ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

This article is available in the ASME Digital Collection at .

This article is available in the ASME Digital Collection at .

Complex systems—such as gas turbines, industrial plants, and infrastructure networks—are of paramount importance to modern societies. However, these systems are subject to various threats. Novel research does not only focus on monitoring and improving the robustness and reliability of systems but also focus on their recovery from adverse events. The concept of resilience encompasses these developments. Appropriate quantitative measures of resilience can support decision-makers seeking to improve or to design complex systems. In this paper, we develop comprehensive and widely adaptable instruments for resilience-based decision-making. Integrating an appropriate resilience metric together with a suitable systemic risk measure, we design numerically efficient tools aiding decision-makers in balancing different resilience-enhancing investments. The approach allows for a direct comparison between failure prevention arrangements and recovery improvement procedures, leading to optimal tradeoffs with respect to the resilience of a system. In addition, the method is capable of dealing with the monetary aspects involved in the decision-making process. Finally, a grid search algorithm for systemic risk measures significantly reduces the computational effort. In order to demonstrate its wide applicability, the suggested decision-making procedure is applied to a functional model of a multistage axial compressor, and to the U-Bahn and S-Bahn system of Germany's capital Berlin. This article is available in the ASME Digital Collection at .

The direct physical loss from a tornado is one possible factor in considering resilience goals for a community. Estimating such loss has historically been achieved either through analysis of empirical data from historical events meant to then match future hypothetical events or through a cost analysis based on a building's damage state. These approaches provide a solid baseline for estimating loss from wind events; however, gathering data from historical events may assume all locations are the same, while analyses based solely on the building damage state may not include a building's contents. This study builds on work previously established in determining loss from building damage state fragilities, by including a loss to the building's interior (including contents) based on Federal Emergency Management Agency's (FEMA's) HAZUS equations. The approach laid out in this paper is then validated, showing what is deemed an acceptable level of accuracy, using the May 22, 2011 Joplin tornado that devastated the local community. Once validated, the same tornado path is relocated in different directions, ultimately crossing most of city of Joplin in four additional hypothetical scenarios. The results of both hindcasting the 2011 Joplin tornado and its hypothetical track variations show commercial (nonresidential) type buildings as key in contributing to the direct physical loss of a wind event. Ultimately, this provides decision makers with a point of consideration when evaluating their community's resilience goals. This article is available in the ASME Digital Collection at .

Electricity transmission systems are essential to a community's functionalities but are susceptible to the aggressive environmental or operational factors such as the natural hazards and the environment-induced component deterioration during their service life. The service reliability of a power grid system shall be assessed under a probability-based framework taking into account the spatial uncertainty and correlation arising from both the component performance (i.e., plants, substations, and transmission lines) and the load effects. This paper estimates the resilience of a power grid system in the presence of the impacts of component deterioration and correlation, with an emphasis on the earthquake excitation hazards. The spatial variability of the component capacity and that associated with the earthquake ground motion are modeled by the Gaussian copula function. A posthazard performance indicator (PPI) is used to represent the seismic vulnerability of a grid system subjected to earthquake actions. With this, the statistics (mean value and variance) of the PPI conditional on the occurrence of an earthquake is estimated by a simulation-based method, and the resilience of the grid system (measured by a resilience index based on the recovery process of PPI) is assessed in a closed-form. The applicability of the proposed method is demonstrated through the seismic resilience assessment of an illustrative grid system. This article is available in the ASME Digital Collection at .

Critical infrastructure networks are geographically distributed systems spanning multiple scales. These networks are increasingly interdependent for normal operations, which causes localized asset failures from natural hazards or man-made interference to propagate across multiple networks, affecting those far removed from an initiating failure event. This paper provides methodology to identify such failure propagation effects by quantifying the spatial variability in magnitude, frequency, and disruptive reach of failures across national infrastructure networks. To achieve this, we present methodology to combine functionally interdependent infrastructure networks with geographic interdependencies by simulating complete asset failures across a national scale grid of spatially localized hazards. A range of metrics are introduced to compare the systemic vulnerabilities of infrastructure systems and the resulting spatial variability in both the potential for initiating widespread failures and the risk of being impacted by distant hazards. We demonstrate the approach through an application in New Zealand of infrastructures across the energy (electricity, petroleum supply), water and waste (water supply, wastewater, solid waste), telecommunications (mobile networks), and transportation sectors (passenger rail, ferry, air, and state highways). In addition to identifying nationally significant systemic vulnerabilities, we observe that nearly half (46%) of the total disruptions across the simulation set can be attributed to network propagation initiated asset failures. This highlights the importance in considering interdependencies when assessing infrastructure risks and prioritizing investment decisions for enhancing resilience of national networks. This article is available in the ASME Digital Collection at .

This paper proposes a new strategy to improve the reliability of phased mission systems (PMS), namely, by swapping of components. In the proposed strategy, when a component fails, it can be swapped by another one in the system which is still functioning. We consider both the options to swap components at any time and for swaps to be possible only at phase transitions. This paper also discusses the strategy of swapping components according to structure importance. The structure importance is used to measure the importance level of the components in contributing to system reliability. Then, when a component with high importance fails, it is swapped by another component with lower importance from the system which has not yet failed. The survival signature methodology is implemented to assess the reliability of PMS when there is a possibility of components swapping. In addition, we consider the cost effectiveness of component swapping through two models (time independent and time dependent) of penalty costs for PMS. This article is available in the ASME Digital Collection at .

Modeling the resilience of interdependent critical infrastructure (ICI) requires a careful assessment of interdependencies as these systems are becoming increasingly interconnected. The interdependent connections across ICIs are often subject to uncertainty due to the lack of relevant data. Yet, this uncertainty has not been properly characterized. This paper develops an approach to model the resilience of ICIs founded in probabilistic graphical models. The uncertainty of interdependency links between ICIs is modeled using stochastic block models (SBMs). Specifically, the approach estimates the probability of links between individual systems considered as blocks in the SBM. The proposed model employs several attributes as predictors. Two recovery strategies based on static and dynamic component importance ranking are developed and compared. The proposed approach is illustrated with a case study of the interdependent water and power networks in Shelby County, TN. Results show that the probability of interdependency links varies depending on the predictors considered in the estimation. Accounting for the uncertainty in interdependency links allows for a dynamic recovery process. A recovery strategy based on dynamically updated component importance ranking accelerates recovery, thereby improving the resilience of ICIs. This article is available in the ASME Digital Collection at .

Robust design strategies continue to be relevant during concept-stage complex system design to minimize the impact of uncertainty in system performance due to uncontrollable external failure events. Historical system failures such as the 2003 North American blackout and the 2011 Arizona-Southern California Outages show that decision making, during a cascading failure, can significantly contribute to a failure's magnitude. In this paper, a scalable, model-based design approach is presented to optimize the quantity and location of decision-making agents in a complex system, to minimize performance loss variability after a cascading failure, regardless of where the fault originated in the system. The result is a computational model that enables designers to explore concept-stage design tradeoffs based on individual risk attitudes (RA) for system performance and performance variability, after a failure. The IEEE RTS-96 power system test case is used to evaluate this method, and the results reveal key topological locations vulnerable to cascading failures, that should not be associated with critical operations. This work illustrates the importance of considering decision making when evaluating system level tradeoffs, supporting robust design. This article is available in the ASME Digital Collection at .

Light, sweet crude oils are depleting, forcing oil and gas companies to explore and operate in increasingly deeper waters, remote geographical locations, and harsher environmental conditions with higher safety risks. This paper analyzed the common misuse and errors of typical quantitative risk assessments (QRAs) during identification, assessment, approval, and project implementation stages of a project for the case of optimization of facilities and manning fixed offshore oil and gas platform, as well as the scenario of a gas leak from riser pipeline of a floating production platform. The lessons learned were then applied to design an optimized QRA process for a real case, preproject assessment for a proposed addition of a riser platform (R-A) to a fixed offshore oil and gas platform complex using individual risk per annum (IRPA) and potential loss of life (PLL) analyses. Findings reveal that applying the standard hazard and effects management process (HEMP) and as low as reasonably practicable (ALARP) guidelines and tools alone are insufficient. The application of practical lessons learned from the past oil and gas disasters using IRPA and PLL parameters has helped this research to produce an optimized QRA. The optimized QRA process is a live process which could be further improved with future lessons learned. This article is available in the ASME Digital Collection at .

This paper proposes the time-dependent reliability model of structures under different stochastic loads based on the stress–strength interference (SSI) model and probabilistic statistical theory. First, the different stochastic loads mean that the loads are discrete, and that one applied load is from random variable and the other applied load may be from another random variable, and the cumulative effect and correlation are considered when they are applied for multiple times. Second, two different time-dependent reliability models, with and without considering strength degradation, are established in this paper. Meanwhile, the relationship between the discrete load frequency and time is described using Poisson process, and the time-dependent reliability model under continuous time is also provided. Finally, the proposed time-dependent reliability model of structures is validated with the Monte Carlo simulation (MCS), and the effectiveness of the proposed method is testified. This article is available in the ASME Digital Collection at .

Cyber-physical systems are engineered systems that rely on the integration of physical processes and computational resources. While this integration enables advanced techniques for monitoring and controlling systems, it also exposes the physical process to cyber-threats. An attacker who is able to access control inputs and mask measurements could damage the system while remaining undetected. By masking certain measurement signals, an attacker may be able to render a portion of the state space unobservable, meaning that it is impossible to estimate or infer the value of those states. This is called an observability attack. A game-theoretic approach is presented to analyze observability attacks. The attacker's strategy set includes all possible combinations of masked measurements. The defender's strategy set includes all possible combinations of measurement reinforcements. The attacker's and defender's utilities are quantified using the responses of the observable and unobservable states. The observability attack game is analyzed for a nuclear balance of plant system. Multiple pure-strategy and mixed-strategy Nash equilibria are identified, and the conditions for their existence are presented. Using this procedure, a security and control engineer can select the optimal strategy to defend a cyber-physical system from observability attacks. This article is available in the ASME Digital Collection at .

Most of the existing reliability-based design optimization (RBDO) are not capable of analyzing data from multifidelity sources to improve the confidence of optimal solution while maintaining computational efficiency. In this paper, we propose a novel reliability-based multifidelity optimization (RBMO) framework that adaptively integrates both low- and high-fidelity data for achieving reliable optimal designs. The Gaussian process (GP) modeling technique is first utilized to build a hybrid surrogate model by fusing data sources with different fidelity levels. To reduce the number of low- and high-fidelity data, an adaptive hybrid learning (AHL) algorithm is then developed to efficiently update the hybrid model. The updated hybrid surrogate model is used for reliability and sensitivity analyses in solving an RBDO problem, which provides a pseudo-optimal solution in the RBMO framework. An optimal solution that meets the reliability targets can be achieved by sequentially performing the adaptive hybrid learning at the iterative pseudo-optimal designs and solving RBDO problems. The effectiveness of the proposed framework is demonstrated through three case studies. This article is available in the ASME Digital Collection at .

Interface stiffness is an important factor influencing the performance of mechanical products. Uncertain factors affect the interface stiffness and stability in the process of product design, manufacture, and operation. How to reduce the impact of uncertain factors on the interface stiffness is a vital problem in interface design. In this paper, a robust optimal design method is proposed for mechanical interfaces considering uncertain factors, which combines the finite element simulation, experiment, and optimization to reduce the sensitivity of interface stiffness to uncertain factors. The proposed interface design method provides an effective way to improve the interface stiffness under uncertain conditions. In order to validate the proposed method, the bolted connection structure of a flange is applied as an example. The interface stiffness of the flange is selected as an optimization target, and the Gaussian process regression is used to construct a two-layer optimal model of the objective function for the design and uncertain parameters. When experimental and optimization results differ significantly, the Kalman filter is used to provide the feedback for the optimization results until the results meet requirements. The final results show that the optimized mechanical interface stiffness is increased by 15.5%, and the error between the optimized prediction and experimental results is within 1% after three times experimental validation and feedback adjustment. This article is available in the ASME Digital Collection at .

For many data-driven reliability problems, the population is not homogeneous; i.e., its statistics are not described by a unimodal distribution. Also, the interval of observation may not be long enough to capture the failure statistics. A limited failure population (LFP) consists of two subpopulations, a defective and a nondefective one, with well-separated modes of the two underlying distributions. In reliability and warranty forecasting applications, the estimation of the number of defective units and the estimation of the parameters of the underlying distribution are very important. Among various estimation methods, the maximum likelihood estimation (MLE) approach is the most widely used. Its likelihood function, however, is often incomplete, resulting in an erroneous statistical inference. In this paper, we estimate the parameters of a LFP analytically using a rational function fitting (RFF) method based on the Weibull probability plot (WPP) of observed data. We also introduce a censoring factor (CF) to assess how sufficient the number of collected data is for statistical inference. The proposed RFF method is compared with existing MLE approaches using simulated data and data related to automotive warranty forecasting. This article is available in the ASME Digital Collection at .

Statistical volume elements (SVEs) are used to homogenize fracture strength of rock, based on the microcrack statistics of a real-world Yuen-Long marble sample. The small size of SVEs enables maintaining inhomogeneities in fracture properties with lower computational cost compared to methods that explicitly model microcracks at macroscale. Maintaining inhomogeneity is important to capture realistic fracture patterns in rock as a quasi-brittle material. Uniaxial tensile, uniaxial compressive, and shear strengths are derived for arbitrary angle for loading and orientation of a single crack by using the linear elastic fracture mechanics (LEFM) method and incorporating frictional effects. Mesoscopic fracture strength fields are generated for different strengths and angle of loading by traversing the spatial domain with circular SVEs. Increasing the SVE size smoothens the spatial inhomogeneity and angular anisotropy of homogenized strengths. Spatial and angular covariance functions of the random fields are obtained to demonstrate how fracture strength varies in space and by changing the angle of loading. Two isotropic and anisotropic rock domains are studied and shown to have very different single- and two-point statistics. Macroscopic fracture simulations by an asynchronous spacetime discontinuous Galerkin (aSDG) method demonstrate that most macroscopic cracks for the anisotropic domain are aligned with the weakest strength planes. This article is available in the ASME Digital Collection at .

The manual for assessing safety hardware (MASH) defines crash tests to assess the impact performance of highway safety features in frontal and oblique impact events. Within MASH, the risk of injury to the occupant is assessed based on a “flail-space” model that estimates the average deceleration that an unrestrained occupant would experience when contacting the vehicle interior in a MASH crash test and uses the parameter as a surrogate for injury risk. MASH occupant risk criteria, however, are considered conservative in their nature, due to the fact that they are based on unrestrained occupant accelerations. Therefore, there is potential for increasing the maximum limits dictated in MASH for occupant risk evaluation. A frontal full-scale vehicle impact was performed with inclusion of an instrumented anthropomorphic test device (ATD). The scope of this study was to investigate the performance of the flail space model (FSM) in a full-scale crash test compared to the instrumented ATD recorded forces which can more accurately predict the occupant response during a collision event. Additionally, a finite element (FE) model was developed and calibrated against the full-scale crash test. The calibrated model can be used to perform parametric simulations with different testing conditions. Results obtained through this research will be considered for better correlation between vehicle accelerations and occupant injury. This becomes extremely important for designing and evaluating barrier systems that must fit within geometrical site constraints, which do not provide adequate length to redirect test vehicles according to MASH conservative evaluation criteria. This article is available in the ASME Digital Collection at .

In this paper, a new model is proposed for system degradation evaluation under sliding wear failure mechanism. This model estimates the material loss with respect to progression of sliding distance. This model is generated by considering physical and geometrical aspects of the system which is under wear failure mechanism. Several sets of experimental data are used for validation of the presented model. These experimental data are related to the pin-on-disk test including initially conformal and nonconformal contacts. A dataset of the pin-on-disk test by ASTM-G99 standard is used for additional model validation. The rail system data are employed for validation of the model in the practical systems. Uncertainty is analyzed by Monte Carlo simulation to determine the variations of the predicted material loss. Finally, the reliability assessment of these systems is performed. This article is available in the ASME Digital Collection at .