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Experimental investigation and predictive modeling of shear performance for concrete-encased steel beams using artificial neural networks

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Abstract

This manuscript employs a highly efficient artificial intelligence (AI) technique for machine learning (ML) through artificial neural networks (ANNs) and introduces a novel numerical predictive model capable of accurately forecasting the shear capacity of concrete-encased steel (CES) beams. The research begins by conducting shear tests on nine CES beams with high-strength steel, which addresses a significant gap in the shear performance of high-strength steel in CES beams. Subsequently, a comprehensive database of CES beam shear tests is established to train and validate the ANN model. The database consists of 242 sets of test data, compiled from published literature, encompassing a wide range of geometrical and material properties. A sensitivity analysis of the proposed model is then performed using a Pearson chi-square test to determine the relative importance of each input parameter on shear strength. Furthermore, a thorough examination is conducted to assess the impact of each parameter. Finally, the proposed predictive model is compared against current design codes, including ANSI/AISC 360–16 (USA, North America), BS EN 1994-1-1:2004 (Europe), and JGJ 138–2016 (China, Asia). The comparison reveals that ML technology exhibits higher accuracy and robustness in predicting shear bearing capacity.

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Authors and Affiliations

  1. School of Civil Engineering, Northeast Forestry University, Harbin, 150000, China

    Jun Wang & Menglin Cui

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Contributions

WJ: Resources, Supervision, Project administration, Funding acquisition, Writing—Review & Editing, Visualization, Supervision. CM: Term, Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data Curation, Writing—Original Draft, Writing—Review & Editing, Visualization, Supervision.

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Correspondence to Menglin Cui.

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Appendices

Appendix A

Tables

Table 6 Experimental data used for ANN model
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6 and

Table 7 Comparison of experimentally and numerically predicted shear strength
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7.

Appendix B: The weight and bias for the ANN model

$${X}^{T}=\left({f}_{c} , B ,D ,{\rho }_{sh} ,{\rho }_{sw} ,{f}_{yw} , \lambda ,{p}_{r}\right)$$
$${W}_{1}=\left(\begin{array}{cc}\begin{array}{cc}\begin{array}{c}\begin{array}{c}\begin{array}{c}-0.0311\\ -0.0010\end{array}\\ \begin{array}{c}-0.0030\\ +0.0848\end{array}\end{array}\\ \begin{array}{c}\begin{array}{c}-0.3684\\ +0.0040\end{array}\\ \begin{array}{c}+0.8720\\ -4.5948\end{array}\end{array}\end{array}& \begin{array}{c}\begin{array}{c}\begin{array}{c}-0.0553\\ +0.0247\end{array}\\ \begin{array}{c}-0.0122\\ -5.8023\end{array}\end{array}\\ \begin{array}{c}\begin{array}{c}-2.2935\\ -0.0272\end{array}\\ \begin{array}{c}+0.4644\\ -10.421\end{array}\end{array}\end{array}\end{array}& \begin{array}{cc}\begin{array}{c}\begin{array}{c}\begin{array}{c}+0.0007\\ +0.0040\end{array}\\ \begin{array}{c}+0.0053\\ +3.1074\end{array}\end{array}\\ \begin{array}{c}\begin{array}{c}+2.7683\\ +0.0047\end{array}\\ \begin{array}{c}+1.2689\\ -0.2857\end{array}\end{array}\end{array}& \begin{array}{c}\begin{array}{c}\begin{array}{c}+0.0255\\ -0.0029\end{array}\\ \begin{array}{c}+0.0041\\ +0.3077\end{array}\end{array}\\ \begin{array}{c}\begin{array}{c}+2.7727\\ -0.0206\end{array}\\ \begin{array}{c}+1.7845\\ +0.8892\end{array}\end{array}\end{array}\end{array}\end{array}\right) {B}_{1}=\left(\begin{array}{c}+2.1215\\ +13.932\\ -8.5465\\ -0.6031\end{array}\right)$$
$${W}_{2}=\left(\begin{array}{cc}\begin{array}{cc}-3.5295& -3.4323\\ -1.0791& +2.1352\end{array}& \begin{array}{cc}-0.2218& +1.1047\\ +0.0770& +1.5640\end{array}\\ \begin{array}{cc}+2.2960& +0.9730\\ -0.3449& -1.1034\end{array}& \begin{array}{cc}+0.2050& -0.3092\\ -0.0432& -0.1027\end{array}\end{array}\right) {B}_{2}=\left(\begin{array}{c}+0.2011\\ -0.0821\\ +0.4003\\ +1.6204\end{array}\right)$$
$${W}_{3}=\left(\begin{array}{c}+1180.95\\ +764.381\\ -88.7798\\ +1566.92\end{array}\right) {B}_{3}=\left(+222.636\right)$$

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Wang, J., Cui, M. Experimental investigation and predictive modeling of shear performance for concrete-encased steel beams using artificial neural networks. Mater Struct 56, 141 (2023). https://doi.org/10.1617/s11527-023-02226-5

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