ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering

In the insurance industry, the actuarial team experiences significant challenges for pricing a competitive but also fair premium and keeping an accurate level of reserve, which leads inevitably to numerous adjustments over time and potentially several millions of US dollars in annual losses. Because the model and parameter uncertainties play key roles for actuaries, decision makers, and policymakers, the implementation of advanced mathematical and statistical techniques is highly required. Over the last two decades, applications of regime switching models to finance and economics have received strong attention among researchers and particularly among market practitioners. This paper attempts to consider how a linear arbitrary regime switching system in discrete-time framework could be applied to calculate the medium- and long- term reserves and the relevant premiums (abbreviated here as the P-R process) from the point of view of an insurer. In this direction, some recently developed techniques from linear robust control theory are applied to explore the stability, stabilization, and robust $H_{\infty }$-control of a P-R system and the potential effects of abrupt structural changes in the economic fundamentals, as well as the insurer’s strategy over a finite time period. Sufficient linear matrix inequality conditions are derived for this treatment. Finally, a numerical example is illustrated.

This work addresses the issue of model selection in adaptive kriging-based Monte Carlo reliability analysis. It is shown that arbitrary model selection (kriging trend and correlation) can lead to poor probability of failure estimates for complex systems. We propose a method for kriging model development that employs information-theoretic multimodel inference and introduces an averaged kriging model derived from the associated model probabilities. The proposed multimodel kriging model is then integrated into an adaptive sample selection method that merges the surrogate enhanced stochastic search method with a learning function modified from the adaptive kriging—Monte Carlo simulation (AK-MCS) method. The result is an efficient method for a surrogate model–based reliability analysis that converges as fast as, or faster than, the AK-MCS method but with significantly improved robustness providing greater assurance in model accuracy.

From Presidential Policy Directive 21, professional societies’ national meetings, to major United Nations initiatives, resilience has captured the attention of powerful stakeholders. Resilience models focus on system performance and threats to the system. Few models consider the preferences of the stakeholders of the systems. This course of study investigates the effect of incorporating stakeholder preferences into four existing resilience models: Resilience Factor, Quotient Resilience, Total Quotient Resilience, and Integral Resilience. The preferences of interest are stakeholder time horizon, endogenous performance preference, and intertemporal substitutability of system performance. The resultant eight illustrative models are comparatively examined for sensitivity to changes in system performance and stakeholder preferences using four fundamental system performance and stakeholder preference models. A city infrastructure system dynamics simulation serves as a case study for the eight resilience models. The resilience models rank the preferred courses of action for improving the infrastructure system. The study concludes that the Extended Integral Resilience model most effectively differentiates among the alternative courses of action.

In this study, the reliability of prestressed concrete (PC) bridge girders in the shear rating process is quantified, in which the use of a simple procedure to enhance the accuracy of shear capacity evaluation for rating is evaluated. It was found that the approximate methods used in code-based procedures to develop shear resistance used for reliability calibration of existing code standards result in larger discrepancies in reliability than previously assumed, as well as substantially conservative shear capacity evaluation in most cases. To examine the effects of using a more accurate shear rating process, a selection of typical PC bridge girders designed and rated for Michigan legal loads is considered as an example. For the example girders, use of existing methods resulted in some reliability indices below the required levels. However, use of the more accurate procedure resulted in shear reliability indices that met the target reliability index, thus preventing unnecessary traffic restrictions as well as avoiding potentially rating structures unconservatively.