Connection Design of Precast Concrete Structures Using Machine Learning Techniques

Document Type : Regular Article

Authors

1 Ph.D. Student, Faculty of Civil Engineering, National Institute of Technology Kurukshetra, Haryana, India

2 Associate Professor, Faculty of Civil Engineering, National Institute of Technology Kurukshetra, Haryana, India

3 Assistant Professor, Faculty of Civil Engineering, National Institute of Technology Kurukshetra, Haryana, India

Abstract

In this research, the number of dowels (horizontal connection) has been determined using support vector machines (SVM), gradient boosting and artificial neural networks (ANN-Multilayer perceptron). Building height, length and thickness of the wall, maximum shear, maximum compressive force and maximum tension were the input parameters while the output parameter was the number of dowels. 1140 machine learning models were used, out of which 814 were used as training datasets and 326 as test datasets. A coefficient of correlation of 0.9264, root mean square error of 0.3677 and scattering Index of 4.75 % was achieved by SVM radial basis kernel function (SVM-RBF) as compared to a coefficient of correlation of 0.9232, root mean square error of 0.3743 and scattering Index of 4.83 % by resilient ANN-Multilayer perceptron, suggesting that SVM-RBF is more accurate in estimating the number of dowels. The study's encouraging findings highlight the need for additional research into the use of machine learning in civil engineering.

Keywords

Main Subjects

References

[1]     Benjamin JR. Variability analysis of shear wall structures. Earthq Eng Res 1968;2:45–52.
[2]     Bozdogan KB, Ozturk D. Vibration analysis of asymmetric shear wall structures using the transfer matrix method. Iran J Sci Technol - Trans Civ Eng 2012;34:1–14.
[3]     Xiaolei H, Xuewei C, Cheang J, Guiniu M, Peifeng W. Numerical analysis of cyclic loading test of shear walls based on open SEES. Proc 14th WCEE, China 2008.
[4]     Carpinteri A, Corrado M, Lacidogna G, Cammarano S. Lateral load effects on tall shear wall structures of different height. Struct Eng Mech 2012;41:313–37. https://doi.org/10.12989/sem.2012.41.3.313.
[5]     Biswas JK. Three Dimensional Analysis of Shear Wall Multi Storey Buildings. McMaster University, 1974.
[6]     Fahjan YM, Kubin J, Tan MT. Nonlinear analysis methods for reinforced concrete buildings with shear walls. 14th Eur. Conf. Earthq. Eng., vol. 5, 2010, p. 3380–7.
[7]     R. V, P. JK. Experimental Study on Two Simple Mechanical Precast Beam-Column Connections under Reverse Cyclic Loading. J Perform Constr Facil 2013;27:402–14. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000324.
[8]     Maya LF, Zanuy C, Albajar L, Lopez C, Portabella J. Experimental assessment of connections for precast concrete frames using ultra high performance fibre reinforced concrete. Constr Build Mater 2013;48:173–86. https://doi.org/10.1016/j.conbuildmat.2013.07.002.
[9]     Shariatmadar H, Zamani Beydokhti E. An investigation of seismic response of precast concrete beam to column connections: Experimental study. Asian J Civ Eng 2014;15:849–67.
[10]   Zoubek B, Fischinger M, Isakovic T. Estimation of the cyclic capacity of beam-to-column dowel connections in precast industrial buildings. Bull Earthq Eng 2015;13:2145–68. https://doi.org/10.1007/s10518-014-9711-0.
[11]   Camnasio E, Kriakopoulos P. Experimental Analysis on the Cyclic Performance of Bolted Precast Connections. In: Hordijk DA, Luković M, editors. High Tech Concr. Where Technol. Eng. Meet, Cham: Springer International Publishing; 2018, p. 1015–22. https://doi.org/10.1007/978-3-319-59471-2_118.
[12]   Provost-Smith DJ, Elsayed M, Nehdi ML. Investigation of grouted dowel connections for precast wall construction. ACI Struct J 2019;116:41–50. https://doi.org/10.14359/51710860.
[13]   Alshaikh IMH, Abadel AA, Alrubaidi M. Precast RC structures’ progressive collapse resistance: Current knowledge and future requirements. Structures 2022;37:338–52. https://doi.org/10.1016/j.istruc.2021.12.086.
[14]   Pal M, Deswal S. Support vector regression based shear strength modelling of deep beams. Comput Struct 2011;89:1430–9. https://doi.org/10.1016/j.compstruc.2011.03.005.
[15]   Asteris PG, Kolovos KG, Douvika MG, Roinos K. Prediction of self-compacting concrete strength using artificial neural networks. Eur J Environ Civ Eng 2016;20:s102–22. https://doi.org/10.1080/19648189.2016.1246693.
[16]   Asteris PG, Plevris V. Anisotropic masonry failure criterion using artificial neural networks. Neural Comput Appl 2017;28:2207–29. https://doi.org/10.1007/s00521-016-2181-3.
[17]   Asteris PG, Kolovos KG. Self-compacting concrete strength prediction using surrogate models. Neural Comput Appl 2019;31:409–24. https://doi.org/10.1007/s00521-017-3007-7.
[18]   Asteris P, Roussis P, Douvika M. Feed-Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials. Sensors 2017;17:1344. https://doi.org/10.3390/s17061344.
[19]   Asteris PG, Nozhati S, Nikoo M, Cavaleri L, Nikoo M. Krill herd algorithm-based neural network in structural seismic reliability evaluation. Mech Adv Mater Struct 2019;26:1146–53. https://doi.org/10.1080/15376494.2018.1430874.
[20]   RStudio RsT. Integrated Development for R. RStudio, Inc., Boston, MA 2015.
[21]   Vapnik V. The nature of statistical learning theory. Springer science & business media; 1999.
[22]   DAHIYA N. Machine learning based modelling for estimation of the fundamental time period of precast concrete structures using computer programming. Stavební Obz - Civ Eng J 2021;30. https://doi.org/10.14311/CEJ.2021.02.0041.
[23]   Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat 2001;29:1189–232.
[24]   Dahiya N, Saini B, Chalak HD. Gradient boosting-based regression modelling for estimating the time period of the irregular precast concrete structural system with cross bracing. J King Saud Univ - Eng Sci 2021. https://doi.org/10.1016/j.jksues.2021.08.004.
[25]   Dahiya N, Saini B, Chalak HD. Deep Neural Network-Based Storey Drift Modelling of Precast Concrete Structures Using RStudio. J Soft Comput Civ Eng 2021;5:88–100. https://doi.org/10.22115/scce.2021.289034.1329.
[26]   CSI. ETABS Integrated Software for Structural Analysis and Design, Version: 18.0.2. 2018.

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History

  • Receive Date: 17 August 2022
  • Revise Date: 25 January 2023
  • Accept Date: 04 April 2023
  • First Publish Date: 04 April 2023

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