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Seung-Eock Kim,Quang-Viet Vu,George Papazafeiropoulos,Zhengyi Kong,Viet-Hung Truong 국제구조공학회 2020 Steel and Composite Structures, An International J Vol.37 No.2
In this paper, the efficiency of five Machine Learning (ML) methods consisting of Deep Learning (DL), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Gradient Tree Booting (GTB) for regression and classification of the Ultimate Load Factor (ULF) of nonlinear inelastic steel frames is compared. For this purpose, a two-story, a six-story, and a twenty-story space frame are considered. An advanced nonlinear inelastic analysis is carried out for the steel frames to generate datasets for the training of the considered ML methods. In each dataset, the input variables are the geometric features of W-sections and the output variable is the ULF of the frame. The comparison between the five ML methods is made in terms of the mean-squared-error (MSE) for the regression models and the accuracy for the classification models, respectively. Moreover, the ULF distribution curve is calculated for each frame and the strength failure probability is estimated. It is found that the GTB method has the best efficiency in both regression and classification of ULF regardless of the number of training samples and the space frames considered.
Behavior of composite CFST beam-concrete column joints
Seung-Eock Kim,Ji-Hun Choi,Thai-Hoan Pham,Viet-Hung Truong,Zhengyi Kong,Nguyen-Thê Duong,Quang-Viet Vu 국제구조공학회 2020 Steel and Composite Structures, An International J Vol.37 No.1
This paper introduces a new composite joint, which is the composite CFST beam- concrete column joint, and it is more convenient for transportation and erection than conventionally welded joints. The main components of this joint include steel H-beams welded with CFST beams, reinforced concrete columns, and reinforced concrete slabs. The steel H-beams and CFST beams are connected with a concrete slab using shear connectors to ensure composite action between them. An experimental investigation was conducted to evaluate the proposed composite joint performance. A three-dimensional (3D) finite element (FE) model was developed and analyzed for this joint using the ABAQUS/explicit. The FE model accuracy was validated by comparing its results with the relevant test results. Additionally, the parameters that consisted of the steel box beam thickness, concrete compressive strength, steel yield strength, and reinforcement ratio in the concrete slab were considered to investigate their influence on the proposed joint performance.