Research on Mechanical Properties and Parameter Identification of Beam-Column Joint with Gusset Plate Angle Using Experiment and Stochastic Sensitivity Analysis

Based on stochastic sensitivity analysis, a new style of joint structure with greater ductility and higher strength—the beam-column joint with gusset plate angle (JGA) steel—was proposed. Research on the static and hysteretic behavior of the JGA was performed using finite element analysis and experimental methods. The research results indicated that adding a seat angle could increase the positive and negative initial rotational stiffness and strength and provide a better energy consumption performance of the joint. An improved chaotic particle swarm optimization (ICPSO) neural network algorithm was used to study the stochastic sensitivity. Seven important parameters that influence the bending stiffness and strength of the JGA, namely, the beam height, beam flange width, beam web thickness, gusset plate thickness, connection angle steel thickness, connection angle steel width, and seat angle steel thickness, were investigated by stochastic sensitivity analysis. Moreover, the beam height, connection angle steel, and seat angle steel thickness, which had significant influences on the mechanical properties of the joints, were studied in depth by finite element analysis. Within the range of the parameters of the joint, the higher the beam height was, the larger the connection angle thickness was; the smaller the connection angle width was, the better the joint performance was. A reasonable design of the JGA is proposed: a beam with the SH2 section (250 × 125 × 6 × 9 mm) and a 10 mm thick and 75 mm long angle steel connection.

Stochastic Analysis of the Electromagnetic Induction Effect on a Neuron's Action Potential Dynamics

This paper examines the effect of electromagnetic induction on the electrophysiology of a single cortex neuronthrough two different modes associated with the nature of the external neuronal stimulus. By using the recently extended induction-based variant of the well-known and biologically plausible Hodgkin-Huxley neuron model, bifurcation analysis is performed. Electromagnetic induction caused by magnetic flux is captured using a polynomial approximation of a memristor embedded into the neuron model. In order to determine true influence of the variability of ion channels conductivity, the stochastic sensitivity analysis is performed post hoc. Additionally, numerical simulations are enriched with uncertainty quantification, observing values of ion channels conductivity as random variables. The aim of the study is to computationally determine the sensitivity of the action potential dynamics with respect to the changes in conductivity of each ion channel so that the future experimental procedures, most often medical treatments, may be adapted to different individuals in various environmental conditions.

Confidence-Based Design Optimization for a More Conservative Optimum Under Surrogate Model Uncertainty Caused by Gaussian Process

Even though many efforts have been devoted to effective strategies to build accurate surrogate models, surrogate model uncertainty is inevitable due to a limited number of available simulation samples. Therefore, the surrogate model uncertainty, one of the epistemic uncertainties in reliability-based design optimization (RBDO), has to be considered during the design process to prevent unexpected failure of a system that stems from an inaccurate surrogate model. However, there have been limited attempts to obtain a reliable optimum taking into account the surrogate model uncertainty due to its complexity and computational burden. Thus, this paper proposes a confidence-based design optimization (CBDO) under surrogate model uncertainty to find a conservative optimum despite an insufficient number of simulation samples. To compensate the surrogate model uncertainty in reliability analysis, the confidence of reliability is brought to describe the uncertainty of reliability. The proposed method employs the Gaussian process modeling to explicitly quantify the uncertainty of a surrogate model. Thus, metamodel-based importance sampling and expansion optimal linear estimation are exploited to reduce the computational burden on confidence estimation. In addition, stochastic sensitivity analysis of the confidence is developed for CBDO, which is formulated to find a conservative optimum than an RBDO optimum at a specific confidence level. Numerical examples using mathematical functions and finite element analysis show that the proposed confidence analysis and CBDO can prevent overestimation of reliability caused by an inaccurate surrogate model.