Application of machine learning models and GSA method for designing stud connectors
Abstract
The design of stud connectors is aided by determining the relationship between shear strength and the input variables (number, diameter, height, tensile strength and elastic modulus of the studs, and compressive strength and elastic modulus of the concrete) that influence strength. Since strength is nonlinearly related to the influencing variables, which makes the predictions of the relevant empirical equations unreliable, the use of machine learning (ML) models is preferred. The prediction results of eight machine learning models were evaluated, including linear regression (LR1), ridge regression (RR), lasso regression (LR2), back-propagation artificial neural network (BP ANN), genetic algorithm optimized BP ANN (GA-BP ANN), extreme learning machines (ELM), random forests (RF), and support vector machines (SVM). The results show that the GA-BP ANN model is the most accurate model for prediction with a mean absolute percentage error (MAPE) of 6.17% and an R2 of 0.9599. Based on the GA-BP ANN model and the global sensitivity analysis (GSA) method, a new parameter importance analysis method was developed to compare the magnitude of the effect of different input variables on strength. It was found that stud diameter had the greatest effect on shear strength.
Keyword : stud connectors, multiple machine-learning model comparisons, global sensitivity analysis, metrics influencing shear strength
How to Cite
Sun, G., Kang, J., & Shi, J. (2024). Application of machine learning models and GSA method for designing stud connectors. Journal of Civil Engineering and Management, 30(4), 373–390. https://doi.org/10.3846/jcem.2024.21348
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Allahyari, H., Nikbin, I., Rahimi, S., & Heidarpour, A. (2018). A new approach to determine strength of Perfobond rib shear connector in steel-concrete composite structures by employing neural network. Engineering Structures, 157, 235–249. https://doi.org/10.1016/j.engstruct.2017.12.007
American Association of State Highway and Transportation Officials. (2017). AASHTO LRFD bridge design specifications (AASHTO LRFDUS-2017) (8th ed.). Washington, DC, USA.
Barjouei, H. S., Ghorbani, H., Mohamadian, N., Wood, D. A., Davoodi, S., Moghadasi, J., & Saberi, H. (2021). Prediction performance advantages of deep machine learning algorithms for two-phase flow rates through wellhead chokes. Journal of Petroleum Exploration and Production, 11, 1233–1261. https://doi.org/10.1007/s13202-021-01087-4
Bayram, S., & Çıtakoğlu, H. (2023). Modeling monthly reference evapotranspiration process in Turkey: Application of machine learning methods. Environmental Monitoring and Assessment, 195(1), Article 67. https://doi.org/10.1007/s10661-022-10662-z
Bernus, A., Ottlé, C., & Raoult, N. (2021). Variance based sensitivity analysis of FLake lake model for global land surface modeling. Journal of Geophysical Research - Atmospheres, 126, Article e2019JD031928. https://doi.org/10.1029/2019JD031928
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Chahnasir, E. S., Zandi, Y., Shariati, M., Dehghani, E., Toghroli, A., Mohamed, E. T., Shariati, A., Safa, M., Wakil, K., & Khorami, M. (2018). Application of support vector machine with firefly algorithm for investigation of the factors affecting the shear strength of angle shear connectors. Smart Structures and Systems, 22(4), 413–424.
Citakoglu, H., & Demir, V. (2023). Developing numerical equality to regional intensity-duration-frequency curves using evolutionary algorithms and multi-gene genetic programming. Acta Geophysica, 71(1), 469–488. https://doi.org/10.1007/s11600-022-00883-8
Coşkun, Ö., & Citakoglu, H. (2023). Prediction of the standardized precipitation index based on the long short-term memory and empirical mode decomposition-extreme learning machine models: The Case of Sakarya, Türkiye. Physics and Chemistry of the Earth, 131, Article 103418. https://doi.org/10.1016/j.pce.2023.103418
Ding, F., Ni, M., Gong, Y., Yu, Z., Zhou, Z., & Zhou, L. (2014). Experimental study on slip behavior and calculation of shear bearing capacity for shear stud connectors. Journal of Building Structures, 35(9), 98–106 (in Chinese).
Ding, F., Yin, G., Wang, H., Wang, L., & Guo, Q. (2017). Static behavior of stud connectors in bi-direction push-off tests. Thin-Walled Structures, 120, 307–318. https://doi.org/10.1016/j.tws.2017.09.011
Ding, J., Li, Y., Xing, W., Ren, P., & Yuan, C. (2021). Mechanical properties and engineering application of single-span steel-concrete double-sided composite beams. Journal of Building Engineering, 1, Article 102644. https://doi.org/10.1016/j.jobe.2021.102644
European Committee for Standardization. (1994). Eurocode 4: Design of composite steel and concrete structures (EN 1994-1-1). Brussels, Belgium.
Farouk, A. I. B., Zhu, J. S., & Gu, Y. H. (2022). Finite element analysis of the shear performance of box-groove interface of ultra-high-performance concrete (UHPC)-normal strength concrete (NSC) composite girder. Innovative Infrastructure Solutions, 7, Article 212. https://doi.org/10.1007/s41062-022-00815-x
Farouk, A. I. B., Rong, W., & Zhu, J. (2023) Compressive behavior of ultra-high-performance-normal strength concrete (UHPC-NSC) column with the longitudinal grooved contact surface. Journal of Building Engineering, 68, Article 106074. https://doi.org/10.1016/j.jobe.2023.106074
Garzón-Roca, J., Marco, C. O., & Adam, J. M. (2013). Compressive strength of masonry made of clay bricks and cement mortar: Estimation based on Neural Networks and Fuzzy Logic. Engineering Structures, 48, 21–27. https://doi.org/10.1016/j.engstruct.2012.09.029
Ghorbani, H., Wood, D. A., Choubineh, A., Tatar, A., Abarghoyi, P. G., Madani, M., & Mohamadian, N. (2020). Prediction of oil flow rate through an orifice flow meter: Artificial intelligence alternatives compared. Petroleum, 6, 404–419. https://doi.org/10.1016/j.petlm.2018.09.003
Gu, J., Liu, D., Deng, W., & Zhang, J. (2019). Experimental study on the shear resistance of a comb-type perfobond rib shear connector. Journal of Constructional Steel Research, 158, 279–289. https://doi.org/10.1016/j.jcsr.2019.03.032
Guan, C., Duan, Y. Z., Zhai, J. Q., & Han, D. (2019). Hydraulic dynamics in split fuel injection on a common rail system and their artificial neural network prediction. Fuel, 255, Article 115792. https://doi.org/10.1016/j.fuel.2019.115792
Guo, H. Y., Dong, Y., Bastidas-Arteaga, E., & Gu, X. L. (2021). Probabilistic failure analysis, performance assessment, and sensitivity analysis of corroded reinforced concrete structures. Engineering Failure Analysis, 124, Article 105328. https://doi.org/10.1016/j.engfailanal.2021.105328
Hossain, K., Gladson, L., & Anwar, M. (2017). Modeling shear strength of medium-to ultra-high-strength steel fiber-reinforced concrete beams using artificial neural network. Neural Computing & Applications, 28(1), 1119–1130. https://doi.org/1007/s00521-016-2417-2
Hu, Y. Q., Qiu, M. H., Chen, L. L., Zhong, R., & Wang, J. (2021). Experimental and analytical study of the shear strength and stiffness of studs embedded in high strength concrete. Engineering Structures, 236, Article 111792. https://doi.org/10.1016/j.engstruct.2020.111792
Khalaf, J., Majeed, A., Aldlemy, M., Ali, Z., & Yaseen, Z. (2021). Hybridized deep learning model for perfobond rib shear strength connector prediction. Complexity, 2021, Article 6611885. https://doi.org/10.1155/2021/6611885
Kim, K., Han, O., Heo, W., & Kim, S. (2020). Behavior of Y-type perfobond rib shear connection under different cyclic loading conditions. Structures, 26, 562–571. https://doi.org/10.1016/j.istruc.2020.04.053
Luo, Y., Hoki, K., Hayashi, K., & Nakashima, M. (2016). Behavior and strength of headed stud-SFRCC shear connection. I: Experimental study. Journal of Structural Engineering, 142(2), Article 4015112. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001363
Ministry of Housing and Urban-Rural Development of the People’s of China. (2013). Code for design of steel and concrete composite bridges (GB 50917-2013). Beijing, China (in Chinese).
Ministry of Housing and Urban-Rural Development of the People’s of China. (2017). Standard for design of steel structures (GB 50017-2017). Beijing, China (in Chinese).
Özbayrak, A., Ali, M. K., & Çıtakoğlu, H. (2023). Buckling load estimation using multiple linear regression analysis and multigene genetic programming method in cantilever beams with transverse stiffeners. Arabian Journal for Science and Engineering, 48(4), 5347–5370. https://doi.org/10.1007/s13369-022-07445-6
Pianosi, F., Beven, K., Freer, J., Hall, J. W., Rougier, J., Stephenson, D. B., & Wagener, T. (2016). Sensitivity analysis of environmental models: A systematic review with practical workflow. Environmental Modelling & Software, 79, 214–232. https://doi.org/10.1016/j.envsoft.2016.02.008
Safa, M., Shariati, M., Ibrahim, Z., Toghroli, A., & Petkovic, D. (2016). Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam’s shear strength. Steel and Composite Structures, 21(3), 679–688. https://doi.org/10.12989/scs.2016.21.3.679
Sedghi, Y., Zandi, Y., Shariati, M., Ahmadi, E., Azar, V. M., Toghroli, A., Safa, M., Mohamad, E. T., Khorami, M., & Wakil, K. (2018). Application of ANFIS technique on performance of C and L shaped angle shear connectors. Smart Structures and Systems, 22(3), 335–340. https://doi.org/10.12989/sss.2018.22.3.335
Setvati, M. R., & Hicks, S. J. (2022). Machine learning models for predicting resistance of headed studs embedded in concrete. Engineering Structures, 254, Article 113803. https://doi.org/10.1016/j.engstruct.2021.113803
Shim, C. S., Lee, P. G., & Yoon, T. Y. (2004). Static behavior of large stud shear connectors. Engineering Structures, 26(12), 1853–1860. https://doi.org/10.1016/j.engstruct.2004.07.011
Shen, Y. W., Yap, K. S., & Li, X. (2020). A new probabilistic output constrained optimization extreme learning machine. IEEE Access, 8, 28934–28946. https://doi.org/10.1109/ACCESS.2020.2971012
Slater, E., Moni, M., & Alam, M. (2012). Predicting the shear strength of steel fiber reinforced concrete beams. Construction and Building Materials, 26(1), 423–436. https://doi.org/10.1016/j.conbuildmat.2011.06.042
Soroush, Z., Brian, T., & Abdollah, S. (2020). Significant variables affecting the performance of concrete panels impacted by wind-borne projectiles: A global sensitivity analysis. International Journal of Impact Engineering, 144, Article 03650. https://doi.org/10.1016/j.ijimpeng.2020.103650
Sobol, I. M. (1993). Sensitivity estimates for nonlinear mathematical models. Mathematical and Computer Modelling, 1(4), 407–414.
Tm, A., Jd, C., & Bra, B. (2019). Shear resistance of headed shear studs welded on welded plates in composite floors. Engineering Structures, 197, Article 109412. https://doi.org/10.1016/j.engstruct.2019.109412
Tzuc, O. M., Gamboa, O. R., Rosel, R. A., Poot, M. C., Edelman, H., Torres, M. J., & Bassam, A. (2021). Modeling of hygrothermal behavior for green facade’s concrete wall exposed to nordic climate using artificial intelligence and global sensitivity analysis. Journal of Building Engineering, 33, Article 101625. https://doi.org/10.1016/j.jobe.2020.101625
Vigneri, V., Odenbreit, C., & Romero-Gu, Z. A. (2021). Numerical study on design rules for minimum degree of shear connection in propped steel-concrete composite beams. Engineering Structures, 241(4), Article 112466. https://doi.org/10.1016/j.engstruct.2021.112466
Wang, Q., & Liu, Y. (2013). Experimental study of shear capacity of stud connector. Journal of Tongji University (Natural Science), 41(5), 659–663 (in Chinese).
Wang, J., Guo, J., Jia, L., Chen, S., & Dong, Y. (2017). Push-out tests of demountable headed stud shear connectors in steel-UHPC composite structures. Composite Structures, 170, 69–79. https://doi.org/10.1016/j.compstruct.2017.03.004
Wang, J., Qi, J., Tong, T., Xu, Q., & Xiu, H. (2019). Static behavior of large stud shear connectors in steel-UHPC composite structures. Engineering Structures, 178, 534–542. https://doi.org/10.1016/j.engstruct.2018.07.058
Wang, J., Zhang, A., & Wang, W. (2020). Effects of stud height on shear behavior of stud connectors. Journal of Zhejiang University (Engineering Science), 54(11), 2076–2084 (in Chinese).
Wu, F., Tang, W., Xue, C., Sun, G., & Zhang, H. (2021). Experimental investigation on the static performance of stud connectors in steel-HSFRC composite beams. Materials, 14(11), Article 2744. https://doi.org/10.3390/ma14112744
Xue, W., Ding, M., Wang, H., & Luo, Z. (2008). Static behavior and theoretical model of stud shear connectors. Journal of Bridge Engineering, 13(6), 623–634. https://doi.org/10.1061/(ASCE)1084-0702(2008)13:6(623)
Xue, D., Liu, Y., Zhen, Y., & He, J. (2012). Static behavior of multi-stud shear connectors for steel-concrete composite bridge. Journal of Constructional Steel Research, 74(8), 1–7. https://doi.org/10.1016/j.jcsr.2011.09.017
Yang, X., & Wen, W. (2018). Ridge and Lasso regression models for cross-version defect prediction. IEEE Transactions on Reliability, 67(3), 885–896. https://doi.org/10.1109/TR.2018.2847353
Yang, Y., Liang, W., Yang, Q., & Cheng, Y. (2021). Flexural behavior of web embedded steel-concrete composite beam. Engineering Structures, 240, Article 112345. https://doi.org/10.1016/j.engstruct.2021.112345
Yaseen, Z., Tran, M., Kim, S., Bakhshpoori, T., & Deo, R. (2018). Shear strength prediction of steel fiber reinforced concrete beam using hybrid intelligence models: A new approach. Engineering Structures, 177, 244–255. https://doi.org/10.1016/j.engstruct.2018.09.074
Yosri, A. M., Farouk, A. I. B., Haruna, S. I., Deifalla, A. F., & Shaaban, W. M. (2023) Sensitivity and robustness analysis of adaptive neuro-fuzzy inference system (ANFIS) for shear strength prediction of stud connectors in concrete. Case Studies in Construction Materials, 18, Article e02096. https://doi.org/10.1016/j.cscm.2023.e02096
Yu, Z., Shi, W., & Kuang, Y. (2014). Experimental study on mechanical properties of corroded stud. Journal of Central South University (Science and Technology), 45(1), 249–255 (in Chinese).
Zhang, Y., Liu, A., Chen, B., Zhang, J., Pi, Y., & Bradford, M. (2020). Experimental and numerical study of shear connection in composite beams of steel and steel-fibre reinforced concrete. Engineering Structures, 215, Article 110707. https://doi.org/10.1016/j.engstruct.2020.110707
Zhang, F., Wang, C., Zou, X., Yang, W., Chen, D., Wang, Q., & Wang, L. (2023). Prediction of the shear resistance of headed studs embedded in precast Steel-Concrete structures based on an interpretable machine learning method. Buildings, 13(2), Article 496. https://doi.org/10.3390/buildings13020496
Zhu, L., Wang, J. J., Li, X., Tang, L., & Yu, B. Y. (2020). Experimental and numerical study of curved SFRC and ECC composite beams with various connectors. Thin-Walled Structures, 155, Article 106938. https://doi.org/10.1016/j.tws.2020.106938
Zouzou, Y., & Citakoglu, H. (2023). General and regional cross-station assessment of machine learning models for estimating reference evapotranspiration. Acta Geophysica, 71(2), 927–947. https://doi.org/10.1007/s11600-022-00939-9
American Association of State Highway and Transportation Officials. (2017). AASHTO LRFD bridge design specifications (AASHTO LRFDUS-2017) (8th ed.). Washington, DC, USA.
Barjouei, H. S., Ghorbani, H., Mohamadian, N., Wood, D. A., Davoodi, S., Moghadasi, J., & Saberi, H. (2021). Prediction performance advantages of deep machine learning algorithms for two-phase flow rates through wellhead chokes. Journal of Petroleum Exploration and Production, 11, 1233–1261. https://doi.org/10.1007/s13202-021-01087-4
Bayram, S., & Çıtakoğlu, H. (2023). Modeling monthly reference evapotranspiration process in Turkey: Application of machine learning methods. Environmental Monitoring and Assessment, 195(1), Article 67. https://doi.org/10.1007/s10661-022-10662-z
Bernus, A., Ottlé, C., & Raoult, N. (2021). Variance based sensitivity analysis of FLake lake model for global land surface modeling. Journal of Geophysical Research - Atmospheres, 126, Article e2019JD031928. https://doi.org/10.1029/2019JD031928
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Chahnasir, E. S., Zandi, Y., Shariati, M., Dehghani, E., Toghroli, A., Mohamed, E. T., Shariati, A., Safa, M., Wakil, K., & Khorami, M. (2018). Application of support vector machine with firefly algorithm for investigation of the factors affecting the shear strength of angle shear connectors. Smart Structures and Systems, 22(4), 413–424.
Citakoglu, H., & Demir, V. (2023). Developing numerical equality to regional intensity-duration-frequency curves using evolutionary algorithms and multi-gene genetic programming. Acta Geophysica, 71(1), 469–488. https://doi.org/10.1007/s11600-022-00883-8
Coşkun, Ö., & Citakoglu, H. (2023). Prediction of the standardized precipitation index based on the long short-term memory and empirical mode decomposition-extreme learning machine models: The Case of Sakarya, Türkiye. Physics and Chemistry of the Earth, 131, Article 103418. https://doi.org/10.1016/j.pce.2023.103418
Ding, F., Ni, M., Gong, Y., Yu, Z., Zhou, Z., & Zhou, L. (2014). Experimental study on slip behavior and calculation of shear bearing capacity for shear stud connectors. Journal of Building Structures, 35(9), 98–106 (in Chinese).
Ding, F., Yin, G., Wang, H., Wang, L., & Guo, Q. (2017). Static behavior of stud connectors in bi-direction push-off tests. Thin-Walled Structures, 120, 307–318. https://doi.org/10.1016/j.tws.2017.09.011
Ding, J., Li, Y., Xing, W., Ren, P., & Yuan, C. (2021). Mechanical properties and engineering application of single-span steel-concrete double-sided composite beams. Journal of Building Engineering, 1, Article 102644. https://doi.org/10.1016/j.jobe.2021.102644
European Committee for Standardization. (1994). Eurocode 4: Design of composite steel and concrete structures (EN 1994-1-1). Brussels, Belgium.
Farouk, A. I. B., Zhu, J. S., & Gu, Y. H. (2022). Finite element analysis of the shear performance of box-groove interface of ultra-high-performance concrete (UHPC)-normal strength concrete (NSC) composite girder. Innovative Infrastructure Solutions, 7, Article 212. https://doi.org/10.1007/s41062-022-00815-x
Farouk, A. I. B., Rong, W., & Zhu, J. (2023) Compressive behavior of ultra-high-performance-normal strength concrete (UHPC-NSC) column with the longitudinal grooved contact surface. Journal of Building Engineering, 68, Article 106074. https://doi.org/10.1016/j.jobe.2023.106074
Garzón-Roca, J., Marco, C. O., & Adam, J. M. (2013). Compressive strength of masonry made of clay bricks and cement mortar: Estimation based on Neural Networks and Fuzzy Logic. Engineering Structures, 48, 21–27. https://doi.org/10.1016/j.engstruct.2012.09.029
Ghorbani, H., Wood, D. A., Choubineh, A., Tatar, A., Abarghoyi, P. G., Madani, M., & Mohamadian, N. (2020). Prediction of oil flow rate through an orifice flow meter: Artificial intelligence alternatives compared. Petroleum, 6, 404–419. https://doi.org/10.1016/j.petlm.2018.09.003
Gu, J., Liu, D., Deng, W., & Zhang, J. (2019). Experimental study on the shear resistance of a comb-type perfobond rib shear connector. Journal of Constructional Steel Research, 158, 279–289. https://doi.org/10.1016/j.jcsr.2019.03.032
Guan, C., Duan, Y. Z., Zhai, J. Q., & Han, D. (2019). Hydraulic dynamics in split fuel injection on a common rail system and their artificial neural network prediction. Fuel, 255, Article 115792. https://doi.org/10.1016/j.fuel.2019.115792
Guo, H. Y., Dong, Y., Bastidas-Arteaga, E., & Gu, X. L. (2021). Probabilistic failure analysis, performance assessment, and sensitivity analysis of corroded reinforced concrete structures. Engineering Failure Analysis, 124, Article 105328. https://doi.org/10.1016/j.engfailanal.2021.105328
Hossain, K., Gladson, L., & Anwar, M. (2017). Modeling shear strength of medium-to ultra-high-strength steel fiber-reinforced concrete beams using artificial neural network. Neural Computing & Applications, 28(1), 1119–1130. https://doi.org/1007/s00521-016-2417-2
Hu, Y. Q., Qiu, M. H., Chen, L. L., Zhong, R., & Wang, J. (2021). Experimental and analytical study of the shear strength and stiffness of studs embedded in high strength concrete. Engineering Structures, 236, Article 111792. https://doi.org/10.1016/j.engstruct.2020.111792
Khalaf, J., Majeed, A., Aldlemy, M., Ali, Z., & Yaseen, Z. (2021). Hybridized deep learning model for perfobond rib shear strength connector prediction. Complexity, 2021, Article 6611885. https://doi.org/10.1155/2021/6611885
Kim, K., Han, O., Heo, W., & Kim, S. (2020). Behavior of Y-type perfobond rib shear connection under different cyclic loading conditions. Structures, 26, 562–571. https://doi.org/10.1016/j.istruc.2020.04.053
Luo, Y., Hoki, K., Hayashi, K., & Nakashima, M. (2016). Behavior and strength of headed stud-SFRCC shear connection. I: Experimental study. Journal of Structural Engineering, 142(2), Article 4015112. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001363
Ministry of Housing and Urban-Rural Development of the People’s of China. (2013). Code for design of steel and concrete composite bridges (GB 50917-2013). Beijing, China (in Chinese).
Ministry of Housing and Urban-Rural Development of the People’s of China. (2017). Standard for design of steel structures (GB 50017-2017). Beijing, China (in Chinese).
Özbayrak, A., Ali, M. K., & Çıtakoğlu, H. (2023). Buckling load estimation using multiple linear regression analysis and multigene genetic programming method in cantilever beams with transverse stiffeners. Arabian Journal for Science and Engineering, 48(4), 5347–5370. https://doi.org/10.1007/s13369-022-07445-6
Pianosi, F., Beven, K., Freer, J., Hall, J. W., Rougier, J., Stephenson, D. B., & Wagener, T. (2016). Sensitivity analysis of environmental models: A systematic review with practical workflow. Environmental Modelling & Software, 79, 214–232. https://doi.org/10.1016/j.envsoft.2016.02.008
Safa, M., Shariati, M., Ibrahim, Z., Toghroli, A., & Petkovic, D. (2016). Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam’s shear strength. Steel and Composite Structures, 21(3), 679–688. https://doi.org/10.12989/scs.2016.21.3.679
Sedghi, Y., Zandi, Y., Shariati, M., Ahmadi, E., Azar, V. M., Toghroli, A., Safa, M., Mohamad, E. T., Khorami, M., & Wakil, K. (2018). Application of ANFIS technique on performance of C and L shaped angle shear connectors. Smart Structures and Systems, 22(3), 335–340. https://doi.org/10.12989/sss.2018.22.3.335
Setvati, M. R., & Hicks, S. J. (2022). Machine learning models for predicting resistance of headed studs embedded in concrete. Engineering Structures, 254, Article 113803. https://doi.org/10.1016/j.engstruct.2021.113803
Shim, C. S., Lee, P. G., & Yoon, T. Y. (2004). Static behavior of large stud shear connectors. Engineering Structures, 26(12), 1853–1860. https://doi.org/10.1016/j.engstruct.2004.07.011
Shen, Y. W., Yap, K. S., & Li, X. (2020). A new probabilistic output constrained optimization extreme learning machine. IEEE Access, 8, 28934–28946. https://doi.org/10.1109/ACCESS.2020.2971012
Slater, E., Moni, M., & Alam, M. (2012). Predicting the shear strength of steel fiber reinforced concrete beams. Construction and Building Materials, 26(1), 423–436. https://doi.org/10.1016/j.conbuildmat.2011.06.042
Soroush, Z., Brian, T., & Abdollah, S. (2020). Significant variables affecting the performance of concrete panels impacted by wind-borne projectiles: A global sensitivity analysis. International Journal of Impact Engineering, 144, Article 03650. https://doi.org/10.1016/j.ijimpeng.2020.103650
Sobol, I. M. (1993). Sensitivity estimates for nonlinear mathematical models. Mathematical and Computer Modelling, 1(4), 407–414.
Tm, A., Jd, C., & Bra, B. (2019). Shear resistance of headed shear studs welded on welded plates in composite floors. Engineering Structures, 197, Article 109412. https://doi.org/10.1016/j.engstruct.2019.109412
Tzuc, O. M., Gamboa, O. R., Rosel, R. A., Poot, M. C., Edelman, H., Torres, M. J., & Bassam, A. (2021). Modeling of hygrothermal behavior for green facade’s concrete wall exposed to nordic climate using artificial intelligence and global sensitivity analysis. Journal of Building Engineering, 33, Article 101625. https://doi.org/10.1016/j.jobe.2020.101625
Vigneri, V., Odenbreit, C., & Romero-Gu, Z. A. (2021). Numerical study on design rules for minimum degree of shear connection in propped steel-concrete composite beams. Engineering Structures, 241(4), Article 112466. https://doi.org/10.1016/j.engstruct.2021.112466
Wang, Q., & Liu, Y. (2013). Experimental study of shear capacity of stud connector. Journal of Tongji University (Natural Science), 41(5), 659–663 (in Chinese).
Wang, J., Guo, J., Jia, L., Chen, S., & Dong, Y. (2017). Push-out tests of demountable headed stud shear connectors in steel-UHPC composite structures. Composite Structures, 170, 69–79. https://doi.org/10.1016/j.compstruct.2017.03.004
Wang, J., Qi, J., Tong, T., Xu, Q., & Xiu, H. (2019). Static behavior of large stud shear connectors in steel-UHPC composite structures. Engineering Structures, 178, 534–542. https://doi.org/10.1016/j.engstruct.2018.07.058
Wang, J., Zhang, A., & Wang, W. (2020). Effects of stud height on shear behavior of stud connectors. Journal of Zhejiang University (Engineering Science), 54(11), 2076–2084 (in Chinese).
Wu, F., Tang, W., Xue, C., Sun, G., & Zhang, H. (2021). Experimental investigation on the static performance of stud connectors in steel-HSFRC composite beams. Materials, 14(11), Article 2744. https://doi.org/10.3390/ma14112744
Xue, W., Ding, M., Wang, H., & Luo, Z. (2008). Static behavior and theoretical model of stud shear connectors. Journal of Bridge Engineering, 13(6), 623–634. https://doi.org/10.1061/(ASCE)1084-0702(2008)13:6(623)
Xue, D., Liu, Y., Zhen, Y., & He, J. (2012). Static behavior of multi-stud shear connectors for steel-concrete composite bridge. Journal of Constructional Steel Research, 74(8), 1–7. https://doi.org/10.1016/j.jcsr.2011.09.017
Yang, X., & Wen, W. (2018). Ridge and Lasso regression models for cross-version defect prediction. IEEE Transactions on Reliability, 67(3), 885–896. https://doi.org/10.1109/TR.2018.2847353
Yang, Y., Liang, W., Yang, Q., & Cheng, Y. (2021). Flexural behavior of web embedded steel-concrete composite beam. Engineering Structures, 240, Article 112345. https://doi.org/10.1016/j.engstruct.2021.112345
Yaseen, Z., Tran, M., Kim, S., Bakhshpoori, T., & Deo, R. (2018). Shear strength prediction of steel fiber reinforced concrete beam using hybrid intelligence models: A new approach. Engineering Structures, 177, 244–255. https://doi.org/10.1016/j.engstruct.2018.09.074
Yosri, A. M., Farouk, A. I. B., Haruna, S. I., Deifalla, A. F., & Shaaban, W. M. (2023) Sensitivity and robustness analysis of adaptive neuro-fuzzy inference system (ANFIS) for shear strength prediction of stud connectors in concrete. Case Studies in Construction Materials, 18, Article e02096. https://doi.org/10.1016/j.cscm.2023.e02096
Yu, Z., Shi, W., & Kuang, Y. (2014). Experimental study on mechanical properties of corroded stud. Journal of Central South University (Science and Technology), 45(1), 249–255 (in Chinese).
Zhang, Y., Liu, A., Chen, B., Zhang, J., Pi, Y., & Bradford, M. (2020). Experimental and numerical study of shear connection in composite beams of steel and steel-fibre reinforced concrete. Engineering Structures, 215, Article 110707. https://doi.org/10.1016/j.engstruct.2020.110707
Zhang, F., Wang, C., Zou, X., Yang, W., Chen, D., Wang, Q., & Wang, L. (2023). Prediction of the shear resistance of headed studs embedded in precast Steel-Concrete structures based on an interpretable machine learning method. Buildings, 13(2), Article 496. https://doi.org/10.3390/buildings13020496
Zhu, L., Wang, J. J., Li, X., Tang, L., & Yu, B. Y. (2020). Experimental and numerical study of curved SFRC and ECC composite beams with various connectors. Thin-Walled Structures, 155, Article 106938. https://doi.org/10.1016/j.tws.2020.106938
Zouzou, Y., & Citakoglu, H. (2023). General and regional cross-station assessment of machine learning models for estimating reference evapotranspiration. Acta Geophysica, 71(2), 927–947. https://doi.org/10.1007/s11600-022-00939-9