73. Mickleborough NC, Ning F, Chan C-M (1999) Prediction of stiffness of reinforced concrete shearwalls under service loads. Struct J 96:1018–1026

    Google Scholar 

  74. Sittipunt C, Wood SL, Lukkunaprasit P, Pattararattanakul P (2001) Cyclic behavior of reinforced concrete structural walls with diagonal web reinforcement. Struct J 98:554–562

    Google Scholar 

  75. Zhang Y, Wang Z (2000) Seismic behavior of reinforced concrete shear walls subjected to high axial loading. Struct J 97:739–750

    Google Scholar 

  76. Terzioglu T, Orakcal K, Massone LM (2018) Cyclic lateral load behavior of squat reinforced concrete walls. Eng Struct 160:147–160. https://doi.org/10.1016/j.engstruct.2018.01.024

    Article  Google Scholar 

  77. Abdulridha A, Palermo D (2017) Behaviour and modelling of hybrid SMA-steel reinforced concrete slender shear wall. Eng Struct 147:77–89

    Google Scholar 

  78. Qazi S, Michel L, Ferrier E (2019) Seismic behaviour of RC short shear wall strengthened with externally bonded CFRP strips. Compos Struct 211:390–400

    Google Scholar 

  79. Wang W, Wang Y, Lu Z (2018) Experimental study on seismic behavior of steel plate reinforced concrete composite shear wall. Eng Struct 160:281–292

    Google Scholar 

  80. Ren F, Chen J, Chen G, Guo Y, Jiang T (2018) Seismic behavior of composite shear walls incorporating concrete-filled steel and FRP tubes as boundary elements. Eng Struct 168:405–419

    Google Scholar 

  81. Le Nguyen K, Brun M, Limam A, Ferrier E, Michel L (2014) Pushover experiment and numerical analyses on CFRP-retrofit concrete shear walls with different aspect ratios. Compos Struct 113:403–418

    Google Scholar 

  82. Choi CS (2006) Improvement of earthquake-resistant performance of squat shear walls under reversed cyclic loads. In: Key engineering materials. Trans Tech Publication, pp 535–538

  83. Cortés-Puentes WL, Palermo D (2018) Seismic retrofit of concrete shear walls with SMA tension braces. J Struct Eng 144:4017200

    Google Scholar 

  84. Qiao Q, Cao W, Qian Z, Li X, Zhang W, Liu W (2017) Cyclic behavior of low rise concrete shear walls containing recycled coarse and fine aggregates. Materials (Basel) 10:1400

    Google Scholar 

  85. Blandon CA, Arteta CA, Bonett RL, Carrillo J, Beyer K, Almeida JP (2018) Response of thin lightly-reinforced concrete walls under cyclic loading. Eng Struct 176:175–187

    Google Scholar 

  86. Christidis KI, Vougioukas E, Trezos KG (2016) Strengthening of non-conforming RC shear walls using different steel configurations. Eng Struct 124:258–268

    Google Scholar 

  87. Liao F-Y, Han L-H, Tao Z (2012) Performance of reinforced concrete shear walls with steel reinforced concrete boundary columns. Eng Struct 44:186–209

    Google Scholar 

  88. Dazio A, Beyer K, Bachmann H (2009) Quasi-static cyclic tests and plastic hinge analysis of RC structural walls. Eng Struct 31:1556–1571

    Google Scholar 

  89. Zhi Q, Guo Z, Xiao Q, Yuan F, Song J (2017) Quasi-static test and strut-and-tie modeling of precast concrete shear walls with grouted lap-spliced connections. Constr Build Mater 150:190–203. https://doi.org/10.1016/j.conbuildmat.2017.05.183

    Article  Google Scholar 

  90. Shen D, Yang Q, Jiao Y, Cui Z, Zhang J (2017) Experimental investigations on reinforced concrete shear walls strengthened with basalt fiber-reinforced polymers under cyclic load. Constr Build Mater 136:217–229

    Google Scholar 

  91. Zhang J-W, Zheng W-B, Cao W-L, Dong H-Y, Li W-D (2019) Seismic behavior of low-rise concrete shear wall with single layer of web reinforcement and inclined rebars: restoring force model. KSCE J Civ Eng 23:1302–1319

    Google Scholar 

  92. Hidalgo PA, Ledezma CA, Jordan RM (2002) Seismic behavior of squat reinforced concrete shear walls. Earthq Spectra 18:287–308

    Google Scholar 

  93. Zhou A, Qu BY, Li H, Zhao SZ, Suganthan PN, Zhangd Q (2011) Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm Evol Comput 1:32–49. https://doi.org/10.1016/j.swevo.2011.03.001

    Article  Google Scholar 

  94. Gogna A, Tayal A (2013) Metaheuristics: review and application. J Exp Theor Artif Intell 25:503–526. https://doi.org/10.1080/0952813X.2013.782347

    Article  Google Scholar 

  95. Gunn SR (1998) Support vector machines for classification and regression. ISIS Tech Rep 14:5–16

    Google Scholar 

  96. A.C. Institute (2014) Building code requirements for structural concrete (ACI 318-14): commentary on building code requirements for structural concrete (ACI 318R-14): an ACI report, American Concrete Institute. ACI

  97. A.S. of C. Engineers (2005) Seismic design criteria for structures, systems, and components in nuclear facilities (ASCE/SEI 43-05).: introduction; chapter 2 earthquake ground motion; chapter 3 evaluation of seismic demand; chapter 4 evaluation of structural capacity; chapter 5 load. American Society of Civil Engineers

  98. Wang M, Wan W (2019) A new empirical formula for evaluating uniaxial compressive strength using the Schmidt hammer test. Int J Rock Mech Min Sci 123:104094. https://doi.org/10.1016/j.ijrmms.2019.104094

    Article  Google Scholar 

  99. Ali M, Zhu P, Jiang R, Huolin M, Ehsan M, Hussain W, Zhang H, Ashraf U, Ullaah J (2023) Reservoir characterization through comprehensive modeling of elastic logs prediction in heterogeneous rocks using unsupervised clustering and class-based ensemble machine learning. Appl Soft Comput 148:110843. https://doi.org/10.1016/j.asoc.2023.110843

    Article  Google Scholar 

  100. Ali M, Zhu P, Huolin M, Pan H, Abbas K, Ashraf U, Ullah J, Jiang R, Zhang H (2023) A novel machine learning approach for detecting outliers, rebuilding well logs, and enhancing reservoir characterization. Nat Resour Res 32:1047–1066. https://doi.org/10.1007/s11053-023-10184-6

    Article  Google Scholar 

  101. Gandomi AH, Roke DA, Sett K (2013) Genetic programming for moment capacity modeling of ferrocement members. Eng Struct 57:169–176. https://doi.org/10.1016/j.engstruct.2013.09.022

    Article  Google Scholar 

  102. Naderpour H, Rezazadeh Eidgahee D, Fakharian P, Rafiean AH, Kalantari SM (2019) A new proposed approach for moment capacity estimation of ferrocement members using group method of data handling. Eng Sci Technol Int J. https://doi.org/10.1016/j.jestch.2019.05.013

    Article  Google Scholar 

  103. Naderpour H, Kheyroddin A, Amiri GG (2010) Prediction of FRP-confined compressive strength of concrete using artificial neural networks. Compos Struct 92:2817–2829. https://doi.org/10.1016/j.compstruct.2010.04.008

    Article  Google Scholar 

  104. Naderpour H, Mirrashid M (2018) An innovative approach for compressive strength estimation of mortars having calcium inosilicate minerals. J Build Eng 19:205–215. https://doi.org/10.1016/j.jobe.2018.05.012

    Article  Google Scholar 

  105. Kalman Sipos T, Parsa P (2020) Empirical formulation of ferrocement members moment capacity using artificial neural networks. J Soft Comput Civ Eng 4:111–126. https://doi.org/10.22115/scce.2020.221268.1181

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Civil Engineering, Semnan University, Semnan, Iran

    Payam Parsa & Hosein Naderpour

  2. Department of Civil Engineering, Toronto Metropolitan University, Toronto, Canada

    Hosein Naderpour

  3. Department of Civil and Mineral Engineering, University of Toronto, Toronto, Canada

    Nima Ezami

  4. GEI Consultants Inc., Markham, Canada

    Nima Ezami

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  1. Payam Parsa
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Parsa, P., Naderpour, H. & Ezami, N. A novel formulation for predicting the shear strength of RC walls using meta-heuristic algorithms. Neural Comput & Applic 36, 8727–8756 (2024). https://doi.org/10.1007/s00521-024-09514-3

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