Experimental Study on the Shear Capacity of RC Deep Beams Strengthened with External V-Shaped Rods
Pramod Rai
Department of Civil Engineering, Kasetsart University, Jatujak, Bangkok, Thailand , Thailand
Kitjapat Phuvoravan
Department of Civil Engineering, Kasetsart University, Jatujak, Bangkok, Thailand , Thailand
Received 30 April 2025; received in revised form 29 October2025; accepted 07 November 2025
Keywords: external posttension technique, nonlinear finite element, experimental investigation, shear strengthening, reinforced concrete deep beam, strut-and-tie model
Abstract
This research investigated the shear strengthening technique of Reinforced Concrete (RC) deep beams using a V-shaped external rod system. Shear behavior, the stress in an external rod, and the shear capacity at the diagonal shear failure of a strengthened beam were focused mainly. Experimental tests of control and two strengthened beams were carried out to observe the effect of the external rod on shear behavior of RC deep beam. A theoretical approach to compute the stress in the external rod and the nominal strength of the strengthened beam in the diagonal shear failure were examined based on the experimental test results and verified using Finite Element Method (FEM) in ABAQUS. The computed nominal shear strength of the strengthened beam was 10% higher than the experimental test. The strengthening technique shifted the brittle shear failure to ductile shear failure and improved the performance of RC deep beam.
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References
[1] N. K. Im and K. Hasegawa, “Automatic ship berthing using parallel neural controller,” Proc. International Federation of Automatic Control (IFAC), vol. 34, no. 7, pp. 51-57, 2001.
[2] N. K. Im and K. Hasegawa, “Motion identification using neural networks and its application to automatic ship berthing under wind,” Journal of Ship and Ocean Technology, vol. 6, no. 1, pp. 16-26, 2002.
[3] N. K. Im, “All direction approach automatic ship berthing controller using ANN (artificial neural networks),” Journal of Institute of Control, Robotics and Systems, vol. 13, no. 4, pp. 304-308, 2007.
[4] C. H. Bae, S. K. Lee, S. E. Lee, and J. H. Kim, “A study of the automatic berthing system of a ship using artificial neural network,” Journal of Korean Navigation and Port Research, vol. 32, no. 8, pp. 589-596, 2008.
[5] Y. A. Ahmed and K. Hasegawa, “Automatic ship berthing using artificial neural network trained by consistent teaching data using nonlinear programming method,” Engineering Applications of Artificial Intelligence, vol. 26, no. 10, pp. 2287-2304, 2013.
[6] N. K. Im and V. S. Nguyen, “Artificial neural network controller for automatic ship berthing using head-up coordinate system,” International Journal of Naval Architecture and Ocean Engineering, vol. 10, no. 3, pp. 235-249, 2018.
[7] V. Nair and G. E. Hinton, “Rectified linear units improve restricted Boltzmann machines,” Proc. 27th International Conference on Machine Learning (ICML-10), 2010.
[8] A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” Proc. International Conference on Machine Learning, vol. 30, no. 1, 2013.
[9] K. Levenberg, “A method for the solution of certain non-linear problems in least squares,” Quarterly of Applied Mathematics, vol. 2, no. 2, pp. 164-168, 1944.
[10] T. Bazzi, R. Ismail, and M. Zohdy, “Comparative performance of several recent supervised learning algorithms,” International Journal of Computer and Information Technology, vol. 7, 2018.
[11] T. Tieleman and G. Hinton, Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude, COURSERA: Neural Networks for Machine Learning, 2012.
[12] D. P. Kingma and J. Ba, “Adam: a method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.
[13] K. H. Shon, “Hydrodynamic forces and maneuvering characteristics of ships at low advance speed,” Journal of the Society of Naval Architects of Korea, vol. 29, no. 3, pp. 90-101, 1992.
[14] V. S. Nguyen, V. C. Do, and N. K. Im, “Development of automatic ship berthing system using artificial neural network and distance measurement system,” International Journal of Fuzzy Logic and Intelligent Systems, vol. 18, no. 1, pp. 41-49, 2018.
[15] Y. A. Ahmed and K. Hasegawa, “Automatic ship berthing using artificial neural network based on virtual window concept in wind condition,” Proc. International Federation of Automatic Control (IFAC), vol. 45, no. 24, pp. 286-291, 2012.
[16] X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural network,” Proc. International Conference on Artificial Intelligence and Statistics, vol. 9, pp. 246-266, 2010.
[17] K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: surpassing human-level performance on Imagenet classification,” Proc. IEEE International Conference on Computer Vision, pp. 1026-1034, 2015.
[18] H. Robbins and S. Monro, “A stochastic approximation method,” The Annals of Mathematical Stastics, pp. 400-407, 1951.
[19] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533-536, 1986.
[20] J. Duchi, E. Hazan, and Y. Singer, “Adaptive subgradient methods for online learning and stochastic optimization,” Journal of Machine Learning Research, pp. 2121-2159, July 2011.
[21] C. M. Bishop, Neural networks for pattern recognition. Oxford: Clarendon Press, New York, 1996.
[22] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and D. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” Journal of Machine Learning Research, vol. 15, no.1, pp. 1929-1958, 2014.
[23] S. Loffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” arXiv preprint arXiv:1502.03167, 2015.
[24] J. N. Newman, Marine hydrodynamics. Cambridge, MA: MIT Press, 1980.
[25] C. Nwankpa, W. Ijomah, A. Gachagan, and S. Marshall, “Activation functions: comparison of trends in practice and research for deep learning,” arXiv preprint arXiv:1811.03378, 2018.
[26] S. K. Lee, G. W. Lee, S. J. Lee, and S. R. Jeong, “A study on the automatic berthing control of a ship by artificial neural network,” Journal of Korean Navigation and Port Research, vol. 32, no. 4, pp. 21-28, 1997.