Abstract
Concrete-filled steel tube columns (CFSTCs) are preferred due to enhanced ductility and energy absorption. The capability of an artificial neural network (ANN) based analytical model on estimating the ultimate load capacity of circular CFSTCs under axial loadings has been investigated in this study. To provide a better prediction in modeling, 150 comprehensive experimental data were obtained from circular CFSTC’s geometrical and mechanical properties, such as height, diameter, thickness, the yield stress of steel, unconfined concrete strength, Young’s modulus of steel and concrete, etc., were examined. The backpropagation-training practice available in ANN was used to update the weights of each layer based on the network output error. For feedforward–backpropagation, the Levenberg–Marquardt algorithm was employed. The effectiveness of the ANN model was developed using general-purpose software MATLAB® by training and predicting the ultimate load capacity of circular CFSTCs. Finally, about 75% of the data were used for ANN training, and the remaining 25% was used for testing the ANN model. The results show that the predicted values of ultimate load capacity using the ANN model agree well with that of the corresponding experimental observations, and the percentage difference is about ± 10%. Additionally, a new engineering index, a20-index, was predicted to further verify the reliability of the model. The findings of this article are new and will significantly contribute to the existing technology of ANN-based modeling in composite construction.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability and Materials
Some or all data, models, or code generated or used during the study are available from the corresponding author by request.
Abbreviations
- \({{D}}\) :
-
Outer diameter of steel tube
- \({{{d}}}_{{{k}}}\) :
-
Direction vector
- \({{E}}\) :
-
Output error function
- \({{{E}}}_{{{c}}}\) :
-
Young’s modulus of concrete
- \({{{E}}}_{{{s}}}\) :
-
Young’s modulus of steel,
- \({{{f}}}_{{{c}}}\) :
-
Unconfined concrete compressive strength
- \({{{f}}}_{{{y}}}\) :
-
Yield strength of structural steel
- \({{g}}\) :
-
Nonlinear activation function
- \({{L}}\) :
-
Length of CFSTC
- \({{m}}20\) :
-
No. of samples whose value of the ratio experimental to predicted lies between \(0.8\) and \(1.2\)
- \({{N}}\) :
-
No. of dataset sample
- \({{{P}}}_{{{u}}}\) :
-
Ultimate axial load capacity
- \({{{P}}}_{{{u}}}^{{{E}}}\) :
-
Experimental ultimate load
- \({{{P}}}_{{{u}}}^{{{A}}{{N}}{{N}}}\) :
-
Predicted ultimate load in the ANN-based model
- \({{{P}}}_{{{u}}}^{{{M}}{{A}}{{R}}{{S}}}\) :
-
Predicted ultimate load in the MARS-based model
- \({{{P}}}_{{{u}}}^{{{R}}{{V}}{{M}}}\) :
-
Predicted ultimate load in the RVM-based model
- \({{{R}}}^{2}\) :
-
Coefficient of determination R-squared
- \({{t}}\) :
-
Wall thickness of steel tube
- \({{{w}}}_{{{k}}}\) :
-
Individual weight at epoch \({{k}}\)
- \({{{x}}}_{{{i}}}^{{{a}}}\boldsymbol{ }{{{i}}}^{{{t}}{{h}}}\) :
-
Component of the input vector before normalization
- \({{{x}}}_{{{i}}}^{{{n}}}\boldsymbol{ }{{{i}}}^{{{t}}{{h}}}\) :
-
Component of the input vector after normalization
- \({{{x}}}_{{{i}}}^{{{m}}{{a}}{{x}}}\) :
-
Maximum value of all the components of the input vector before the normalization
- \({{{x}}}_{{{i}}}^{{{m}}{{i}}{{n}}}\) :
-
Minimum value of all the components of the input vector before the normalization
- \({{\eta}}\) :
-
Learning rate
- \({{\xi}}\) :
-
Confinement factor
References
Abdelkarim, O.I., Gheni, A., Anumolu, S., Wang, S., & Elgawady, M. (2015). Hollow-core FRP-concrete-steel bridge columns under extreme loading. Report No. cmr15-008, Missouri Department of Transportation Research, Development and Technology, Missouri University of Science and Technology.
AISC 360‒16. (2016). ANSI/AISC 360‒16 Specification for Structural Steel Buildings (pp. 676). Chicago, Illinois, USA. https://www.aisc.org/Specification-for-Structural-Steel-Buildings-ANSIAISC-360-16-1.
Akbas, B., Shen, J., & Sabol, T. A. (2011). Estimation of seismic-induced demands on column splices with a neural network model. Applied Soft Computing, 11(8), 4820–4829. https://doi.org/10.1016/j.asoc.2011.06.019
Al-eliwi, B., Ekmekyapar, T., & Al-juboori, H. A. (2017). Comparison of AISC 360–16 and EC4 for the prediction of composite column capacity. The International Journal of Energy and Engineering Sciences (IJEES), 2(2), 3–22.
Aslani, F., Uy, B., Tao, Z., & Mashiri, F. (2015). Behaviour and design composite columns incorporating compact high-strength steel plates. Journal of Constructional Steel Research, 107, 94–110. https://doi.org/10.1016/j.jcsr.2015.01.005
Asteris, P. G., & Mokos, V. G. (2020). Concrete compressive strength using artificial neural networks. Neural Computing and Applications, 32, 11807–11826. https://doi.org/10.1007/s00521-019-04663-2
Avci-Karatas, C. (2021). Modeling approach for estimation of ultimate load capacity of concrete-filled steel tube composite stub columns based on relevance vector machine. Nigde Omer Halisdemir University Journal of Engineering Sciences. https://doi.org/10.28948/ngumuh.759297
Avci-Karatas, C. (2019). Prediction of ultimate load capacity of concrete-filled steel tube columns using multivariate adaptive regression splines (MARS). Steel Compos Struct, 33(4), 583–594. https://doi.org/10.12989/scs.2019.33.4.583
Avci-Karatas, C., Celik, O. C., & Eruslu, S. O. (2019). Modeling of buckling restrained braces (BRBs) using full-scale experimental data. KSCE Journal of Civil Engineering, 23(10), 4431–4444. https://doi.org/10.1007/s12205-019-2430-y
Avci-Karatas, C., Celik, O. C., & Yalcin, C. (2018). Experimental investigation of aluminum alloy and steel core buckling restrained braces (BRBs). International Journal of Steel Structures, 18(2), 650–673. https://doi.org/10.1007/s13296-018-0025-y
Beale, M. H., Hagan, M. T., & Demuth, H. B. (2010). Neural network toolbox 7 user’s guide. Version R2011b, The MathWorks, Inc. 3 Apple Hill Drive Natick.
Behnood, A., & Golafshani, E. M. (2018). Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves. Journal of Cleaner Production, 202(20), 54–64. https://doi.org/10.1016/j.jclepro.2018.08.065
Bianconi, A., Von Zuben, C. J., Serapião, A. B., & Govone, J. S. (2010). Artificial neural networks: A novel approach to analysing the nutritional ecology of a blowfly species, Chrysomya Megacephala. Journal of Insect Science, 10(58), 1536–2442. https://doi.org/10.1673/031.010.5801
Celik, O. C., Yuksel, E., Avci-Karatas, C., Bal, A., Gokce, T., Bago, Z., & Koller, G. (2015). Component testing of steel-core buckling restrained braces (BRBs) with pinned end connections. In Proceedings of the 8th International Conference on Advances in Steel Structures (ICASS-2015).
Chalioris, C. E., Zapris, A. G., & Karayannis, C. G. (2020). U-jacketing applications of fiber-reinforced polymers in reinforced concrete T-beams against shear—tests and design. Fibers, 8(2), 13. https://doi.org/10.3390/fib8020013
Cheng, M. Y., & Cao, M. T. (2013). Evolutionary multivariate adaptive regression splines for estimating shear strength in reinforced-concrete deep beams. Engineering Applications of Artificial Intelligence, 28, 86–96. https://doi.org/10.1016/j.engappai.2013.11.001
Chithra, S., Kumar, S. R. R. S., Chinnaraju, K., & Alfin-Ashmita, F. (2016). A comparative study on the compressive strength prediction models for high performance concrete containing nano silica and copper slag using regression analysis and artificial neural networks. Construction and Building Materials, 114, 528–535. https://doi.org/10.1016/j.conbuildmat.2016.03.214
Demuth, H.B., Beale, M. H., & Hagan, M. T. (2006). Neural network toolbox 5 user’s guide. Version 5, The MathWorks, Inc. 3 Apple Hill Drive Natick.
De Oliveira, W. L. A., De Nardin, S., de Cresce El, A. L. H., & El Debs, M. K. (2009). Influence of concrete strength and length/diameter on the axial capacity of CFT columns. Journal of Constructional Steel Research, 65(12), 2103–2110. https://doi.org/10.1016/j.jcsr.2009.07.004
Duan, Z. H., Kou, S. C., & Poon, C. S. (2013). Prediction of compressive strength of recycled aggregate concrete using artificial neural networks. Construction and Building Materials, 40, 1200–1206. https://doi.org/10.1016/j.conbuildmat.2012.04.063
Dutta, S., Murthy, A. R., Kim, D., & Samui, P. (2017). Prediction of compressive strength of self-compacting concrete using intelligent computational modelling. Comput Mater Contin, 53(2), 157–174. https://doi.org/10.3970/cmc.2017.053.167
Eurocode 4 (EC4): EN 1994-1-1. (2004). (English) Design of composite steel and concrete structure—Part 1–1: General rules and rules for buildings (pp. 117). CEN, Brussels: European Committee for Standardization. Authority: The European Union per Regulation 305/2011, Directive 98/34/EC, Directive 2004/18/EC]. https://eurocodes.jrc.ec.europa.eu/showpage.php?id=134.
Ekmekyapar, T., & Al-Eliwi, B. J. M. (2016). Experimental behaviour of circular concrete filled steel tube columns and design specifications. Thin-Walled Structures, 105, 220–230. https://doi.org/10.1016/j.tws.2016.04.004
Ellobody, E., Young, B., & Lam, D. (2006). Behavior of normal and high strength concrete-filled compact steel tube circular stub columns. Journal of Constructional Steel Research, 62(7), 706–715. https://doi.org/10.1016/j.jcsr.2005.11.002
Gallant, S .(1987). Automated generation of connectionist expert systems for problems involving noise and redundancy. In Proceedings of the 3rd Conference on Uncertainty in Artificial Intelligence (UAI’87) (pp. 212–221). ISBN: 978-0-444-87417-7.
Gardener, N. J., & Jacobson, R. (1967). Structural behavior of concrete filled steel tubes. Journal of American Concrete Institute (ACI), 64(7), 404–413.
Gardener, N. J. (1968). Use of spiral welded steel tubes in pipe columns. Journal of American Concrete Institute (ACI), 65(11), 937–942.
Gavin, H. P. (2013). The Levenberg-Marquardt algorithm for nonlinear least squares curve-fitting problems. Department of Civil and Environmental Engineering, Duke University.
Giakoumelis, G., & Lam, D. (2004). Axial capacity of circular concrete-filled tube columns. Journal of Constructional Steel Research, 60(7), 1049–1068. https://doi.org/10.1016/j.jcsr.2003.10.001
Ghamari, A., & Johari Naeimi, A. (2020). Investigating the seismic behaviour of high-performance steel plate shear walls. Proceedings of the Institution of Civil Engineers-Structures and Buildings. https://doi.org/10.1680/jstbu.20.00108
Gholampour, A., Mansouri, I., Kisi, O., & Ozbakkaloglu, T. (2020). Evaluation of mechanical properties of concretes containing coarse recycled concrete aggregates using multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), and least squares support vector regression (LSSVR) models. Neural Computing and Applications, 32, 295–308. https://doi.org/10.1007/s00521-018-3630-y
Guler, S., Copur, A., & Aydogan, M. (2013). Axial capacity and ductility of circular UHPC-filled steel tube columns. Mag Concrete Res, 65(15), 898–905. https://doi.org/10.1680/macr.12.00211
Guler, S., Copur, A., & Aydogan, M. (2014). A comparative study on square and circular high strength concrete-filled steel tube columns. Advanced Steel Construction, 10(2), 234–247. https://doi.org/10.18057/IJASC.2014.10.2.7
Gupta, P. K., Sarda, S. M., & Kumar, M. S. (2007). Experimental and computational study of concrete filled steel tubular columns under axial loads. Journal of Constructional Steel Research, 63(2), 182–193. https://doi.org/10.1016/j.jcsr.2006.04.004
Han, L. H., & Yao, G. H. (2004). Experimental behaviour of thin-walled hollow structural steel (HSS) columns filled with self-consolidating concrete (SCC). Thin-Walled Structures, 42(9), 1357–1377. https://doi.org/10.1016/j.tws.2004.03.016
Han, L. H., Yao, G. H., & Zhao, X. L. (2005). Tests and calculations for hollow structural steel (HSS) stub columns filled with self-consolidating concrete (SCC). Journal of Constructional Steel Research, 61(9), 1241–1269. https://doi.org/10.1016/j.jcsr.2005.01.004
Han, L. H., Hou, C. C., & Wang, Q. L. (2014). Behavior of circular CFST stub columns under sustained load and chloride corrosion. Journal of Constructional Steel Research, 103, 23–36. https://doi.org/10.1016/j.jcsr.2014.07.021
Haque, M. E., & Sudhakar, K. V. (2002). ANN back-propagation prediction model for fracture toughness in microalloy steel. International Journal of Fatigue, 24(9), 1003–1010. https://doi.org/10.1016/S0142-1123(01)00207-9
Haykin, S. (1998). Neural networks: A comprehensive foundation (2nd ed.). Prentice Hall.
Hecht-Nielsen, R. (1989). Neurocomputer Applications. In: Eckmiller R., v.d. Malsburg C. (eds) Neural Computers. Springer Study Edition, vol 41. Springer. https://doi.org/10.1007/978-3-642-83740-1_45
Hoveidae, N., & Radpour, S. (2021). A novel all-steel buckling restrained brace for seismic drift mitigation of steel frames. Bulletin of Earthquake Engineering. https://doi.org/10.1007/s10518-020-01038-0
Huang, C. S., Yeh, Y. K., Liu, G. Y., Hu, H. T., Tsai, K. C., Weng, Y. T., Wang, S. H., & Wu, M. H. (2002). Axial load behavior of stiffened concrete-filled steel columns. Journal of Structural Engineering (ASCE), 128(9), 1222–1230. https://doi.org/10.1061/(ASCE)0733-9445(2002)128:9(1222)
Ince, R. (2004). Prediction of fracture parameters of concrete by artificial neural networks. Engineering Fracture Mechanics, 71(15), 2143–2159. https://doi.org/10.1016/j.engfracmech.2003.12.004
Johansson, M., & Gylltoft, K. (2002). Mechanical behavior of circular steel–concrete composite stub columns. Journal of Structural Engineering (ASCE), 128(8), 1073–1081. https://doi.org/10.1061/(ASCE)0733-9445(2002)128:8(1073)
Kamarthi, S. V., & Pittner, S. (1999). Accelerating neural network training using weight extrapolations. Neural Networks, 12(9), 1285–1299. https://doi.org/10.1016/S0893-6080(99)00072-6
Kaur, J., & Kaur, K. (2017). A fuzzy approach for an IoT-based automated employee performance appraisal. Comput Mater Contin, 53(1), 23–36.
Kitada, T. (1998). Ultimate strength and ductility of state-of-the-art concrete-filled steel bridge piers in Japan. Engineering Structures, 20(4–6), 347–354. https://doi.org/10.1016/S0141-0296(97)00026-6
Lam, D., & Gardner, L. (2008). Structural design of stainless steel concrete filled columns. Journal of Constructional Steel Research, 64(11), 1275–1282. https://doi.org/10.1016/j.jcsr.2008.04.012
Le, T. T. (2022). Practical machine learning-based prediction model for axial capacity of square CFST columns. Mechanics of Advanced Materials and Structures. https://doi.org/10.1080/15376494.2020.1839608
Lee, S. H., Uy, B., Kim, S. H., Choi, Y. H., & Choi, S. M. (2011). Behavior of highstrength circular concrete-filled steel tubular (CFST) column under eccentric loading. Journal of Constructional Steel Research, 67, 1–13. https://doi.org/10.1016/j.jcsr.2010.07.003
Levenberg, K. (1944). A method for the solution of certain problems in least squares. Quarterly of Applied Mathematics, 2(2), 164–168.
Liu, D. L., Gho, W. M., & Yuan, J. (2003). Ultimate capacity of high-strength rectangular concrete-filled steel hollow section stub columns. Journal of Constructional Steel Research, 59(12), 1499–1515. https://doi.org/10.1016/S0143-974X(03)00106-8
Liu, D. L., & Gho, W. M. (2005). Axial load behaviour of high-strength rectangular concrete filled steel tubular stub columns. Thin-Walled Structures, 43(8), 1131–1142. https://doi.org/10.1016/j.tws.2005.03.007
Lue, D. M., Liu, J. L., & Yen, T. (2007). Experimental study on rectangular CFT columns with high-strength concrete. Journal of Constructional Steel Research, 63(1), 37–44. https://doi.org/10.1016/j.jcsr.2006.03.007
Madsen, K., Nielsen, HB., & Tingleff, O. (2004). Methods for non-linear least squares problems. Informatics and Mathematical Modelling (IMM). Technical University of Denmark, Lecture notes. Available at http://www.imm.dtu.dk/courses/02611/nllsq.pdf.
Mansouri, I., Kisi, O., Sadeghian, P., Lee, C. H., & Hu, J. W. (2017). Prediction of ultimate strain and strength of FRP-confined concrete cylinders using soft computing methods. Applied Sciences, 7(8), 751. https://doi.org/10.3390/app7080751
Mansouri, I., Safa, M., Ibrahim, Z., Kisi, O., Tahir, M. M., Baharom, S. B., & Azimi, M. (2016). Strength prediction of rotary brace damper using MLR and MARS. Structural Engineering and Mechanics, 60(3), 471–488. https://doi.org/10.12989/sem.2016.60.3.471
Marquardt, D. W. (1963). An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics, 11(2), 431–441. https://doi.org/10.1137/0111030
Mittelmann, H. D. (2004). The Least Squares Problem. [web page] http://plato.asu.edu/topics/problems/nlolsq.html, Jul. 2004.
Murthy, A. R., Vishnuvardhan, S., Saravanan, M., & Gandhi, P. (2019). Relevance vector based approach for the prediction of stress intensity factor for the pipe with circumferential crack under cyclic loading. Struct Eng Mech, 72(1), 31–41. https://doi.org/10.12989/sem.2019.72.1.031
Ngo, N. T., Le, H. A., & Pham, T. P. T. (2021). Integration of support vector regression and grey wolf optimization for estimating the ultimate bearing capacity in concrete-filled steel tube columns. Neural Computing and Applications. https://doi.org/10.1007/s00521-020-05605-z
Osborne, M. R. (1992). Fisher’s method of scoring. Int. Stat. Rev., 86, 271–286. https://doi.org/10.2307/1403504
O'Shea, M. D, Bridge, R. Q. (1994). Tests on thin-walled concrete-filled steel tubes. In Proceedings of the 12th International Specialty Conference on Cold-Formed Steel Strucures (pp. 399–419).
O’Shea, M. D., & Bridge, R. Q. (1998). Tests on circular thin-walled steel tubes filled with medium and high strength concrete. Australian Civ. Eng. Trans., 40, 15–27.
O’Shea, M. D., & Bridge, R. Q. (2000). Design of circular thin-walled concrete filled steel tubes. J Struct Eng (ASCE)., 126(11), 1295–1303. https://doi.org/10.1061/(ASCE)0733-9445(2000)126:11(1295)
Prasanna, P. K., Murthy, A. R., & Srinivas, K. (2018). Prediction of compressive strength of GGBS based concrete using RVM. Struct Eng Mech, 68(6), 691–700. https://doi.org/10.12989/sem.2018.68.6.691
Roshan, P. R. (2007). Predicting bid prices in construction projects using non-parametric statistical models. Master of Science in Civil Engineering Thesis, A&M University.
Sakino, K., & Hayashi, H. (1991). Behavior of concrete filled steel tubular stub columns under concentric loading. In Proceedings of the 3rd International Conference on Steel Concrete Composite Structures (pp. 25–30).
Sakino. K., & Sun, Y. (2000). Steel jacketing for improvement of column strength and ductility. In Proceedings of the 12th World Conference on Earthquake Engineering.
Sakino, K., Nakahara, H., Morino, S., & Nishiyama, I. (2004). Behavior of centrally loaded concrete-filled steel-tube short columns. Journal of Structural Engineering (ASCE), 130(2), 180–188. https://doi.org/10.1061/(ASCE)0733-9445(2004)130:2(180)
Schneider, S. P. (1998). Axially loaded concrete-filled steel tubes. Journal of Structural Engineering (ASCE), 124(10), 1125–1138. https://doi.org/10.1061/(ASCE)0733-9445(1998)124:10(1125)
Soman, M., & Chandrakumar, C. R. (2018). Axial behaviour of glass fibre reinforced polymer-confined reinforced concrete short columns. Structural Engineering International, 28(1), 44–50. https://doi.org/10.1080/10168664.2018.1431422
Talyan, N., Elias, S., & Matsagar, V. (2021). Earthquake response control of isolated bridges using supplementary passive dampers. Practice Periodical on Structural Design and Construction, 26(2), 04021002. https://doi.org/10.1061/(ASCE)SC.1943-5576.0000563
Tan, K. F., Pu, X. C., & Cai, S. H. (1999). Study on the mechanical properties of steel extra-high strength concrete encased in steel tubes. Journal of Building Structures, 20(1), 10–15.
Tomii, M., Yoshimura, K., & Morishita, Y. (1977). Experimental studies on concrete filled steel tubular stub columns under concentric loading. In Proceedings of the International Colloquium on Stability of Structures Under Static and Dynamic Loads (pp. 718–741).
Uy, B. (2001). Strength of short concrete filled high strength steel box columns. Journal of Constructional Steel Research, 57(2), 113–134. https://doi.org/10.1016/S0143-974X(00)00014-6
Xiong, M. X., Xiong, D. X., & Liew, J. Y. R. (2017). Axial performance of short concrete filled steel tubes with high- and ultra-high- strength materials. Engineering Structures, 136, 494–510. https://doi.org/10.1016/j.engstruct.2017.01.037
Yamamoto, T., Kawaguchi, J., & Morino, S. (2002). Experimental study of scale effects on the compressive behavior of short concrete-filled steel tube columns. In Proceedings of the United Engineering Foundation Conference on Composite Construction in Steel and Concrete IV (AICE) (pp. 879–890).
Yu, Z. W., Ding, F. X., & Cai, C. S. (2007). Experimental behavior of circular concretefilled steel tube stub columns. Journal of Constructional Steel Research, 63, 165–174. https://doi.org/10.1016/j.jcsr.2006.03.009
Yu, Q., Tao, Z., & Wu, Y. X. (2008). Experimental behaviour of high performance concrete filled steel tubular columns. Thin Walled Structures, 46(4), 362–370. https://doi.org/10.1016/j.tws.2007.10.001
Yuvaraj, P., Murthy, A. R., Iyer, N. R., Samui, P., & Sekar, S. K. (2013). Multivariate adaptive regression splines model to predict fracture characteristics of high strength and ultra high strength concrete beams. CMC: Computers, Materials and Continua, 36(1), 73–97. https://doi.org/10.3970/cmc.2013.036.073
Yuvaraj, P., Murthy, A. R., Iyer, N. R., Samui, P., & Sekar, S. K. (2014). Prediction of fracture characteristics of high strength and ultra high strength concrete beams based on relevance vector machine. International Journal of Damage Mechanics, 23(7), 979–1004. https://doi.org/10.1177/1056789514520796
Funding
No funding was received to assist with the preparation of this article.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The author has no conflicts of interest to declare that are relevant to the content of this article.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Avci-Karatas, C. Artificial Neural Network (ANN) Based Prediction of Ultimate Axial Load Capacity of Concrete-Filled Steel Tube Columns (CFSTCs). Int J Steel Struct 22, 1341–1358 (2022). https://doi.org/10.1007/s13296-022-00645-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13296-022-00645-8