Vol. 20, No. 4, pp. 376-384 (2024)
A BAYESIAN-OPTIMIZED NEURAL NETWORK MODEL FOR
SHEAR CAPACITY OF A PERFOBOND STRIP CONNECTOR IN
VARIOUS TYPES OF COMPOSITE STRUCTURES
Minh Hai Nguyen and Hoang Nam Phan *
Faculty of Road and Bridge Engineering, The University of Danang - University of Science and Technology, Da Nang, Viet Nam
*(Corresponding author: E-mail:This email address is being protected from spambots. You need JavaScript enabled to view it.)
Received: 17 February 2024; Revised: 21 August 2024; Accepted: 5 September 2024
DOI:10.18057/IJASC.2024.20.4.5
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ABSTRACT
Perfobond strips are integral to composite steel-concrete structures or joints between precast concrete elements. However, the diverse boundary conditions and design parameters in various applications have led to numerous empirical and analytical methods to investigate their shear behavior. Existing empirical formulas often fail to accurately assess the shear capacity of perfobond strip connectors under different conditions. This study addresses this issue by developing a comprehensive prediction model for the shear capacity of perfobond strip connectors using a Bayesian-optimized artificial neural network (ANN). The proposed model evaluates shear capacity under various conditions, including the presence or absence of penetrating rebar in perforations, the use of normal or fiber-reinforced concrete, and various experimental specimen shapes applied in different composite structures. By utilizing an extensive dataset of 253 specimens, including 136 previously tested by the authors, the model is trained and optimized with a Bayesian optimization algorithm using a Gaussian process prior. This approach explores a wide range of hyperparameters to achieve optimal performance. The results show that the model excels in predicting the shear capacity of perfobond strip connectors across different design parameters and experimental conditions. A subsequent parametric study confirms the model's consistency with the shear-resisting mechanism of perfobond strips, underscoring the reliability and effectiveness of the ANN-based model. This model serves as a valuable tool for accurately predicting shear capacity in perfobond strip connectors across diverse design scenarios.
KEYWORDS
Perfobond strip, Bayesian optimization, Artificial neural network, Shear capacity, Sensitivity analysis, A technical paper
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