Application of Neural Networks for the Estimation of the Shear Strength of Circular RC Columns
Received: 5 August 2022 | Revised: 21 August 2022 | Accepted: 26 August 2022 | Online: 27 September 2022
Corresponding author: D. D. Nguyen
Abstract
This study aims to develop Artificial Neural Networks (ANNs) for predicting the shear strength of circular Reinforced Concrete (RC) columns. A set of 156 experimental data samples of various circular RC columns were utilized to establish the ANN model. The performance results of the ANN model show that it predicts the shear strength of circular RC columns accurately with a high coefficient of determination (0.99) and a small root-mean-square error (4.6kN). The result comparison reveals that the proposed ANN model can predict the shear strength of the columns more accurately than the existing equations. Moreover, an ANN-based formula is proposed to explicitly calculate the shear strength of the columns. Additionally, a practical Graphical User Interface (GUI) tool is developed for facilitating the practical design process of the circular RC columns.
Keywords:
circular reinforced concrete column, shear strength, graphical user interface, artificial neural networksDownloads
References
N. Caglar, "Neural network based approach for determining the shear strength of circular reinforced concrete columns," Construction and Building Materials, vol. 23, no. 10, pp. 3225–3232, Oct. 2009. DOI: https://doi.org/10.1016/j.conbuildmat.2009.06.002
H. Ketabdari, F. Karimi, and M. Rasouli, "Shear strength prediction of short circular reinforced-concrete columns using soft computing methods," Advances in Structural Engineering, vol. 23, no. 14, pp. 3048–3061, Oct. 2020. DOI: https://doi.org/10.1177/1369433220927270
A. Fiore, G. C. Marano, D. Laucelli, and P. Monaco, "Evolutionary Modeling to Evaluate the Shear Behavior of Circular Reinforced Concrete Columns," Advances in Civil Engineering, vol. 2014, Sep. 2014, Art. no. e684256. DOI: https://doi.org/10.1155/2014/684256
A. Said and N. Gordon, "Predicting Shear Strength of RC Columns Using Artificial Neural Networks," Journal of Building Materials and Structures, vol. 6, no. 2, pp. 64–76, Jul. 2019. DOI: https://doi.org/10.34118/jbms.v6i2.69
H.-T. Thai, "Machine learning for structural engineering: A state-of-the-art review," Structures, vol. 38, pp. 448–491, Apr. 2022. DOI: https://doi.org/10.1016/j.istruc.2022.02.003
V.-L. Tran, D.-K. Thai, and S.-E. Kim, "Application of ANN in predicting ACC of SCFST column," Composite Structures, vol. 228, Nov. 2019, Art. no. 111332. DOI: https://doi.org/10.1016/j.compstruct.2019.111332
V.-L. Tran, D.-K. Thai, and D.-D. Nguyen, "Practical artificial neural network tool for predicting the axial compression capacity of circular concrete-filled steel tube columns with ultra-high-strength concrete," Thin-Walled Structures, vol. 151, Jun. 2020, Art. no. 106720. DOI: https://doi.org/10.1016/j.tws.2020.106720
V.-L. Tran and S.-E. Kim, "Efficiency of three advanced data-driven models for predicting axial compression capacity of CFDST columns," Thin-Walled Structures, vol. 152, Jul. 2020, Art. no. 106744. DOI: https://doi.org/10.1016/j.tws.2020.106744
T.-H. Nguyen, N.-L. Tran, and D.-D. Nguyen, "Prediction of Axial Compression Capacity of Cold-Formed Steel Oval Hollow Section Columns Using ANN and ANFIS Models," International Journal of Steel Structures, vol. 22, no. 1, pp. 1–26, Feb. 2022. DOI: https://doi.org/10.1007/s13296-021-00557-z
N.-L. Tran, T.-H. Nguyen, V.-T. Phan, and D.-D. Nguyen, "A Machine Learning-Based Model for Predicting Atmospheric Corrosion Rate of Carbon Steel," Advances in Materials Science and Engineering, vol. 2021, Dec. 2021, Art. no. e6967550. DOI: https://doi.org/10.1155/2021/6967550
T.-H. Nguyen, N.-L. Tran, and D.-D. Nguyen, "Prediction of Critical Buckling Load of Web Tapered I-Section Steel Columns Using Artificial Neural Networks," International Journal of Steel Structures, vol. 21, no. 4, pp. 1159–1181, Aug. 2021. DOI: https://doi.org/10.1007/s13296-021-00498-7
S. Tahzeeb and S. Hasan, "A Neural Network-Based Multi-Label Classifier for Protein Function Prediction," Engineering, Technology & Applied Science Research, vol. 12, no. 1, pp. 7974–7981, Feb. 2022. DOI: https://doi.org/10.48084/etasr.4597
S. A. A. Biabani and N. A. Tayyib, "A Review on the Use of Machine Learning Against the Covid-19 Pandemic," Engineering, Technology & Applied Science Research, vol. 12, no. 1, pp. 8039–8044, Feb. 2022. DOI: https://doi.org/10.48084/etasr.4628
K. Aldriwish, "A Deep Learning Approach for Malware and Software Piracy Threat Detection," Engineering, Technology & Applied Science Research, vol. 11, no. 6, pp. 7757–7762, Dec. 2021. DOI: https://doi.org/10.48084/etasr.4412
S. Mangalathu, G. Heo, and J.-S. Jeon, "Artificial neural network based multi-dimensional fragility development of skewed concrete bridge classes," Engineering Structures, vol. 162, pp. 166–176, May 2018. DOI: https://doi.org/10.1016/j.engstruct.2018.01.053
S.-H. Hwang, S. Mangalathu, J. Shin, and J.-S. Jeon, "Machine learning-based approaches for seismic demand and collapse of ductile reinforced concrete building frames," Journal of Building Engineering, vol. 34, Feb. 2021, Art. no. 101905. DOI: https://doi.org/10.1016/j.jobe.2020.101905
V.-L. Tran and S.-E. Kim, "A practical ANN model for predicting the PSS of two-way reinforced concrete slabs," Engineering with Computers, vol. 37, no. 3, pp. 2303–2327, Jul. 2021. DOI: https://doi.org/10.1007/s00366-020-00944-w
H. Naderpour, M. Mirrashid, and P. Parsa, "Failure mode prediction of reinforced concrete columns using machine learning methods," Engineering Structures, vol. 248, Dec. 2021, Art. no. 113263. DOI: https://doi.org/10.1016/j.engstruct.2021.113263
D.-D. Nguyen, V.-L. Tran, D.-H. Ha, V.-Q. Nguyen, and T.-H. Lee, "A machine learning-based formulation for predicting shear capacity of squat flanged RC walls," Structures, vol. 29, pp. 1734–1747, Feb. 2021. DOI: https://doi.org/10.1016/j.istruc.2020.12.054
D.-K. Thai, T. M. Tu, T. Q. Bui, and T.-T. Bui, "Gradient tree boosting machine learning on predicting the failure modes of the RC panels under impact loads," Engineering with Computers, vol. 37, no. 1, pp. 597–608, Jan. 2021. DOI: https://doi.org/10.1007/s00366-019-00842-w
S. Mangalathu and J.-S. Jeon, "Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques," Engineering Structures, vol. 160, pp. 85–94, Apr. 2018. DOI: https://doi.org/10.1016/j.engstruct.2018.01.008
S. Mangalathu, H. Jang, S.-H. Hwang, and J.-S. Jeon, "Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls," Engineering Structures, vol. 208, Apr. 2020, Art. no. 110331. DOI: https://doi.org/10.1016/j.engstruct.2020.110331
M. R. Azadi Kakavand, H. Sezen, and E. Taciroglu, "Data-Driven Models for Predicting the Shear Strength of Rectangular and Circular Reinforced Concrete Columns," Journal of Structural Engineering, vol. 147, no. 1, Jan. 2021, Art. no. 04020301. DOI: https://doi.org/10.1061/(ASCE)ST.1943-541X.0002875
W. Ghannoum et al., "NEES: ACI 369 Rectangular Column Database," 2015, https://datacenterhub.org/resources/255.
ACI 318-(2014), Building Code Requirements for Structural Concrete and Commentary. Farmington Hills, MI, USA: ACI Concrete, 2014.
M. Ascheim and J. P. Moehle, "Shear Strength and Deformability of RC Bridge Columns Subjected to Inelastic Cyclic Displacements," University of California, Berkeley, CA, USA, UCB/EERC-92/04, Mar. 1992. Accessed: Aug. 31, 2022.
D. E. Biskinis, G. K. Roupakias, and M. N. Fardis, "Degradation of Shear Strength of Reinforced Concrete Members with Inelastic Cyclic Displacements," Structural Journal, vol. 101, no. 6, pp. 773–783, Nov. 2004. DOI: https://doi.org/10.14359/13452
J. P. Moehle, K. J. Elwood, and H. Sezen, "Gravity Load Collapse of Building Frame During Earthquakes," Special Publication, vol. 197, pp. 215–238, Apr. 2002.
M. J. N. Priestley, R. Verma, and Y. Xiao, "Seismic Shear Strength of Reinforced Concrete Columns," Journal of Structural Engineering, vol. 120, no. 8, pp. 2310–2329, Aug. 1994. DOI: https://doi.org/10.1061/(ASCE)0733-9445(1994)120:8(2310)
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