Predicting shear capacity of rectangular hollow RC columns using neural networks

Asian Journal of Civil Engineering - Trang 1-12 - 2023
Xuan-Bang Nguyen1, Viet-Linh Tran2, Huy-Thien Phan2, Duy-Duan Nguyen2
1Institute of Techniques for Special Engineering, Le Quy Don Technical University, Hanoi, Vietnam
2Department of Civil Engineering, Vinh University, Vinh, Vietnam

Tóm tắt

This study predicts the shear strength of rectangular hollow reinforced concrete (RC) columns using artificial neural network (ANN). A total of 120 experimental results are collected from literature and used for establishing the machine learning model. The results reveal that the proposed ANN model predicts the shear strength of rectangular hollow RC columns accurately with $${R}^{2}$$ of 0.99. Additionally, the relative importance of input parameters on the calculated shear strength of RC columns is evaluated using Shapley value. Based on the ANN model, a graphical user interface tool is also developed and readily used in predicting the shear strength of rectangular hollow RC columns.

Tài liệu tham khảo

ACI-318–14 (2014). Building code requirements for structural concrete (ACI 318–14) and commentary. American Concrete Institute.

ASCE/SEI-41-06. (2007). Seismic rehabilitation of existing buildings (ASCE/SEI 41–06). American Society of Civil Engineers, Reston, VA.

Ascheim, M., & Moehle, J. (1992). Shear strength and deformability of RC bridge columns subjected to inelastic cyclic displacements.

Asteris, P.G., & Mokos, V.G. (2019). Concrete compressive strength using artificial neural networks. Neural Computing and Applications, 32(15), 11807–11826.

Biskinis, D. E., Roupakias, G. K., & Fardis, M. N. (2004). Degradation of shear strength of reinforced concrete members with inelastic cyclic displacements. Structural Journal, 101, 773–783.

Caglar, N., Pala, M., Elmas, M., & Eryılmaz, D. M. (2009). A new approach to determine the base shear of steel frame structures. Journal of Constructional Steel Research, 65, 188–195.

Calvi, G. M., Pavese, A., Rasulo, A., & Bolognini, D. (2005). Experimental and numerical studies on the seismic response of RC hollow bridge piers. Bulletin of Earthquake Engineering, 3, 267–297.

Cassese, P. (2017). Seismic performance of existing hollow reinforced concrete bridge columns. (Ph. D. Dissertation. Department of Structures for Engineering and …).

Cassese, P., De Risi, M. T., & Verderame, G. M. (2019). A modelling approach for existing shear-critical RC bridge piers with hollow rectangular cross section under lateral loads. Bulletin of Earthquake Engineering, 17, 237–270.

Cassese, P., De Risi, M. T., & Verderame, G. M. (2020). Seismic assessment of existing hollow circular reinforced concrete bridge piers. Journal of Earthquake Engineering, 24, 1566–1601.

Cheng, C.-T., Mo, Y., & Yeh, Y.-K. (2005). Evaluation of as-built, retrofitted, and repaired shear-critical hollow bridge columns under earthquake-type loading. Journal of Bridge Engineering, 10, 520–529.

EN-1998-1. (2004). Eurocode 8: Design of structures for earthquake resistance—Part 1: General rules. Seismic actions and rules for buildings.

Falcone, R., Lima, C., & Martinelli, E. (2020). Soft computing techniques in structural and earthquake engineering: A literature review. Engineering Structures, 207, 110269.

Farfani, H. A., Behnamfar, F., & Fathollahi, A. (2015). Dynamic analysis of soil-structure interaction using the neural networks and the support vector machines. Expert Systems with Applications, 42, 8971–8981.

Faria, R., Pouca, N. V., & Delgado, R. (2004). Simulation of the cyclic behaviour of R/C rectangular hollow section bridge piers via a detailed numerical model. Journal of Earthquake Engineering, 8, 725–748.

Feng, D.-C., Liu, Z.-T., Wang, X.-D., Jiang, Z.-M., & Liang, S.-X. (2020). Failure mode classification and bearing capacity prediction for reinforced concrete columns based on ensemble machine learning algorithm. Advanced Engineering Informatics, 45, 101126.

Gharehbaghi, S., Yazdani, H., & Khatibinia, M. (2019). Estimating inelastic seismic response of reinforced concrete frame structures using a wavelet support vector machine and an artificial neural network. Neural Computing and Applications, 32, 2975–2988.

Ghee, A. B., Priestley, M. N., & Paulay, T. (1989). Seismic shear strength of circular reinforced concrete columns. Structural Journal, 86, 45–59.

Han, Q., Du, X., Zhou, Y., & Lee, G. C. (2013). Experimental study of hollow rectangular bridge column performance under vertical and cyclically bilateral loads. Earthquake Engineering and Engineering Vibration, 12, 433–445.

Kaveh, A., Dadras Eslamlou, A., Javadi, S., & Geran Malek, N. (2021). Machine learning regression approaches for predicting the ultimate buckling load of variable-stiffness composite cylinders. Acta Mechanica, 232, 921–931.

Kaveh, A., Eskandari, A., and Movasat, M. (2023). Buckling resistance prediction of high-strength steel columns using metaheuristic-trained artificial neural networks. (Elsevier), pp. 104853.

Kaveh, A., and Khavaninzadeh, N. (2023). Efficient training of two ANNs using four meta-heuristic algorithms for predicting the FRP strength. (Elsevier), pp. 256–272.

Kim, T.-H. (2019). Analytical seismic performance assessment of hollow reinforced-concrete bridge columns. Magazine of Concrete Research, 71, 719–733.

Kim, T.-H., Kim, I.-H., Lee, J.-H., & Shin, H. M. (2019). Hollow bridge columns with triangular confining reinforcement. Canadian Journal of Civil Engineering, 46, 467–480.

Kim, T.-H., Lee, J.-H., & Shin, H.-M. (2014a). Hollow reinforced concrete bridge column systems with reinforcement details for material quantity reduction: I. Development and verification. Journal of the Earthquake Engineering Society of Korea, 18, 1–8.

Kim, T.-H., Lee, J.-H., & Shin, H. M. (2014b). Performance assessment of hollow RC bridge columns with triangular reinforcement details. Magazine of Concrete Research, 66, 809–824.

Kowalsky, M. J., & Priestley, M. N. (2000). Improved analytical model for shear strength of circular reinforced concrete columns in seismic regions. Structural Journal, 97, 388–396.

Ma, Y., & Gong, J.-X. (2018). Probability identification of seismic failure modes of reinforced concrete columns based on experimental observations. Journal of Earthquake Engineering, 22, 1881–1899.

Mai, S. H., Tran, V.-L., Nguyen, D.-D., Nguyen, V. T., & Thai, D.-K. (2022). Patch loading resistance prediction of steel plate girders using a deep artificial neural network and an interior-point algorith. Steel and Composite Structures, 45, 159.

Mangalathu, S., Hwang, S.-H., & Jeon, J.-S. (2020). Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach. Engineering Structures, 219, 110927.

Mangalathu, S., & Jeon, J.-S. (2019). Machine learning–based failure mode recognition of circular reinforced concrete bridge columns: Comparative study. Journal of Structural Engineering, 145, 04019104.

Mo, Y., Jeng, C.-H., & Perng, S. (2001). Seismic shear behavior of rectangular hollow bridge columns. Structural Engineering and Mechanics, 12, 429–448.

Mo, Y., & Nien, I. (2002). Seismic performance of hollow high-strength concrete bridge columns. Journal of Bridge Engineering, 7, 338–349.

Mo, Y., Wong, D., & Maekawa, K. (2003). Seismic performance of hollow bridge columns. Structural Journal, 100, 337–348.

Morfidis, K., & Kostinakis, K. (2018). Approaches to the rapid seismic damage prediction of r/c buildings using artificial neural networks. Engineering Structures, 165, 120–141.

Nguyen, D.-D., Tran, V.-L., Ha, D.-H., Nguyen, V.-Q., & Lee, T.-H. (2021a). A machine learning-based formulation for predicting shear capacity of squat flanged RC walls (pp. 1734–1747). Elsevier.

Nguyen, T.-H., Tran, N.-L., & Nguyen, D.-D. (2021b). Prediction of axial compression capacity of cold-formed steel oval hollow section columns using ANN and ANFIS Models. International Journal of Steel Structures. https://doi.org/10.1007/s13296-021-00557-z

Pekelnicky, R., Engineers, S., Chris Poland, S., & Engineers, N. (2012). ASCE 41–13: Seismic evaluation and retrofit rehabilitation of existing buildings. In: Proceedings of the SEAOC.

Phan, V.-T., Tran, V.-L., Nguyen, V.-Q., & Nguyen, D.-D. (2022). Machine learning models for predicting shear strength and identifying failure modes of rectangular RC columns. Buildings, 12, 1493.

Pinto, A., Molina, J., & Tsionis, G. (2003). Cyclic tests on large-scale models of existing bridge piers with rectangular hollow cross-section. Earthquake Engineering & Structural Dynamics, 32, 1995–2012.

Priestley, M. N., Verma, R., & Xiao, Y. (1994). Seismic shear strength of reinforced concrete columns. Journal of Structural Engineering, 120, 2310–2329.

Qi, Y.-L., Han, X.-L., & Ji, J. (2013). Failure mode classification of reinforced concrete column using Fisher method. Journal of Central South University, 20, 2863–2869.

Razzaghi, M. S., Safarkhanlou, M., Mosleh, A., & Hosseini, P. (2018). Fragility assessment of RC bridges using numerical analysis and artificial neural networks. Earthquakes and Structures, 15, 431–441.

Sezen, H., & Moehle, J. P. (2004). Shear strength model for lightly reinforced concrete columns. Journal of Structural Engineering, 130, 1692–1703.

Shin, M., Choi, Y. Y., Sun, C.-H., & Kim, I.-H. (2013). Shear strength model for reinforced concrete rectangular hollow columns. Engineering Structures, 56, 958–969.

Sun, Z., Wang, D., Wang, T., Wu, S., & Guo, X. (2019). Investigation on seismic behavior of bridge piers with thin-walled rectangular hollow section using quasi-static cyclic tests. Engineering Structures, 200, 109708.

Tran, N.-L., Nguyen, D.-D., & Nguyen, T.-H. (2022). Prediction of speed limit of cars moving on corroded steel girder bridges using artificial neural networks. Sādhanā, 47, 1–14.

Tran, V.-L., Thai, D.-K., & Nguyen, D.-D. (2020). 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, 151, 106720.

Yang, C., Xie, L., & Li, A. (2019). Full-scale experimental and numerical investigations on seismic performance of square RC frame columns with hollow sections. Journal of Earthquake Engineering, 26(1), 427–448.

Yeh, Y.-K., Mo, Y., & Yang, C. (2002a). Full-scale tests on rectangular hollow bridge piers. Materials and Structures, 35, 117–125.

Yeh, Y.-K., Mo, Y. L., & Yang, C. (2002b). Seismic performance of rectangular hollow bridge columns. Journal of Structural Engineering, 128, 60–68.

Zhu, L., Elwood, K., & Haukaas, T. (2007). Classification and seismic safety evaluation of existing reinforced concrete columns. Journal of Structural Engineering, 133, 1316–1330.

Thông tin
Thông tin xuất bản

Asian Journal of Civil Engineering

1-12

Thông tin tác giả