Providing Models of the Compressive Strength of Square and Rectangular (S/R) Concrete Confined Using Genetic Programming

Moodi, Yaser

doi:10.61186/NMCE.2312.1040

Providing Models of the Compressive Strength of Square and Rectangular (S/R) Concrete Confined Using Genetic Programming

Document Type : Research

Author

Yaser Moodi

Assistant Professor, Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran

10.61186/NMCE.2312.1040

Abstract

The accurate prediction of the compressive strength of FRP (Fiber-Reinforced Polymer)-confined is essential for structural engineers and designers. Several experimental studies have been conducted on concrete confined with FRP sheets. Different models in order to determine the compressive strength of FRP-confined concrete are provided in the previous researches. This study develops a practical model using genetic programming (GP) to reliably predict the compressive strength of FRP-confined concrete across various FRP types, enhancing its applicability for engineers. Firstly, a wide range of experimental data for square and rectangular (S/R) columns confined with a variety of FRP sheets has been collected (Including 463 specimens). 324 specimens (70 %) were used for modeling. For proposing models by using GP, the input and output variables were considered dimensionless. So input variables including b/h, r/b, r/h, r/tf, tf/h, Ff/ fco, and Ef/ fco and output is considered as fcc/fco. To present the model using GP, the three-transfer function set was selected. Finally, results compared with the existing models. The predictions of GP show satisfactory estimations, so that GP have averagely increased R2 approximately 9.91% rather than other models.

Keywords

Genetic Programming

Columns

FRP confinement

Compressive strength

Strengthening

Subjects

Structural Engineering

[1] Nanni A, Bradford NM (1995) FRP jacketed concrete under uniaxial compression. Construction and Building Materials 9:115–124.

[2] Saadatmanesh, H. Ehsani, M.R. Li MW (1994) Strength and Ductility of Concrete Columns Externally Reinforced With Fiber Composite Straps. ACI, Structural Journal 91:434–447

[3] Saafi, M., Toutanji, H. Li Z (1999) Behavior of concrete columns confined with fiber reinforced polymer tubes. ACI, Materials Journal J 96:500–509.

[4] Mirmiran A, Shahawy M (1996) A new concrete-filled hollow FRP composite column. Composites Part B: Engineering 27:263–268.

[5] Moodi Y, Mousavi SR, Ghavidel A, et al (2018) Using Response Surface Methodology and providing a modified model using whale algorithm for estimating the compressive strength of columns confined with FRP sheets. Construction and Building Materials 183:163–170.

[6] Moodi Y, Mousavi SR, Sohrabi MR (2019) New models for estimating compressive strength of concrete confined with FRP sheets in circular sections. Journal of Reinforced Plastics and Composites 38:1014–1028.

[7] Naderpour H, Mirrashid M (2020) Confinement Coefficient Predictive Modeling of FRP-Confined RC Columns. Advances in Civil Engineering Materials 9:20190145.

[8] Kamgar R, Naderpour H, Komeleh HE, et al (2020) A Proposed Soft Computing Model for Ultimate Strength Estimation of FRP-Confined Concrete Cylinders. Applied Sciences 10:1769.

[9] Cui W, Zhao LC, Xu YP, Mamlooki M (2022) The Compressive Strength Prediction for FRP-Confined Concrete in Circular Columns by Applying the Normalized AlexNet-ELM and the Advanced Red Fox Optimization Algorithm. Advanced Theory and Simulations 5:2100410.

[10] Kavya BR, HSS, Prashantha SJ, Shrikanth AS (2022) Prediction of Mechanical Properties Of Steel Fiber Reinforced Concrete Using Cnn. Jordan Journal of Civil Engineering 16:284–293

[11] Choobbasti AJ, Farrokhzad F, Mashaie SR, Azar PH (2015) Mapping of soil layers using artificial neural network (case study of Babol, northern Iran). Journal of the South African Institution of Civil Engineering 57:59–66.

[12] Shahri SF, Mousavi SR (2021) Bond strength prediction of spliced GFRP bars in concrete beams using soft computing methods. Computers and Concrete 24:305–317.

[13] Mousavi SM, Bahr Peyma A, Mousavi SR, Moodi Y (2023) Predicting the Ultimate and Relative Bond Strength of Corroded Bars and Surrounding Concrete by Considering the Effect of Transverse Rebar Using Machine Learning. Iranian Journal of Science and Technology, Transactions of Civil Engineering 47:193–219.

[14] Jamali F, Mousavi SR, Peyma AB, Moodi Y (2022) Prediction of compressive strength of fiber-reinforced polymers-confined cylindrical concrete using artificial intelligence methods. Journal of Reinforced Plastics and Composites 41:679–704.

[15] Rezazadeh Eidgahee D, Rafiean AH, Haddad A (2020) A Novel Formulation for the Compressive Strength of IBP-Based Geopolymer Stabilized Clayey Soils Using ANN and GMDH-NN Approaches. Iranian Journal of Science and Technology - Transactions of Civil Engineering 44:219–229.

[16] Rezaie-Balf M (2019) Multivariate Adaptive Regression Splines Model for Prediction of Local Scour Depth Downstream of an Apron Under 2D Horizontal Jets. Iranian Journal of Science and Technology - Transactions of Civil Engineering 43:103–115.

[17] Koza J (1992) Genetic Programming: On the Programming of Computers by Means of Natural Selection. Bradford Books.

[18] Safarzadeh A, Zaji AH, Bonakdari H (2017) Comparative Assessment of the Hybrid Genetic Algorithm–Artificial Neural Network and Genetic Programming Methods for the Prediction of Longitudinal Velocity Field around a Single Straight Groyne. Applied Soft Computing Journal 60:213–228.

[19] Lim JC, Karakus M, Ozbakkaloglu T (2016) Evaluation of ultimate conditions of FRP-confined concrete columns using genetic programming. Computers & Structures 162:28–37.

[20] Kalfat R, Nazari A, Al-Mahaidi R, Sanjayan J (2016) Genetic programming in the simulation of Frp-to-concrete patch-anchored joints. Composite Structures 138:305–312.

[21] Gandomi AH, Sajedi S, Kiani B, Huang Q (2016) Genetic programming for experimental big data mining: A case study on concrete creep formulation. Automation in Construction 70:89–97.

[22] Cevik A, Arslan MH, Köroǧlu MA (2010) Genetic-programming-based modeling of RC beam torsional strength. KSCE Journal of Civil Engineering 2010 14:3 14:371–384.

[23] Jeong H, Ji S, Kim JH, et al (2022) Development of Mapping Function to Estimate Bond–Slip and Bond Strength of RC Beams Using Genetic Programming. International Journal of Concrete Structures and Materials 16:1–19.

[24] Kheyroddin A, Maleki F (2022) Numerical Methods in Civil Engineering Prediction of effective moment of inertia for hybrid FRP-steel reinforced concrete beams using the genetic algorithm. Numerical Methods in Civil Engineering 7:9–15

[25] Torabi M, Sarkardeh H, Mohammad Mirhosseini S (2022) Prediction of soil permeability coefficient using the GEP approach. Numerical Methods in Civil Engineering 7:9–15

[26] Lim JC, Karakus M, Ozbakkaloglu T (2016) Evaluation of ultimate conditions of FRP-confined concrete columns using genetic programming. Computers & Structures 162:28–37.

[27] Kaveh A, Shabani Rad A (2023) Metaheuristic-based optimal design of truss structures using algebraic force method. Structures 50:1951–1964.

[28] Alavi SH, Mashayekhi M, Zolfaghari M (2024) Optimizing gas pipeline routing considering seismic risk through metaheuristic algorithm. Research Square.

[29] Moodi Y, Ghasemi M, Mousavi SR (2021) Estimating the compressive strength of rectangular fiber reinforced polymer–confined columns using multilayer perceptron, radial basis function, and support vector regression methods. Journal of Reinforced Plastics and Composites 41:130–146.

[30] Ozbakkaloglu T (2013) Behavior of square and rectangular ultra high-strength concrete-filled FRP tubes under axial compression. Composites Part B: Engineering 54:97–111.

[31] Ozbakkaloglu T (2014) Ultra-high-strength concrete-filled frp tubes: Compression tests on square and rectangular columns. Key Engineering Materials. 575-576:239–244

[32] Louk Fanggi BA, Ozbakkaloglu T (2015) Square FRP-HSC-steel composite columns: Behavior under axial compression. Engineering Structures 92:156–171.

[33] Fallah Pour A, Gholampour A, Zheng J, Ozbakkaloglu T (2019) Behavior of FRP-confined high-strength concrete under eccentric compression: Tests on concrete-filled FRP tube columns. Composite Structures 220:261–272.

[34] Demir U, Sahinkaya Y, Ispir M, Ilki A (2018) Assessment of Axial Behavior of Circular HPFRCC Members Externally Confined with FRP Sheets. Polymers 10:138.

[35] Rodriguez-Vazquez K, Fleming PJ (1998) Multiobjective genetic programming: A nonlinear system identification application. Genetic Programming Conference 34:930–936

[36] Moradi M, Bagherieh AR, Esfahani MR (2019) Constitutive modeling of steel fiber-reinforced concrete. The International Journal of Damage Mechanics 29:388–412.

[37] Pham TM, Hadi MNS (2014) Stress Prediction Model for FRP Confined Rectangular Concrete Columns with Rounded Corners. Journal of Composites for Construction 18:04013019.

[38] Moodi Y, Farahi Shahri S, Mousavi SR (2017) Providing a model for estimating the compressive strength of square and rectangular columns confined with a variety of fibre-reinforced polymer sheets. Journal of Reinforced Plastics and Composites 36:1602–1612.

[39] Lam L, Teng JG (2003) Design-Oriented Stress-Strain Model for FRP-Confined Concrete in Rectangular Columns. Journal of Reinforced Plastics and Composites 22:1149–1186.

[40] Harajli MH, Hantouche EG, Soudki K (2006) Stress-strain model for fiber-reinforced polymer jacketed concrete columns. ACI Structural Journal 103:672–682.

[41] Ilki A, Kumbasar N (2003) Compressive behaviour of carbon fibre composite jacketed concrete with circular and non-circular cross-sections. Journal of Earthquake Engineering 7:381–406.

[42] Wei YY, Wu YF (2012) Unified stress-strain model of concrete for FRP-confined columns. Construction and Building Materials 26:381–392.

[43] Toutanji H, Han M, Gilbert J, Matthys S (2010) Behavior of Large-Scale Rectangular Columns Confined with FRP Composites. Journal of Composites for Construction 14:62–71.