This paper presents the development of a comprehensive composite beam-column fiber element for large displacement nonlinear inelastic analysis of concrete-filled steel tube ͑CFT͒ columns. The bond/slip formulation represents the interaction between concrete and steel over the entire contact surface between the two materials. Thus, the modeling accounts for the two factors that cause the slippage between steel shell and concrete core. The first factor is the difference between axial elongation of the steel shell and the concrete core, and the second is the difference between curvatures in the cross section for the concrete core and the steel shell. These effects are integrated over the perimeter and are added to the virtual work expression of the basic element. Furthermore, the constitutive models employed for concrete and steel are based on the results of a recent study and include the confinement and biaxial effects. A 13 degree of freedom ͑DOF͒ element with three nodes, which has five DOF per end node and three DOF on the middle node, has been chosen. The quadratic Lagrangian shape functions for axial deformation and the quartic Hermitian shape functions for the transverse directions are used. The model is implemented to analyze several CFT columns under constant concentric axial load and cyclic lateral load. The effect of semi-and perfect bond is investigated and compared with experiments. Good correlation has been found between experimental results and theoretical analyses. The results show that the use of a studded or ribbed steel shell causes greater ultimate strength and higher dissipation of energy than the columns with nonstudded steel shells.
This article presents a signal-based seismic structural health monitoring technique for damage detection and evaluating damage severity of a multi-story frame subjected to an earthquake event. As a case study, this article is focused on IASC–ASCE benchmark problem to provide the possibility for side-by-side comparison. First, three signal processing techniques including empirical mode decomposition, Hilbert vibration decomposition, and local mean decomposition, categorized as instantaneous time–frequency methods, have been compared to find a method with the best resolution in extracting frequency responses. Time-varying single degree of freedom and multiple degree of freedom models are used since real vibration signals are nonstationary and nonlinear in nature. Based on the results, empirical mode decomposition has proved to outperform than the others. Second, empirical mode decomposition is used to extract the acceleration response of the sensors. Next, a two-stage artificial neural network is used to classify damage patterns. The first artificial neural network identifies location and severity of damage and the second one calculates the severity of damage for the entire structure. IASC–ASCE benchmark problem is used to validate the proposed procedure. By taking advantage of signal processing and artificial intelligence techniques, damage detection of structures was successfully carried out in three levels including damage occurrence, damage severity, and the location of damage.
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