Open Access Open Access  Restricted Access Subscription or Fee Access

Estimating Ultimate Moment Capacity of Spirally Reinforced Concrete Columns Using Various Artificial Neural Networks


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/irece.v14i4.22143

Abstract


Over the last few decades, intensive investigations on the artificial neural network capabilities for addressing structural engineering problems have been concluded in the literature. Multiple models for predicting the load-bearing capacity and failure mode have been developed in this regard. However, most of the studies on the capabilities of artificial neural networks for estimating the ultimate moment capacity were focused on the feedforward backpropagation approach. As a result, this research aims to investigate the performance of using different artificial neural network approaches to forecast the ultimate moment capacity of spiral RC columns. As a part of the study, the performance of feedforward backpropagation, cascade-forward neural networks, and generalized regression neural networks will be compared and evaluated against experimental and traditional results. The findings demonstrated that artificial neural networks provide a reliable method for forecasting the spiral RC columns' moment capacity, and they can outweigh code-based empirical formulation.
Copyright © 2023 Praise Worthy Prize - All rights reserved.

Keywords


Spiral Reinforced Concrete Columns; Ultimate Moment Capacity; Artificial Neural Networks; Feedforward Backpropagation; Cascade-Forward Neural Network; Generalized Regression Neural Networks

Full Text:

PDF


References


H. Tanaka, (1990). Effect of lateral confining reinforcement on the ductile behaviour of reinforced concrete columns.

H. L. Coffman, M. L. Marsh, and C. B. Brown, Seismic durability of retrofitted reinforced-concrete columns. Journal of Structural Engineering, Vol.119(5), 1643-1661, 1993.
https://doi.org/10.1061/(ASCE)0733-9445(1993)119:5(1643)

P. Desayi, K. T. Sundara Raja Iyengar, and T. Sanjeeva Reddy, Equation for stress-strain curve of concrete confined in circular steel spiral. Matériaux et Construction, Vol. 11(5), 339-345, 1978.
https://doi.org/10.1007/BF02473875

J. B. Mander, M. J. N. Priestley, and R. Park, Observed stress-strain behavior of confined concrete. Journal of Structural Engineering, Vol.114(8): 1827-1849, 1988.
https://doi.org/10.1061/(ASCE)0733-9445(1988)114:8(1827)

L. Bing, R. Park, and H. Tanaka, Stress-strain behavior of high-strength concrete confined by ultra-high-end normal-strength transverse reinforcements. ACI Structural Journal, Vol.98(3): 395-406, 2001.
https://doi.org/10.14359/10228

J. K. Zhou, W. K. Lin, S. X. Guo, J. J. Zeng, and Y. L. Bai, Behavior of FRP-confined FRP spiral reinforced concrete square columns (FCFRCs) under axial compression. Journal of Building Engineering, Vol.45: 103452, 2022.
https://doi.org/10.1016/j.jobe.2021.103452

M. Ahmadi, H. Naderpour, and A. Kheyroddin, ANN model for predicting the compressive strength of circular steel-confined concrete. International Journal of Civil Engineering, Vol.15(2): 213-221, 2017.
https://doi.org/10.1007/s40999-016-0096-0

W. Chang, and W. Zheng, Estimation of compressive strength of stirrup-confined circular columns using artificial neural networks. Structural Concrete, Vol.20(4), 1328-1339, 2019.
https://doi.org/10.1002/suco.201800259

H. Naderpour, P. Parsa, and M. Mirrashid, Innovative Approach for Moment Capacity Estimation of Spirally Reinforced Concrete Columns Using Swarm Intelligence-Based Algorithms and Neural Network. Practice Periodical on Structural Design and Construction, Vol.26(4), 04021043, 2021.
https://doi.org/10.1061/(ASCE)SC.1943-5576.0000612

H. Naderpour, M. Mirrashid, and P. Parsa, Failure mode prediction of reinforced concrete columns using machine learning methods. Engineering Structures, 248, 113263, 2021.
https://doi.org/10.1016/j.engstruct.2021.113263

D. Cha, H. Zhang, and M. Blumenstein, Prediction of maximum wave-induced liquefaction in porous seabed using multi-artificial neural network model. Ocean Engineering, Vol.38(7), 878-887, 2011.
https://doi.org/10.1016/j.oceaneng.2010.08.002

R. Suryanita, H. Maizir, E. Yuniarto, M. Zulfakar, and H. Jingga, Damage Level Prediction of Reinforced Concrete Building Based on Earthquake Time History Using Artificial Neural Network. MATEC Web of Conferences, Vol.138: 1-9, 2017.
https://doi.org/10.1051/matecconf/201713802024

M. Kubat, Neural networks: a comprehensive foundation by Simon Haykin, Macmillan, 1994, ISBN 0-02-352781-7. In The Knowledge Engineering Review, Vol. 13(4): 409-412, 1999.
https://doi.org/10.1017/S0269888998214044

O. Avci, O. Abdeljaber, S. Kiranyaz, and D. Inman, Convolutional neural networks for real-time and wireless damage detection. Conference Proceedings of the Society for Experimental Mechanics Series, 2020.
https://doi.org/10.1007/978-3-030-12115-0_17

G. Arulampalam, and A. Bouzerdoum, A generalized feedforward neural network architecture for classification and regression. Neural Networks, Vol.16(5-6): 561-568, 2003.
https://doi.org/10.1016/S0893-6080(03)00116-3

D. Thatoi, P. Guru, P. K. Jena, S. Choudhury, and H. C. Das, Comparison of CFBP, FFBP, and RBF networks in the field of crack detection. Modelling and Simulation in Engineering, 2014.
https://doi.org/10.1155/2014/292175

D. S. Badde, A. Gupta, and V. K. Patki, Cascade and Feed Forward Backpropagation Artificial Neural Network Models for Prediction of Compressive Strength of Ready Mix Concrete. IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE), 2278-1684, 1-6, 2009.

S. Gholizadeh, Performance-based optimum seismic design of steel structures by a modified firefly algorithm and a new neural network. Advances in Engineering Software, 81(C), 50-65, 2015.
https://doi.org/10.1016/j.advengsoft.2014.11.003

K. Günaydn, and A. Günaydn, Peak ground acceleration prediction by artificial neural networks for northwestern Turkey. Mathematical Problems in Engineering, 2008.
https://doi.org/10.1155/2008/919420

E. Harirchian, T. Lahmer, and S. Rasulzade, Earthquake hazard safety assessment of existing buildings using optimized multi-layer perceptron neural network. Energies, Vol.13(8), 2020.
https://doi.org/10.3390/en13082060

Tavio, T., Machmoed, S., Raka, I., Behavior of Square RC Columns Confined with Interlocking Square Spiral Under Axial Compressive Loading, (2022) International Journal on Engineering Applications (IREA), 10 (5), pp. 322-335.
https://doi.org/10.15866/irea.v10i5.20655

K. Ahmed, S., Al-Azzawi, Z., Mohammed, S., Utilizing Waste-Plastic Pipes as Hoops in Circular Concrete Columns, (2023) International Review of Civil Engineering (IRECE), 14 (2), pp. 132-143.
https://doi.org/10.15866/irece.v14i2.21252

Shbeeb, N., Barham, W., Barsneh, H., Nonlinear Finite Element Analysis of Concrete Columns Confined by Carbon Fiber Reinforced Polymer Sheets, (2022) International Review of Civil Engineering (IRECE), 13 (4), pp. 275-289.
https://doi.org/10.15866/irece.v13i4.19631

Tavio, T., Sabariman, B., Contribution of Lightweight Brick Infiller on Strength and Stiffness of RC Columns Under Cyclic Loading, (2022) International Journal on Engineering Applications (IREA), 10 (5), pp. 384-392.
https://doi.org/10.15866/irea.v10i5.20457

Yehia, S., Abdelbaky, S., Moubarak, A., Ibrahim, A., Experimental and Analytical Codes Comparative Study for Enhanced BFRP R.C. Columns Under Different Types of Loading, (2023) International Review of Civil Engineering (IRECE), 14 (3), pp. 164-174.
https://doi.org/10.15866/irece.v14i3.22882

Ali, A., Abbas, N., Axial Capacity of Rectangular Concrete-Filled Steel Tube Columns Using Artificial Neural Network, (2021) International Review of Civil Engineering (IRECE), 12 (6), pp. 389-397.
https://doi.org/10.15866/irece.v12i6.20823


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize