Data-driven shear capacity analysis of headed stud in steel-UHPC composite structures
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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Detail(s)
Original language | English |
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Article number | 118946 |
Journal / Publication | Engineering Structures |
Volume | 321 |
Online published | 10 Sept 2024 |
Publication status | Published - 15 Dec 2024 |
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Abstract
This study employs machine learning (ML) techniques for shear capacity analysis of headed stud in steel-UHPC composite structure. 194 experimental and numerical results of push-out tests are collected and serve as the dataset for ML models training and testing. Six ML algorithms are implemented to train ML models. Comparison analysis is conducted to compare the performance of three empirical formulae and ML models. The results demonstrate that both the Random Forest and eXtreme Gradient Boosting Trees (XGBoost) models exhibit excellent performance, surpassing an R2 value of 97 % on both training and testing datasets. In contrast, the empirical formulae perform less effectively. Besides, the study incorporates the Shapley additive explanations algorithm to ranking the importance of each feature, and carried out parametric analysis to investigate the correlation between each feature and shear capacity using all samples in the collected dataset. Notably, the most influential variables include diameter and ultimate strength of the headed studs, followed by stud height and thickness of UHPC slab. Cover thickness of UHPC layer and steel fiber volume fractions shows little influence on the shear capacity. Furthermore, based on findings of parametric analysis, design recommendations are provided to avoid shear capacity reduction caused by group effect of studs and UHPC damage. Finally, a user-friendly interactive software is developed and provided to facilitate the shear capacity prediction and design of headed studs in steel-UHPC composite structures. © 2024 Elsevier Ltd.
Research Area(s)
- Headed stud, Machine learning, Model explanation, Shear capacity, Software development, Steel-UHPC composite structures
Citation Format(s)
Data-driven shear capacity analysis of headed stud in steel-UHPC composite structures. / Zhou, Chang; Wang, Wenwei; Zheng, Yuzhou.
In: Engineering Structures, Vol. 321, 118946, 15.12.2024.
In: Engineering Structures, Vol. 321, 118946, 15.12.2024.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review