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IN THIS ISSUE

### Editorial

ASME J. Risk Uncertainty Part B. 2017;4(2):020201-020201-2. doi:10.1115/1.4038592.
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Topics: Risk
Commentary by Dr. Valentin Fuster

### Research Papers

ASME J. Risk Uncertainty Part B. 2017;4(2):021001-021001-4. doi:10.1115/1.4037866.

This paper explores an infrequently encountered hazard associated with liquid fuel tanks on gasoline-powered equipment using unvented fuel tanks. Depending on the location of fuel reserve tanks, waste heat from the engine or other vehicle systems can warm the fuel during operation. In the event that the fuel tank is not vented and if the fuel is sufficiently heated, the liquid fuel may become superheated and pose a splash hazard if the fuel cap is suddenly removed. Accident reports often describe the ejection of liquid as a geyser. This geyser is a transient, two-phase flow of flashing liquid. This could create a fire hazard and result in splashing flammable liquid onto any bystanders. Many existing fuel tank systems are vented to ambient through a vented tank cap. It has been empirically determined that the hazard can be prevented by limiting fuel tank gauge pressure to 10 kPa (1.5 psi). However, if the cap does not vent at an adequate rate, pressure in the tank can rise and the fuel can become superheated. This phenomenon is explored here to facilitate a better understanding of how the hazard is created. The nature of the hazard is explained using thermodynamic concepts. The differences in behavior between a closed system and an open system are discussed and illustrated through experimental results obtained from two sources: experiments with externally heated fuel containers and operation of a gasoline-powered riding lawn mower. The role of the vented fuel cap in preventing the geyser phenomenon is demonstrated.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2017;4(2):021002-021002-12. doi:10.1115/1.4037122.

Loosely interconnected cooperative systems such as cable robots are particularly susceptible to uncertainty. Such uncertainty is exacerbated by addition of the base mobility to realize reconfigurability within the system. However, it also sets the ground for predictive base reconfiguration in order to reduce the uncertainty level in system response. To this end, in this paper, we systematically quantify the output wrench uncertainty based on which a base reconfiguration scheme is proposed to reduce the uncertainty level for a given task (uncertainty manipulation). Variations in the tension and orientation of the cables are considered as the primary sources of the uncertainty responsible for nondeterministic wrench output on the platform. For nonoptimal designs/configurations, this may require complex control structures or lead to system instability. The force vector corresponding to each agent (e.g., pulley and cable) is modeled as random vector whose magnitude and orientation are modeled as random variables with Gaussian and von Mises distributions, respectively. In a probabilistic framework, we develop the closed-form expressions of the means and variances of the output force and moment given the current state (tension and orientation of the cables) of the system. This is intended to enable the designer to efficiently characterize an optimal configuration (location) of the bases in order to reduce the overall wrench fluctuations for a specific task. Numerical simulations as well as real experiments with multiple iRobots are performed to demonstrate the effectiveness of the proposed approach.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2017;4(2):021003-021003-11. doi:10.1115/1.4037519.

The paper treats the important problem related to risk controlled by the simultaneous presence of critical events, randomly appearing on a time interval and shows that the expected time fraction of simultaneously present events does not depend on the distribution of events durations. In addition, the paper shows that the probability of simultaneous presence of critical events is practically insensitive to the distribution of the events durations. These counter-intuitive results provide the powerful opportunity to evaluate the risk of overlapping of random events through the mean duration times of the events only, without requiring the distributions of the events durations or their variance. A closed-form expression for the expected fraction of unsatisfied demand for random demands following a homogeneous Poisson process in a time interval is introduced for the first time. In addition, a closed-form expression related to the expected time fraction of unsatisfied demand, for a fixed number of consumers initiating random demands with a specified probability, is also introduced for the first time. The concepts stochastic separation of random events based on the probability of overlapping and the average overlapped fraction are also introduced. Methods for providing stochastic separation and optimal stochastic separation achieving balance between risk and cost of risk reduction are presented.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2017;4(2):021004-021004-7. doi:10.1115/1.4037219.

In this study, stochastic analysis is aimed for space structures (satellite in low earth orbit, made of aluminum 2024-T3), with the focus on fatigue failure. Primarily, the deterministic fatigue simulation is conducted using Walker and Forman models with constant amplitude loading. Deterministic crack growth was numerically simulated by the authors developed algorithm and is compared with commercial software for accuracy verification as well as validation with the experimental data. For the stochastic fatigue analysis of this study, uncertainty is estimated by using the Monte Carlo simulation. It is observed that by increasing the crack length, the standard deviation (the measure of uncertainty) increases. Also, it is noted that the reduction in stress ratio has the similar effect. Then, stochastic crack growth model, proposed by Yang and Manning, is employed for the reliability analysis. This model converts the existing deterministic fatigue models to stochastic one by adding a random coefficient. Applicability of this stochastic model completely depends on accuracy of base deterministic function. In this study, existing deterministic functions (power and second polynomial) are reviewed, and three new functions, (i) fractional, (ii) global, and (iii) exponential, are proposed. It is shown that the proposed functions are potentially used in the Yang and Manning model for better results.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2017;4(2):021005-021005-8. doi:10.1115/1.4037328.

An abnormal operating effect can be caused by different faults, and a fault can cause different abnormal effects. An information fusion model, with hybrid-type fusion frame, is built in this paper, so as to solve this problem. This model consists of data layer, feature layer and decision layer, based on an improved Dempster–Shafer (D-S) evidence algorithm. After the data preprocessing based on event reasoning in data layer and feature layer, the information will be fused based on the new algorithm in decision layer. Application of this information fusion model in fault diagnosis is beneficial in two aspects, diagnostic applicability and diagnostic accuracy. Additionally, this model can overcome the uncertainty of information and equipment to increase diagnostic accuracy. Two case studies are implemented by this information fusion model to evaluate it. In the first case, fault probabilities calculated by different methods are adopted as inputs to diagnose a fault, which is quite different to be detected based on the information from a single analytical system. The second case is about sensor fault diagnosis. Fault signals are planted into the measured parameters for the diagnostic system, to test the ability to consider the uncertainty of measured parameters. The case study result shows that the model can identify the fault more effectively and accurately. Meanwhile, it has good expansibility, which may be used in more fields.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2017;4(2):021006-021006-12. doi:10.1115/1.4037353.

Vibration induced fatigue (VIF) failure of topside piping is one of the most common causes of the hydrocarbon release on offshore oil and gas platforms operating in the North Sea region. An effective inspection plan for the identification of fatigue critical piping locations has the potential to minimize the hydrocarbon release. One of the primary challenges in preparation of inspection program for offshore piping is to identify the fatigue critical piping locations. At present, the three-staged risk assessment process (RAP) given in the Energy Institute (EI) guidelines is used by inspection engineers to determine the likelihood of failure (LoF) of process piping due to VIF. Since the RAP is afflicted by certain drawbacks, this paper presents an alternative risk assessment approach (RAA) to RAP for identification and prioritization of fatigue critical piping locations. The proposed RAA consists of two stages. The first stage involves a qualitative risk assessment using fuzzy-analytical hierarchy process (FAHP) methodology to identify fatigue critical systems (and the most dominant excitation mechanism) and is briefly discussed in the paper. The fatigue critical system identified during stage 1 of RAA undergoes further assessment in the second stage of the RAA. This stage employs a fuzzy-logic method to determine the LoF of the mainline piping. The outcome of the proposed RAA is the categorization of mainline piping, into high, medium, or low risk grouping. The mainline piping in the high-risk category is thereby prioritized for inspection. An illustrative case study demonstrating the usability of the proposed RAA is presented.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2017;4(2):021007-021007-10. doi:10.1115/1.4037647.

This study focuses on the effect of skull fracture on the load transfer to the head for low-velocity frontal impact of the head against a rigid wall or being impacted by a heavy projectile. The skull was modeled as a cortical–trabecular–cortical-layered structure in order to better capture the skull deformation and consequent failure. The skull components were modeled with an elastoplastic with failure material model. Different methods were explored to model the material response after failure, such as eroding element technique, conversion to fluid, and conversion to smoothed particle hydrodynamic (SPH) particles. The load transfer to the head was observed to decrease with skull fracture.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2017;4(2):021008-021008-15. doi:10.1115/1.4037485.

In the early development phase of complex technical systems, uncertainties caused by unknown design restrictions must be considered. In order to avoid premature design decisions, sets of good designs, i.e., designs which satisfy all design goals, are sought rather than one optimal design that may later turn out to be infeasible. A set of good designs is called a solution space and serves as target region for design variables, including those that quantify properties of components or subsystems. Often, the solution space is approximated, e.g., to enable independent development work. Algorithms that approximate the solution space as high-dimensional boxes are available, in which edges represent permissible intervals for single design variables. The box size is maximized to provide large target regions and facilitate design work. As a result of geometrical mismatch, however, boxes typically capture only a small portion of the complete solution space. To reduce this loss of solution space while still enabling independent development work, this paper presents a new approach that optimizes a set of permissible two-dimensional (2D) regions for pairs of design variables, so-called 2D-spaces. Each 2D-space is confined by polygons. The Cartesian product of all 2D-spaces forms a solution space for all design variables. An optimization problem is formulated that maximizes the size of the solution space, and is solved using an interior-point algorithm. The approach is applicable to arbitrary systems with performance measures that can be expressed or approximated as linear functions of their design variables. Its effectiveness is demonstrated in a chassis design problem.

Topics: Space , Design , Optimization
Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2017;4(2):021009-021009-8. doi:10.1115/1.4037970.

The probabilistic stress-number of cycles curve (P-S-N curve) approach is widely accepted for describing the fatigue strengths of materials. It is also a widely accepted fatigue theory for determining the reliability of a component under fatigue loadings. However, it is an unsolved issue in the P-S-N curve approach that the calculation of reliability of a component under several distributed cyclic numbers at the corresponding constant cyclic stress levels. Based on the commonly accepted concept of the equivalent fatigue damage, this paper proposes a new method to determine the reliability of the component under several distributed cyclic numbers at the corresponding constant cyclic stress levels. Four examples including two validation examples will be provided to demonstrate how to implement the proposed method for reliability calculation under such fatigue cyclic loading spectrum. The relative errors in validation examples are very small. So, the proposed method can be used to evaluate the reliability of a component under several distributed cyclic number at different stress levels.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2018;4(2):021010-021010-9. doi:10.1115/1.4039016.

The early detection of a kick and mitigation with appropriate well control actions can minimize the risk of a blowout. This paper proposes a downhole monitoring system, and presents a dynamic numerical simulation of a compressible two-phase flow to study the kick dynamics at downhole during drilling operation. This approach enables early kick detection and could lead to the development of potential blowout prevention strategies. A pressure cell that mimics a scaled-down version of a downhole is used to study the dynamics of a compressible two-phase flow. The setup is simulated under boundary conditions that resemble realistic scenarios; special attention is given to the transient period after injecting the influx. The main parameters studied include pressure gradient, raising speed of a gas kick, and volumetric behavior of the gas kick with respect to time. Simulation results exhibit a sudden increase of pressure while the kick enters and volumetric expansion of gas as it flows upward. This improved understanding helps to develop effective well control and blowout prevention strategies. This study confirms the feasibility and usability of an intelligent drill pipe as a tool to monitor well conditions and develop blowout risk management strategies.

Commentary by Dr. Valentin Fuster
Select Articles from Part A: Civil Engineering

### Technical Papers

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2018;4(2):. doi:10.1061/AJRUA6.0000965.
Abstract

Abstract  Stochastic soil modeling aims to provide reasonable mean, variance, and spatial correlation of soil properties with quantified uncertainty. Because of difficulties in integrating limited and imperfect prior knowledge (i.e., epistemic uncertainty) with observed site-specific information from tests (i.e., aleatoric uncertainty), a reasonably accurate estimate of the spatial correlation is significantly challenging. Possible reasons include (1) only sparse data being available (i.e., one-dimensional observations are collected at selected locations); and (2) from a physical point of view, the formation process of soil layers is considerably complex. This paper develops a Gaussian Markov random field (GMRF)-based modeling framework to describe the spatial correlation of soil properties conditional on observed electric cone penetration test (CPT) soundings at multiple locations. The model parameters are estimated using a novel stochastic partial differential equation (SPDE) approach and a fast Bayesian algorithm using the integrated nested Laplace approximation (INLA). An existing software library is used to implement the SPDE approach and Bayesian estimation. A real-world example using 185 CPT soundings from Alameda County, California is provided to demonstrate the developed method and examine its performance. The analyzed results from the proposed model framework are compared with the widely accepted covariance-based kriging method. The results indicate that the new approach generally outperforms the kriging method in predicting the long-range variability. In addition, a better understanding of the fine-scale variability along the depth is achieved by investigating one-dimensional residual processes at multiple locations.

Topics:
Modeling , Soil
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2018;4(2):. doi:10.1061/AJRUA6.0000950.
Abstract

Abstract  Metamodeling techniques have been widely used as substitutes for high-fidelity and time-consuming models in various engineering applications. Examples include polynomial chaos expansions, neural networks, kriging, and support vector regression (SVR). This paper attempts to compare the latter two in different case studies so as to assess their relative efficiency on simulation-based analyses. Similarities are drawn between these two metamodel types, leading to the use of anisotropy for SVR. Such a feature is not commonly used in the SVR-related literature. Special care was given to a proper automatic calibration of the model hyperparameters by using an efficient global search algorithm, namely the covariance matrix adaptation–evolution scheme. Variants of these two metamodels, associated with various kernel and autocorrelation functions, were first compared on analytical functions and then on finite element–based models. From the comprehensive comparison, it was concluded that anisotropy in the two metamodels clearly improves their accuracy. In general, anisotropic $L2$-SVR with the Matérn kernels was shown to be the most effective metamodel.

Topics:
Structural engineering , Support vector machines
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2018;4(2):. doi:10.1061/AJRUA6.0000956.
Abstract

Abstract  Corrosion is one of the main causes of pipeline failure, which can have large social, economic, and environmental consequences. To mitigate this risk, pipeline operators perform regular inspections and repairs. The results of the inspections aid decision makers in determining the optimal maintenance strategy. However, there are many possible maintenance strategies, and a large degree of uncertainty, leading to difficult decision making. This paper develops a framework to inform the decision of whether it is better over the long term to continuously repair defects as they become critical or to just replace entire segments of the pipeline. The method uses a probabilistic analysis to determine the expected number of failures for each pipeline segment. The expected number of failures informs the optimal decision. The proposed framework is tailored toward mass amounts of in-line inspection data and multiple pipeline segments. A numerical example of a corroding upstream pipeline illustrates the method.

Topics:
Maintenance , Pipeline systems , Decision making
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2018;4(2):. doi:10.1061/AJRUA6.0000960.
Abstract

Abstract  This paper is focused on the development of an efficient system-level reliability-based design optimization strategy for uncertain wind-excited building systems characterized by high-dimensional design variable vectors (in the order of hundreds). Indeed, although a number of methods have been proposed over the last 15 years for the system-level reliability-based design optimization of building systems subject to stochastic excitation, few have treated problems characterized by more than a handful of design variables. This limits their applicability to practical problems of interest, such as the design optimization of high-rise buildings. To overcome this limitation, a simulation-based method is proposed in this work that is capable of solving reliability-based design optimization problems characterized by high-dimensional design variable vectors while considering system-level performance constraints. The framework is based on approximately decoupling the reliability analysis from the optimization loop through the definition of a system-level subproblem that can be fully defined from the results of a single simulation carried out in the current design point. To demonstrate the efficiency, practicality, and strong convergence properties of the proposed framework, a 40-story uncertain planar frame defined by 200 design variables is optimized under stochastic wind excitation.

Topics:
Reliability-based optimization , Wind
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2018;4(2):. doi:10.1061/AJRUA6.0000964.
Abstract

Abstract  Fragility functions define the probability of meeting or exceeding some damage measure (DM) for a given level of engineering demand (e.g., base shear) or hazard intensity measure (IM; e.g., wind speed, and peak ground acceleration). Empirical fragility functions specifically refer to fragility functions that are developed from posthazard damage assessments, and, as such, they define the performance of structures or systems as they exist in use and under true natural hazard loading. This paper describes major sources of epistemic uncertainty in empirical fragility functions for building performance under natural hazard loading, and develops and demonstrates methods for quantifying these uncertainties using Monte Carlo simulation methods. Uncertainties are demonstrated using a dataset of 1,241 residential structures damaged in the May 22, 2011, Joplin, Missouri, tornado. Uncertainties in the intensity measure (wind speed) estimates were the largest contributors to the overall uncertainty in the empirical fragility functions. With a sufficient number of samples, uncertainties because of potential misclassification of the observed damage levels and sampling error were relatively small. The methods for quantifying uncertainty in empirical fragility functions are demonstrated using tornado damage observations, but are applicable to any other natural hazard as well.

Topics:
Uncertainty , Disasters , Damage assessment