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IN THIS ISSUE

### Research Papers

ASME J. Risk Uncertainty Part B. 2018;4(4):041001-041001-7. doi:10.1115/1.4039243.

As a common type system, multistate weighted k-out-of-n system is of great importance in reliability engineering. The components are usually treated as independent from each other. It is usually not that case in real life and the components are dependent. On the other hand, the performance of the components degrades over time, leading to the change of the components' weight at the same time. As a result, the present paper provides a method to evaluate the dynamic reliability of multistate weighted k-out-of-n: G system with s-dependent components. The degradation of the components follows a Markov process and the components are nonrepairable. Copula function is used to model the s-dependence of the components. The LZ-transform for a discrete-state continuous-time Markov process is combined, and the explicit expression for the survival function and the mean time to failure (MTTF) of the system is obtained. A small electricity generating system is studied based on our method in the illustration, and detailed comparison result is made for dependent case and independent case. Dynamic reliability with varied levels of electricity generation conforming to the actual situation for this generating system is also calculated.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2018;4(4):041002-041002-12. doi:10.1115/1.4039149.

Bayesian networks (BNs) are being studied in recent years for system diagnosis, reliability analysis, and design of complex engineered systems. In several practical applications, BNs need to be learned from available data before being used for design or other purposes. Current BN learning algorithms are mainly developed for networks with only discrete variables. Engineering design problems often consist of both discrete and continuous variables. This paper develops a framework to handle continuous variables in BN learning by integrating learning algorithms of discrete BNs with Gaussian mixture models (GMMs). We first make the topology learning more robust by optimizing the number of Gaussian components in the univariate GMMs currently available in the literature. Based on the BN topology learning, a new multivariate Gaussian mixture (MGM) strategy is developed to improve the accuracy of conditional probability learning in the BN. A method is proposed to address this difficulty of MGM modeling with data of mixed discrete and continuous variables by mapping the data for discrete variables into data for a standard normal variable. The proposed framework is capable of learning BNs without discretizing the continuous variables or making assumptions about their conditional probability densities (CPDs). The applications of the learned BN to uncertainty quantification and model calibration are also investigated. The results of a mathematical example and an engineering application example demonstrate the effectiveness of the proposed framework.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2018;4(4):041003-041003-8. doi:10.1115/1.4039941.

The gear door lock system (GDLS) is a hydraulic and mechatronic system with high degree of complexity and uncertainty, making the performance assessment of the system especially intractable. We develop copula models to estimate the reliability of GDLS with dependent failure modes. Based on the working principle of the GDLS, kinematic and dynamic model with imperfect joints is built in which Latin hypercube sampling (LHS) and kernel smoothing density are utilized to obtain the marginal failure probabilities. Then, copula models are utilized to describe the dependence between the two function failure modes. Furthermore, to be more accurate, mixed copula models are developed. The squared Euclidean distance is adopted to estimate the parameters of the above reliability models. Finally, the Monte Carlo simulation is conducted to evaluate different reliability models.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2018;4(4):041004-041004-7. doi:10.1115/1.4039464.

A series of pedestrian sideswipe impacts were computationally reconstructed; a fast-walking pedestrian was collided laterally with the side of a moving vehicle at 25 km/h or 40 km/h, which resulted in rotating the pedestrian's body axially. Potential severity of traumatic brain injury (TBI) was assessed using linear and rotational acceleration pulses applied to the head and by measuring intracranial brain tissue deformation. We found that TBI risk due to secondary head strike with the ground can be much greater than that due to primary head strike with the vehicle. Further, an “effective” head mass, meff, was computed based upon the impulse and vertical velocity change involved in the secondary head strike, which mostly exceeded the mass of the adult head-form impactor (4.5 kg) commonly used for a current regulatory impact test for pedestrian safety assessment. Our results demonstrated that a sport utility vehicle (SUV) is more aggressive than a sedan due to the differences in frontal shape. Additionally, it was highlighted that a striking vehicle velocity should be lower than 25 km/h at the moment of impact to exclude the potential risk of sustaining TBI, which would be mitigated by actively controlling meff, because meff is closely associated with a rotational acceleration pulse applied to the head involved in the final event of ground contact.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2018;4(4):041005-041005-7. doi:10.1115/1.4039465.

Valuation of transactive energy (TE) systems should be supported by a structured and systematic approach to uncertainty identification, assessment, and treatment in the interest of risk-informed decision making. The proposed approach, a variation of fault tree analysis, is anticipated to support valuation analysts in analyzing conventional and transactive system scenarios. This approach allows for expanding the entire tree up to the level of minute details or collapsing them to a level sufficient enough to get an overview of the problem. Quantification scheme for the described approach lends itself for valuation. The method complements value exchange analysis, simulation, and field demonstration studies. The practicality of the proposed approach is demonstrated through uncertainty assessment of the smart grid interoperability panel peak heat day scenario.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2018;4(4):041006-041006-17. doi:10.1115/1.4039558.

This paper examines the variability of predicted responses when multiple stress–strain curves (reflecting variability from replicate material tests) are propagated through a finite element model of a ductile steel can being slowly crushed. Over 140 response quantities of interest (QOIs) (including displacements, stresses, strains, and calculated measures of material damage) are tracked in the simulations. Each response quantity's behavior varies according to the particular stress–strain curves used for the materials in the model. We desire to estimate or bound response variation when only a few stress–strain curve samples are available from material testing. Propagation of just a few samples will usually result in significantly underestimated response uncertainty relative to propagation of a much larger population that adequately samples the presiding random-function source. A simple classical statistical method, tolerance intervals (TIs), is tested for effectively treating sparse stress–strain curve data. The method is found to perform well on the highly nonlinear input-to-output response mappings and non-normal response distributions in the can crush problem. The results and discussion in this paper support a proposition that the method will apply similarly well for other sparsely sampled random variable or function data, whether from experiments or models. The simple TI method is also demonstrated to be very economical.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2018;4(4):041007-041007-8. doi:10.1115/1.4039467.

The development of robust and adaptable methods of grasping force optimization (GFO) is an important consideration for robotic devices, especially those which are designed to interact naturally with a variety of objects. Along with considerations for the computational efficiency of such methods, it is also important to ensure that a GFO approach chooses forces which can produce a stable grasp even in the presence of uncertainty. This paper examines the robustness of three methods of GFO in the presence of variability in the contact locations and in the coefficients of friction between the hand and the object. A Monte Carlo simulation is used to determine the resulting probability of failure and sensitivity levels when variability is introduced. Two numerical examples representing two common grasps performed by the human hand are used to demonstrate the performance of the optimization methods. Additionally, the method which yields the best overall performance is also tested to determine its consistency when force is applied to the object's center of mass in different directions. The results show that both the nonlinear and linear matrix inequality (LMIs) methods of GFO produce acceptable results, whereas the linear method produces unacceptably high probabilities of failure. Further, the nonlinear method continues to produce acceptable results even when the direction of the applied force is changed. Based on these results, the nonlinear method of GFO is considered to be robust in the presence of variability in the contact locations and coefficients of friction.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2018;4(4):041008-041008-13. doi:10.1115/1.4039784.

Optimal sizing of peak loads has proven to be an important factor affecting the overall energy consumption of heating ventilation and air-conditioning (HVAC) systems. Uncertainty quantification of peak loads enables optimal configuration of the system by opting for a suitable size factor. However, the representation of uncertainty in HVAC sizing has been limited to probabilistic analysis and scenario-based cases, which may limit and bias the results. This study provides a framework for uncertainty representation in building energy modeling, due to both random factors and imprecise knowledge. The framework is shown by a numerical case study of sizing cooling loads, in which uncertain climatic data are represented by probability distributions and human-driven activities are described by possibility distributions. Cooling loads obtained from the hybrid probabilistic–possibilistic propagation of uncertainty are compared to those obtained by pure probabilistic and pure possibilistic approaches. Results indicate that a pure possibilistic representation may not provide detailed information on the peak cooling loads, whereas a pure probabilistic approach may underestimate the effect of uncertain human behavior. The proposed hybrid representation and propagation of uncertainty in this paper can overcome these issues by proper handling of both random and limited data.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2018;4(4):041009-041009-9. doi:10.1115/1.4039471.

A novel uncertainty quantification routine in the genre of adaptive sparse grid stochastic collocation (SC) has been proposed in this study to investigate the propagation of parametric uncertainties in a stall flutter aeroelastic system. In a hypercube stochastic domain, presence of strong nonlinearities can give way to steep solution gradients that can adversely affect the convergence of nonadaptive sparse grid collocation schemes. A new adaptive scheme is proposed here that allows for accelerated convergence by clustering more discretization points in regimes characterized by steep fronts, using hat-like basis functions with nonequidistant nodes. The proposed technique has been applied on a nonlinear stall flutter aeroelastic system to quantify the propagation of multiparametric uncertainty from both structural and aerodynamic parameters. Their relative importance on the stochastic response is presented through a sensitivity analysis.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2018;4(4):041010-041010-9. doi:10.1115/1.4039357.

Go-karts are a common amusement park feature enjoyed by people of all ages. While intended for racing, contact between go-karts does occur. To investigate and quantify the accelerations and forces which result from contact, 44 low-speed impacts were conducted between a stationary (target) and a moving (bullet) go-kart. The occupant of the bullet go-kart was one of two human volunteers. The occupant of the target go-kart was a Hybrid III 50th percentile male anthropomorphic test device (ATD). Impact configurations consisted of rear-end impacts, frontal impacts, side impacts, and oblique impacts. Results demonstrated high repeatability for the vehicle performance and occupant response. Go-kart accelerations and speed changes increased with increased impact speed. Impact duration and restitution generally decreased with increased impact speed. All ATD acceleration, force, and moment values increased with increased impact speed. Common injury metrics such as the head injury criterion (HIC), $Nij$, and $Nkm$ were calculated and were found to be below injury thresholds. Occupant response was also compared to published activities of daily living data.

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(4):. doi:10.1061/AJRUA6.0000982.
Abstract

Abstract  The far-reaching consequences of train derailments have been a major concern to industry and government despite their relatively low occurrence. These consequences include injury, loss of life and property, interruption of services, and destruction of the environment. Thus, it is imperative to carefully examine train derailment severity. The majority of extant literature has failed to consider the multivariate nature of derailment severity and has instead focused mainly on only one severity outcome, namely, the number of derailed cars. However, it is also important to analyze the monetary damage incurred by railroads during derailments. In this paper, a joint mixed copula-based model for derailed cars and monetary damage is presented for the combined analysis of their relationship with a set of covariates that might affect both outcomes. Marginal generalized regression linear models are combined with a bivariate copula, which characterizes the dependence between the two variables. Copulas also address endogeneity due to similar unobserved or omitted variables that may affect both response variables. The copula-based regression model was found to be more appropriate than the independent multivariate regression model. The incorporation of the copula to characterize the dependence resulted in a greater effect on the dispersion estimate than the point estimates. Derailment speed was found to have the most pronounced effect on both response variables. However, it was found to have a greater impact on monetary damage than the number of derailed cars.

Topics:
Regression models , Trains
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2018;4(4):. doi:10.1061/AJRUA6.0000986.
Abstract

Abstract  Estimating the safety effects of emerging or future technology based on expert acquisitions is challenging because the accumulated judgment is at risk to be biased and imprecise. Therefore, this semiquantitative study is proposing and demonstrating an upgraded bowtie analysis for safety effect assessments that can be performed without the need for expert acquisition. While bowtie analysis is commonly used in, for example, process engineering, it is novel in road traffic safety. Four crash case studies are completed using bowtie analysis, letting the input parameters sequentially vary over the entire range of possible expert opinions. The results suggest that only proactive safety measures estimated to decrease the probability of specific crash risk factors to at least “very improbable” can perceptibly decrease crash probability. Further, the success probability of a reactive measure must be at least “moderately probable” to reduce the probability of a serious or fatal crash by half or more. This upgraded bowtie approach allows the identification of (1) the sensitivity of the probability of a crash and its consequences to expert judgment used in the bowtie model and (2) the necessary effectiveness of a chosen safety measure allowing adequate changes in the probability of a crash and its consequences.

Topics:
Safety
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2018;4(4):. doi:10.1061/AJRUA6.0000989.
Abstract

Abstract  This paper presents a comprehensive statistical study on crack and leakage defects of road tunnel linings in China with newly proposed quantitative indexes that can be easily evaluated by robotic inspection techniques. These indexes are crack density, average crack length, nominal crack width, leakage density, and leakage state index. A database covering the defects of 116 road tunnels in China is built from on-site inspection and literatures. Key factors for each evaluation index are determined by analysis of variance and partial correlation analysis. Nonstructural crack is found to be the dominant type of cracking. Temperature-shrinkage stress is the main reason for cracking in cold-region tunnels. Seepage is the most common type of leakage. The underground water source plays a crucial role in the formation of leakage. Probabilistic modeling of the defect is carefully investigated: crack density and leakage state index can approximately follow normal distribution. Leakage density and average crack length are lognormally distributed, whereas nominal crack width of composite-lined new tunnels follows exponential distribution. Bayesian updating is carried out for nonparametric estimation in the case in which the conventional maximum likelihood method is not applicable due to data deficiency. The proposed probabilistic models are validated by on-site inspection and theoretical checking computation.

Topics:
Concretes , Fracture (Materials) , China , Roads , Tunnels , Leakage
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2018;4(4):. doi:10.1061/AJRUA6.0000993.
Abstract

Abstract  Managing geotechnical risk in design-build (DB) projects is complicated because the contract is awarded before a thorough subsurface investigation is conducted, making geotechnical uncertainty high during bid preparation. Typically, owners allocate geotechnical risk between themselves and competing design-builders in the DB project’s request of proposals (RFP), and the perceived RFP risk profile is reflected in the bid prices proposed by competing DB teams. The level of perceived geotechnical uncertainty is exacerbated when the RFP’s geotechnical content is either inadequate or ambiguous, a condition that may not have been recognized by the RFP’s authors. Hence, the purpose of this paper is to understand the influence of differing perceptions on the DB project’s pricing. The paper analyzes the difference in perceived DB geotechnical risk for 27 common geotechnical risk factors identified in a survey of state department of transportation (DOT) geotechnical engineers and professionals of the DB industry. Those geotechnical risk factors were rated on the basis of frequency and impact by 46 DOT and industry practitioners, and the results are synthesized using importance index theory. The study finds a statistically significant difference in the perceptions of importance of geotechnical risk factors between public agencies and the DB industry. The average perceived difference in the rated factors is nearly 12%. This paper recommends that the perceptions of geotechnical risk be aligned before the contract award using progressive DB procurement or after the award using a scope validation period to provide an opportunity to share the geotechnical risks.

Topics:
Design , Highways , Geotechnical risk
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2018;4(4):. doi:10.1061/AJRUA6.0000991.
Abstract