Shear Performance of Deep Concrete Beams with Openings Using Waste Tyre Steel Fibres: FEM and ANN Analysis

Daudi Salezi Augustino

Abstract


The creation of transverse openings in beams triggers the shear performance. The dual impact of height and length on the overall shear performance and strain variations in reinforcements of deep concrete beams with and without fibres was assessed to investigate the effect of opening in the beam. This effect of opening was explored and modelled using finite element software Abaqus and predicted using an artificial neural network (ANN) model. The data set for ANN was 56 deep concrete beams, while for the finite element model (FEM), 12 deep concrete beams were used. The effect of input parameters in the ANN model was assessed through sensitivity analysis. Results show that with an increase in opening depth, the strain in top steel reinforcement shifted to tensile strain, resulting in premature beam failure. In addition, experimental and FEM shear resistance had a mean absolute error (MAE) of 4.1, 5.0, and 20.6% for deep beams without fibres, with fibres and fibre mesh, respectively. Compared to available analytical models, the ANN model reasonably predicts the shear resistance with an R2of 0.84 and a mean square error (MSE) of 0.01. The use of the ANN and FEM models is recommended as they save time, and the prediction does not involve degradation of the environment, hence demonstrating sustainable construction practices.

 

Doi: 10.28991/CEJ-2024-010-08-02

Full Text: PDF


Keywords


Sensitivity Analysis; Shear Resistance; Artificial Neural Network (ANN); Reinforced Concrete Deep; Finite Element Model.

References


Augustino, D. S., Kabubo, C., Kanali, C., & Onchiri, R. O. (2022). The orientation effect of opening and internal strengthening on shear performance of deep concrete beam using recycled tyre steel fibres. Results in Engineering, 15, 100561. doi:10.1016/j.rineng.2022.100561.

Kong, F. K., Robins, P. J., & Sharp, G. R. (1975). Design of Reinforced Concrete Deep Beams in Current Practice. Structural Engineer, 53(4), 173–180.

Dang, T. D., Tran, D. T., Nguyen-Minh, L., & Nassif, A. Y. (2021). Shear resistant capacity of steel fibres reinforced concrete deep beams: An experimental investigation and a new prediction model. Structures, 33, 2284–2300. doi:10.1016/j.istruc.2021.05.091.

Hussein, L. T., & Abbas, R. M. (2022). A Semi-Empirical Equation based on the Strut-and-Tie Model for the Shear Strength Prediction of Deep Beams with Multiple Large Web Openings. Engineering, Technology and Applied Science Research, 12(2), 8289–8295. doi:10.48084/etasr.4743.

Smarzewski, P. (2018). Analysis of failure mechanics in hybrid fibre-reinforced high-performance concrete deep beams with and without openings. Materials, 12(1). doi:10.3390/ma12010101.

Augustino, D. S., Kanali, C., Onchiri, R. O., & Kabubo, C. (2023). Simplified shear equation of deep concrete beam considering orientation effect of opening and mechanical properties of fibre-concrete interface. Heliyon, 9(3), 14441. doi:10.1016/j.heliyon.2023.e14441.

Ibrahim, M. A., El Thakeb, A., Mostfa, A. A., & Kottb, H. A. (2018). Proposed formula for design of deep beams with shear openings. HBRC Journal, 14(3), 450–465. doi:10.1016/j.hbrcj.2018.06.001.

Kong, F. K., & Sharp, G. R. (1977). Structural idealization for deep beams with web openings. Magazine of Concrete Research, 29(99), 81–91. doi:10.1680/macr.1977.29.99.81.

Saleh, M., AlHamaydeh, M., & Zakaria, M. (2023). Finite element analysis of reinforced concrete deep beams with square web openings using damage plasticity model. Engineering Structures, 278, 115496. doi:10.1016/j.engstruct.2022.115496.

ABAQUS. (2014). Analysis User’s Guide Volume V: Prescribed Conditions, Constraints & Interactions. ABAQUS. Providence, United States.

Saabye, N., & Petersson, H. (1992). Introduction to the finite element method. Pearson, London, United Kingdom.

Schneidera, T., Hua, Y., Gaob, X., Dumasc, J., Zorina, D., & Panozzoa, D. (2019). A Large-Scale Comparison of Tetrahedral and Hexahedral Elements for Finite Element Analysis. arXiv preprint arXiv:1903.09332.

ABAQUS. (2014). ABAQUS/CAE 6.14 User’s Manual. Abaqus. Providence, United States.

MATLAB. (2012). MATLAB and Statistics Toolbox Release 2012b; The MathWorks, Inc., Natick, Massachusetts, United States.

Sandeep, M. S., Tiprak, K., Kaewunruen, S., Pheinsusom, P., & Pansuk, W. (2023). Shear strength prediction of reinforced concrete beams using machine learning. Structures, 47, 1196–1211. doi:10.1016/j.istruc.2022.11.140.

Amani, J., & Moeini, R. (2012). Prediction of shear strength of reinforced concrete beams using adaptive neuro-fuzzy inference system and artificial neural network. Scientia Iranica, 19(2), 242–248. doi:10.1016/j.scient.2012.02.009.

Awolusi, T. F., Oke, O. L., Akinkurolere, O. O., Sojobi, A. O., & Aluko, O. G. (2019). Performance comparison of neural network training algorithms in the modeling properties of steel fiber reinforced concrete. Heliyon, 5(1), 1115. doi:10.1016/j.heliyon.2018.e01115.

Huang, X., Cao, H., & Jia, B. (2023). Optimization of Levenberg Marquardt Algorithm Applied to Nonlinear Systems. Processes, 11(6), 1794. doi:10.3390/pr11061794.

Sakamoto, H., Matsumoto, K., Kuwahara, A., & Hayami, Y. (2005). Acceleration and stabilization techniques for the Levenberg-Marquardt method. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E88-A(7), 1971–1977. doi:10.1093/ietfec/e88-a.7.1971.

Farber, R. (2011). CUDA Application Design and Development. Elsevier, Amsterdam, Netherlands.

Saleh, M., AlHamaydeh, M., & Zakaria, M. (2023). Shear capacity prediction for reinforced concrete deep beams with web openings using artificial intelligence methods. Engineering Structures, 280, 115675. doi:10.1016/j.engstruct.2023.115675.

Isleem, H. F., Chukka, N. D. K. R., Bahrami, A., Oyebisi, S., Kumar, R., & Qiong, T. (2023). Nonlinear finite element and analytical modelling of reinforced concrete filled steel tube columns under axial compression loading. Results in Engineering, 19. doi:10.1016/j.rineng.2023.101341.

Yap, S. P., Bu, C. H., Alengaram, U. J., Mo, K. H., & Jumaat, M. Z. (2014). Flexural toughness characteristics of steel-polypropylene hybrid fibre-reinforced oil palm shell concrete. Materials and Design, 57, 652–659. doi:10.1016/j.matdes.2014.01.004.

Augustino, D. S., Onchiri, R. O., Kabubo, C., & Kanali, C. (2022). Mechanical and Durability Performance of High-Strength Concrete with Waste Tyre Steel Fibres. Advances in Civil Engineering, 2022. doi:10.1155/2022/4691972.

Mohamed, M. A. S., Ghorbel, E., & Wardeh, G. (2010). Valorization of micro-cellulose fibers in self-compacting concrete. Construction and Building Materials, 24(12), 2473–2480. doi:10.1016/j.conbuildmat.2010.06.009.

Shelote, K. M., Gavali, H. R., Bras, A., & Ralegaonkar, R. V. (2021). Utilization of co-fired blended ash and chopped basalt fiber in the development of sustainable mortar. Sustainability (Switzerland), 13(3), 1–19. doi:10.3390/su13031247.

Li, L., Xia, J., Chin, C., & Jones, S. (2020). Fibre distribution characterization of ultra-high performance fibre-reinforced concrete (Uhpfrc) plates using magnetic probes. Materials, 13(22), 1–20. doi:10.3390/ma13225064.

Le, A. T., & Le Hoang, A. (2023). Comparisons of flexural, split tensile, double punch, and direct tension tests on high-performance concrete reinforced with different fiber types. Case Studies in Construction Materials, 19, 2413. doi:10.1016/j.cscm.2023.e02413.

ACI 318-19. (2019). Building Code Requirements for Structural Concrete (ACI 318-19): An ACI Standard; Commentary on Building Code Requirements for Structural Concrete (ACI 318R-19). American Concrete Institute (ACI), Michigan, United States.

Wang, H., Zhang, C., & Wu, H. (2023). Shear Capacity Prediction Model of Deep Beam Based on New Hybrid Intelligent Algorithm. Buildings, 13(6). doi:10.3390/buildings13061395.

Le Nguyen, K., Thi Trinh, H., Nguyen, T. T., & Nguyen, H. D. (2023). Comparative study on the performance of different machine learning techniques to predict the shear strength of RC deep beams: Model selection and industry implications. Expert Systems with Applications, 230, 120649. doi:10.1016/j.eswa.2023.120649.

Abaqus. (2014). Abaqus 6.11 Theory Manual. Dassault Systèmes Simulia Corp., Providence, United States.

Kanos, A., Kanos, A., Perdikaris, P. (2006). Size Effect on Concrete Splitting Tensile Strength and Modulus of Elasticity. Measuring, Monitoring and Modeling Concrete Properties, Springer, Dordrecht, Netherlands. doi:10.1007/978-1-4020-5104-3_29.

Rai, P. (2021). Non-Linear Finite Element Analysis of RC Deep Beam Using CDP Model. Advances in Technology Innovation, 6(1), 01–10. doi:10.46604/aiti.2021.5407.

Rutner, M. P. (2010). Using composite behavior to improve the blast resistance of columns in buildings. Blast Protection of Civil Infrastructures and Vehicles Using Composites, 342–374, Woodhead Publishing, Sawston, United Kingdom. doi:10.1533/9781845698034.2.342.

Van der Aa, P. J. (2014). Biaxial Stresses in Steel Fibre Reinforced Concrete Modelling the Pull-Out Behaviour of a Single Steel Fibre using FEM. Master Thesis, Eindhoven University of Technology, Eindhoven, Netherlands.

Joosten, M. W., Dingle, M., Mouritz, A., Khatibi, A. A., Agius, S., & Wang, C. H. (2016). A hybrid embedded cohesive element method for predicting matrix cracking in composites. Composite Structures, 136, 554–565. doi:10.1016/j.compstruct.2015.10.030.

EN 1992-1-1. (2004). Eurocode 2: Design of concrete structures - Part 1-1: General rules and rules for buildings. European Committee for Standardization, Brussels, Belgium.

Shahnewaz, M., & Alam, M. S. (2014). Improved shear equations for steel fiber-reinforced concrete deep and slender beams. ACI Structural Journal, 111(4), 851–860. doi:10.14359/51686736.

Slater, E., Moni, M., & Alam, M. S. (2012). Predicting the shear strength of steel fiber reinforced concrete beams. Construction and Building Materials, 26(1), 423–436. doi:10.1016/j.conbuildmat.2011.06.042.

Hsu, L. S., & Hsu, C. T. T. (1994). Complete stress – strain behaviour of high-strength concrete under compression. Magazine of Concrete Research, 46(169), 301–312. doi:10.1680/macr.1994.46.169.301.

Wahalathantri, B. L., Thambiratnam, D. P., Chan, T. H. T., & Fawzia, S. (2011). A material model for flexural crack simulation in reinforced concrete elements using ABAQUS. First International Conference on Engineering, Design and Developing the Built Environment for Sustainable Wellbeing, Queensland University of Technology, Brisbane, Australia.

Qin, X., Huang, X., Li, Y., & Kaewunruen, S. (2024). Sustainable design framework for enhancing shear capacity in beams using recycled steel fiber-reinforced high-strength concrete. Construction and Building Materials, 411, 134509. doi:10.1016/j.conbuildmat.2023.134509.

Lantsoght, E. O. L. (2019). How do steel fibers improve the shear capacity of reinforced concrete beams without stirrups? Composites Part B: Engineering, 175(July), 107079. doi:10.1016/j.compositesb.2019.107079.

Mansur, M. A., & Alwis, W. A. M. (1984). Reinforced fibre concrete deep beams with web openings. International Journal of Cement Composites and Lightweight Concrete, 6(4), 263–271. doi:10.1016/0262-5075(84)90021-6.

Alsaeq, H. M. (2013). Effects of opening shape and location on the structural strength of RC deep beams with openings. International Journal of Civil and Environmental Engineering, 7(6), 494–499.

Mohamed, A. R., Shoukry, M. S., & Saeed, J. M. (2014). Prediction of the behavior of reinforced concrete deep beams with web openings using the finite element method. Alexandria Engineering Journal, 53(2), 329–339. doi:10.1016/j.aej.2014.03.001.

Al-Ahmed, A. H. A., & Khalaf, M. R. (2017). Openings effect on the performance of reinforced concrete deep beams. Proceeding of the First MoHESR and HCED Iraqi Scholars Conference in Australasia, 5-6 December, 2017, Melbourne, Australia.

Schlaich, J., Schaefer, K., & Jennewein, M. (1987). Toward a Consistent Design of Structural Concrete. PCI Journal, 32(3), 74–150. doi:10.15554/pcij.05011987.74.150.

Li, L. (2019). Fibre Distribution Characterization and its Impact on Mechanical Properties of Ultra-high Performance Fibre Reinforced Concrete. PhD thesis, The University of Liverpool, Liverpool, United Kingdom.

Peng, F., Cai, Y., Yi, W., & Xue, W. (2023). Shear behavior of two-span continuous concrete deep beams reinforced with GFRP bars. Engineering Structures, 290, 116367. doi:10.1016/j.engstruct.2023.116367.

Abadel, A., Alenzi, S., Almusallam, T., Abbas, H., & Al-Salloum, Y. (2023). Shear behavior of self-consolidating concrete deep beams reinforced with hybrid of steel and GFRP bars. Ain Shams Engineering Journal, 14(9), 102136. doi:10.1016/j.asej.2023.102136.

Isleem, H. F., Augustino, D. S., Mohammed, A. S., Najemalden, A. M., Jagadesh, P., Qaidi, S., & Sabri, M. M. S. (2023). Finite element, analytical, artificial neural network models for carbon fibre reinforced polymer confined concrete filled steel columns with elliptical cross sections. Frontiers in Materials, 9. doi:10.3389/fmats.2022.1115394.

Schmidhuber, J. (2015). Deep Learning in neural networks: An overview. Neural Networks, 61, 85–117. doi:10.1016/j.neunet.2014.09.003.

Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A Fast Learning Algorithm for Deep Belief Nets. Neural Computation, 18(7), 1527–1554. doi:10.1162/neco.2006.18.7.1527.

Heaton, J. (2015). Deep learning and neural networks. Heaton Research, Incorporated, Chesterfield, United States.

Demuth, H. B., & Beale, M. H. (2000). Neural network toolbox: for use with MATLAB: user's guide. The MathWorks, Inc., Natick, Massachusetts, United States.

Sheela, K. G., & Deepa, S. N. (2013). Review on Methods to Fix Number of Hidden Neurons in Neural Networks. Mathematical Problems in Engineering, 2013, 1–11. doi:10.1155/2013/425740.

Rachmatullah, M. I. C., Santoso, J., & Surendro, K. (2021). Determining the number of hidden layer and hidden neuron of neural network for wind speed prediction. PeerJ Computer Science, 7, e724. doi:10.7717/peerj-cs.724.

Mrzygłód, B., Hawryluk, M., Janik, M., & Olejarczyk-Wożeńska, I. (2020). Sensitivity analysis of the artificial neural networks in a system for durability prediction of forging tools to forgings made of C45 steel. The International Journal of Advanced Manufacturing Technology, 109(5–6), 1385–1395. doi:10.1007/s00170-020-05641-y.

Hasan, B. A., & Saeed, J. A. (2023). Effect of Concrete Strength on Shear Capacity of Reinforced High-Strength Concrete Continuous Beams without Web Reinforcements. Advances in Civil Engineering, 8784575. doi:10.1155/2023/8784575.


Full Text: PDF

DOI: 10.28991/CEJ-2024-010-08-02

Refbacks

  • There are currently no refbacks.




Copyright (c) 2024 Daudi Salezi Augustino

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
x
Message