Augmented Data-Driven Machine Learning for Digital Twin of Stud Shear Connections
Gi-Tae Roh,
Nhung Vu,
Chi-Ho Jeon
et al.
Abstract:Existing design codes for predicting the strength of stud shear connections in composite structures are limited when adapting to constant changes in materials and configurations. Machine learning (ML) models for predicting shear connection are often constrained by the number of input variables, resembling conventional design equations. Moreover, these models tend to overlook considerations beyond those directly comprising the connection. In addition, the data used in ML are often biased and limited in quantity… Show more
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