Junho Song

Bouc–Wen class models have been widely used to efficiently describe smooth hysteretic behavior in time history and random vibration analyses. This paper proposes a generalized Bouc–Wen model with sufficient flexibility in shape control to describe highly asymmetric hysteresis loops. Also introduced is a mathematical relation between the shape-control parameters and the slopes of the hysteresis loops, so that the model parameters can be identified systematically in conjunction with available para

Bounds on system probability in terms of marginal or joint component probabilities are of interest when exact solutions cannot be obtained. Currently, bounding formulas employing unicomponent probabilities are available for series and parallel systems, and formulas employing bi- and higher-order component probabilities are available for series systems. No theoretical formulas exist for general systems. It is shown in this paper that linear programming (LP) can be used to compute bounds for any s

This paper studies a non-random-walk Markov Chain Monte Carlo method, namely the Hamiltonian Monte Carlo (HMC) method in the context of Subset Simulation used for reliability analysis. The HMC method relies on a deterministic mechanism inspired by Hamiltonian dynamics to propose samples following a target probability distribution. The method alleviates the random walk behavior to achieve a more effective and consistent exploration of the probability space compared to standard Gibbs or Metropolis

Structural failures caused by a strong earthquake may induce a large number of casualties and huge socioeconomic losses. To design a structure that can withstand such earthquake events, it is essential to accurately estimate the nonlinear structural responses caused by strong ground motions. As a replacement of an onerous and complex nonlinear time history analysis, simple regression-based equations have been widely adopted in routine engineering practices. It is, however, noted that the respons

Complexity of today’s engineering systems inevitably makes the computational simulation of their performance challenging and time-consuming. Since structural reliability analysis methods generally repeat such computational simulations, it is essential to reduce the number of function evaluations required to achieve reliable estimates. In research efforts to fulfill this aim, adaptive Kriging methods have gained significant interest because of desirable properties and accuracy of the surrogate mo

Abstract Recent earthquake events evidenced that damage of structural components in a lifeline network may cause prolonged disruption of lifeline services, which eventually results in significant socio‐economic losses in the affected area. Despite recent advances in network reliability analysis, the complexity of the problem and various uncertainties still make it a challenging task to evaluate the post‐hazard performance and connectivity of lifeline networks efficiently and accurately. In order

The first-passage probability, describing the probability that a scalar process exceeds a prescribed threshold during an interval of time, is of great engineering interest. This probability is essential for estimating the reliability of a structural component whose response is a stochastic process. When considering the reliability of an engineering system composed of several interdependent components, the probability that two or more response processes exceed their respective safe thresholds dur

Summary As urban systems become more highly sophisticated and interdependent, their vulnerability to earthquake events exhibits a significant level of uncertainties. Thus, community‐level seismic risk assessments are indispensable to facilitate decision making for effective hazard mitigation and disaster responses. To this end, new frameworks for pre‐ and post‐earthquake regional loss assessments are proposed using deep learning methods. First, to improve the accuracy of the response prediction

This paper provides an overview of theories and applications of structural system reliability (SSR). The paper defines SSR problems and discusses the growing needs for SSR analysis and technical challenges. Detailed literature reviews are provided for three subtopics: SSR methods for Boolean system events, SSR methods for sequential failures, and SSR-based design/topology optimization. Discussions of each subtopic define the target problem using mathematical formulations and categorize existing