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Research Papers

ASME J. Risk Uncertainty Part B. 2017;3(4):041001-041001-16. doi:10.1115/1.4036309.

The potential benefits of a safety program are generally only realized after an incident has occurred. Resource allocation in an organization's safety program has the imperative task of balancing costs and often unrealized benefits. Management can be wary to allocate additional resources to a safety program because it is difficult to estimate the return on investment, especially since the returns are a set of negative outcomes not manifested. One way that safety professionals can provide an estimate of potential return on investment is to forecast how the organizations incident rate can be affected by implementing different resource allocation strategies and what the expected incident rate would have been without intervention. This study evaluates forecasting methods used to predict incidents against one another against a common definition of performance accuracy to identify the method that would be the most applicable to use as part of a safety resource allocation model. By identifying the most accurate forecasting method, the uncertainty of which method a safety professional should utilize for incident rate prediction is reduced. Incident data from the Mine Safety and Health Administration (MSHA) was used to make short- and long-term forecasts. The performance of each of these methods was evaluated against one another to ascertain which method has the highest level of accuracy, lowest bias, and best complexity-adjusted goodness-of-fit metrics. The double exponential smoothing and auto-regressive moving average (ARMA) statistical forecasting methods provided the most accurate incident rate predictions.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2017;3(4):041002-041002-7. doi:10.1115/1.4036064.

Corrosion degradation is a common problem for boiler tubes in power plants, resulting in an unscheduled plant shutdown. In this research, degradation of the corrosion is investigated for boiler tubes by estimating the corrosion lifetime. A special focus is made on the corrosion failures, important failure modes, and mechanisms for the metallic boiler tubes via failure modes and effect analysis (FMEA) method, thereby evaluating the pitting corrosion as the most common failure mode in the tubes. Majority of the available approaches estimates lifetime of the pitting corrosion by deterministic approaches, in which the results are valid only for limited conditions. In order to improve deficiencies of available models, a stochastic method is proposed here to study the corrosion life. The temporal behavior of the metal degradation is analyzed in different conditions through the developed approach, and a proper degradation model is selected. Uncertainty intervals/distributions are determined for some of the model parameters. The deterministic model is converted to a probabilistic model by taking into account the variability of the uncertain input parameters. The model is simulated using Monte Carlo method via simple sampling. The result of the life estimation is updated by the Bayesian framework using Monte Carlo Markov Chain. Finally, for the element that is subjected to the pitting corrosion degradation, the life distribution is obtained. The modeling results show that the pitting corrosion has stochastic behavior with lognormal distribution as proper fit for the pitting corrosion behavior. In order to validate the results, the estimations were compared with the power plant field failure data.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2017;3(4):041003-041003-13. doi:10.1115/1.4035867.

Sketches can be categorized as personal, shared, persuasive, and handover sketches. Depending on each category, their level of ambiguity also varies. The applications of sketches include conceptual design, eliciting user preferences, shape retrieval, and sketch-based modeling (SBM). There is a need for quantification of uncertainty in sketches in mapping of sketches to three-dimensional (3D) models in sketch-based modeling, in eliciting user preferences, and in tuning the level of uncertainty in sketches at the conceptual design stage. This paper investigates the role of probability of importance in quantifying the level of uncertainty in sketches by raising the following three research questions: How are the features in a sketch ranked? What is the probability of importance of features in a sketch? What is the level of uncertainty in a sketch? This paper presents an improved framework for uncertainty quantification in sketches. The framework is capable of identifying and ranking the features in the sketch, determining their probability of importance, and finally quantifying the level of uncertainty in the sketch. Ranking the features of a sketch is performed by a hierarchical approach, whereas probability of importance is determined by assessing the probability of likeliness using a shape matching approach and a probability transformation. Quantification of uncertainty is accomplished by using the principle of normalization of entropy. A case study of a bicycle sketch is used to demonstrate that the framework eliminates the need of expert input in assessment of uncertainty in sketches and, hence, can be used by design practitioners with limited experience.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2017;3(4):041004-041004-7. doi:10.1115/1.4035438.

The reliable prediction and diagnosis of abnormal events provide much needed guidance for risk management. The traditional Bayesian network (traditional BN) has been used to dynamically predict and diagnose abnormal events. However, its inherent limitation caused by discrete categorization of random variables degrades the assessment reliability. This paper applied a continuous Bayesian network (CBN)-based model to reduce the above-mentioned limitation. To compute complex posterior distributions of CBN, the Markov chain Monte Carlo method (MCMC) was used. A case study was conducted to demonstrate the application of CBN, based on which a comparative analysis of the traditional BN and CBN was presented. This work highlights that the use of CBN can overcome the drawbacks of traditional BN to make dynamic prediction and diagnosis analysis more reliable.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2017;3(4):041005-041005-8. doi:10.1115/1.4036310.

Energy dynamics in buildings are inherently stochastic in nature due to random fluctuations from various factors such as heat gain (including the solar) and ambient temperature. This paper proposes a theoretical framework for stochastic modeling of building thermal dynamics as well as its analytical solution strategies. Both the external temperature and the heat gain are modeled as stochastic processes, composed of a periodic (daily) mean-value function and a zero-mean deviation process obtained as the output process of a unit Gaussian white noise passing through a rational filter. Based on the measured climate data, the indicated mean-value functions and rational filters have been identified for different months of a year. Stochastic differential equations in the state vector form driven by white noise processes have been established, and analytical solutions for the mean-value function and covariance matrix of the state vector are obtained. This framework would allow a simple and efficient way to carry out predictions and parametric studies on energy dynamics of buildings with random and uncertain climate effects. It would also provide a basis for the robust design of energy efficient buildings with predictive controllers.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2017;3(4):041006-041006-5. doi:10.1115/1.4036663.

The development of a crashworthy landing gear is presented based on the civil regulations and the military specifications. For this, two representative crashworthy requirements are applied to helicopter landing gear design: the nose gear is designed to collapse in a controlled manner so that it does not penetrate the cabin and cause secondary hazards, and the main gear has to absorb energy as much as possible in crash case to decelerate the aircraft. To satisfy the requirements, the collapse mechanism triggered by shear-pin failure and the shock absorber using blow-off valve (BOV) is implemented in the nose and main gear, respectively. The crash performance of landing gear is demonstrated by drop tests. In the tests, performance data such as ground reaction loads and shock absorber stroke are measured and crash behaviors are recorded by high-speed camera. The test data show a good agreement with the prediction by simulation model, which proves the validity of the design and analysis.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2017;3(4):041007-041007-10. doi:10.1115/1.4036662.

A three-dimensional (3D) mass evacuation simulation using precise kinematic digital human (KDH) models and an experimental study are discussed. The flooding associated with the large tsunami caused by the Great East Japan Earthquake on Mar. 11, 2011, was responsible for more than 90% of the disaster casualties. Unfortunately, it is expected that other huge tsunamis could occur in Japan coastal areas if an earthquake with magnitude greater than eight occurs along the Nankai Trough. Therefore, recent disaster prevention plans should include evacuation to higher buildings, elevated ground, and constructed tsunami evacuation towers. In this study, evacuation simulations with 500 KDHs were conducted. The simulations consisted of several subgroups of KDHs. It is shown that the possible evacuation path of each group should be carefully determined to minimize the evacuation time. Several properties such as evacuee motion characteristics of KDHs, number of evacuees, exit gates, and number of injured persons were carefully considered in the simulations. Evacuee motion was also experimentally investigated by using a multistoried building to replicate the structure of an actual tsunami evacuation tower that could accommodate approximately 120 evacuees. The experimental results suggest that an appropriately divided group population could effectively reduce the overall group evacuation time. The results also suggest that fatigue due to walking during evacuation adversely affects the total evacuation time, especially in the ascent of stairways. The experimental data can be used to obtain more accurate simulations of mass evacuation.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2017;3(4):041008-041008-9. doi:10.1115/1.4036990.

Finite element (FE) models are commonly used for automotive body design. However, even with increasing speed of computers, the FE-based simulation models are still too time-consuming when the models are complex. To improve the computational efficiency, support vector regression (SVR) model, a potential approximate model, has been widely used as the surrogate of FE model for crashworthiness optimization design. Generally, in the traditional SVR, when dealing with nonlinear data, the single kernel function-based projection cannot fully cover data distribution characteristics. In order to eliminate the application limitations of single kernel SVR, a method for reliability-based design optimization (RBDO) based on mixed-kernel-based SVR (MKSVR) is proposed in this research. The mixed kernel is constructed based on the linear combination of radial basis kernel function and polynomial kernel function. Through the particle swarm optimization (PSO) algorithm, the parameters of the mixed kernel SVR are optimized. The proposed method is demonstrated through a representative analytical RBDO problem and a vehicle lightweight design problem. And the comparitive studies for SVR and MKSVR in application indicate that MKSVR surpasses SVR in model accuracy.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2017;3(4):041009-041009-9. doi:10.1115/1.4037210.

The conventional research of risk communication centers on how scientific community can improve trust and credibility in public perception, enhance public understanding of risks, and change public behaviors to conform to technocratic values. More recently, the emphasis of risk communication has evolved from conveying scientific data and risk information to establishing effective information flows. It has been recognized that establishing two-way communication channels among experts, governments, corporate, and general public is important to build trust relationship. With conflicting interests and coordination motive among stakeholders, the societal aspects of risk communication need to be considered. In this paper, a mathematical model of social value of risk information is proposed to explicitly incorporate factors such as public and private information, personal bias, knowledge, and social behavior in risk communication. Uncertainties associated with the perceived risks due to both the lack of knowledge and individual differences in population are considered in the proposed model. The impacts of precision and accuracy of risk information as well as subjective bias on social welfare are characterized. Some of the model predictions on the effectiveness of communication are verified with the observations in other's survey studies. The proposed model could potentially be used to help devise risk communication strategies and policies. Its use is demonstrated in a case study of Fukushima nuclear accident.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2017;3(4):041010-041010-9. doi:10.1115/1.4037123.

This paper proposes a new reliability optimization allocation for multifunction systems with multistate units based on goal-oriented (GO) methodology. First, this optimization allocation method is expounded in terms of establishing GO model, establishing reliability optimization allocation model, and solving algorithm. Then its process is formulated. Finally, the new method is applied in reliability optimization allocation of power-shift steering transmission (PSST), whose goal is to minimize the system cost. The results analysis shows that the system costs for different operation times turn to a relatively stable value, and the allocated reliability indices of unit are satisfied with engineering requirements. All in all, this new optimization allocation method can not only obtain the reasonable allocation results quickly and effectively, but it also can overcome the disadvantages of existing reliability optimization allocation methods for complex multifunction systems efficiently. In addition, the analysis process shows that the reliability optimization allocation method based on GO method can provide a new approach for the reliability optimization allocation of multifunction systems with multistate units.

Commentary by Dr. Valentin Fuster

Technical Brief

ASME J. Risk Uncertainty Part B. 2017;3(4):044501-044501-7. doi:10.1115/1.4035440.

When the hydraulic flow path is incompatible with the physical contours of the centrifugal pump (CP), flow instabilities occur. A prolonged operation in the flow-instability region may result in severe damages of the CP system. Hence, two of the major causes of flow instabilities such as the suction blockage (with five levels of increasing severity) and impeller defects are studied in the present work. Thereafter, an attempt is made to classify these faults and differentiate the physics behind the flow instabilities caused due to them. The tri-axial CP vibration data in time domain are employed for the fault classification. Multidistinct and multicoexisting fault classifications have been performed with different combinations of these faults using support vector machine (SVM) algorithm with radial basis function (RBF) kernel. Prediction results from the experiments and the developed methodology help to segregate the faults into appropriate class, identify the severity of the suction blockage, and substantiate the practical applicability of this study.

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. 2016;3(3):. doi:10.1061/AJRUA6.0000899.

Abstract  Traffic congestion is a serious challenge that urban transportation systems are facing. Variable speed limit (VSL) systems are one of the countermeasures to reduce traffic congestion and smooth traffic flow on roadways. The negative impacts of congestion, including road rage, air pollution, safety issues, and traffic delays, are well recognized. The impact of unexpected delays on road users is quantified through travel time reliability (TTR) measures. In this study, a bilevel optimization problem was introduced to determine location, speed limit reduction, start time, and duration of limited number of VSL signs while maximizing travel time reliability on selected critical paths on a network. The upper-level problem focuses on TTR optimization whereas the lower-level problem assigns traffic to the network using a dynamic traffic assignment simulation tool. A heuristic approach, simulated annealing, was used to solve the problem. The application of the methodology to a real roadway network is shown and results are discussed. The proposed methodology could assist traffic agencies in making proper decisions on how to allocate their limited resources to the network to maximize the benefits.

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2017;3(3):. doi:10.1061/AJRUA6.0000909.

Abstract  Windstorms, in an average year, are responsible for the most insured losses in the United States. Despite the significant losses, estimation of peak wind loading on structures remains difficult due to the inherent uncertainties in the estimation process. These uncertainties have usually been grouped into what has been termed the wind loading chain, which includes elements from the wind climate (e.g., storm type, wind directionality), terrain (e.g., roughness length, surrounding buildings), and aerodynamic (e.g., building shape, orientation) and dynamic (e.g., stiffness) effects. A lack of knowledge in a particular link (i.e., weak link) of the chain can lead to the unreliability of the entire structure. Current projections suggest the frequency and intensity of some environmental extremes, including wind speeds, will be affected due to a changing climate, thereby adding another layer of uncertainty in the wind climate link regarding the treatment of future (i.e., design) extreme wind loading for structures. This paper develops an approach for uncertainty characterization of extreme wind speeds with a worked example applied to nonhurricane events. The objective of this paper is to improve understanding of the wind climate and terrain links in the wind load chain by identifying, quantifying, and attributing uncertainties in these links including those from climate change. This objective will be achieved by (1) identification of new data-driven sources to better understand uncertainties; (2) development of a probabilistic approach to incorporate data and its associated uncertainties into the extreme wind speed estimation process, including projections of extreme winds in future climate states; and (3) quantification and attribution of uncertainties to the links of the chain considered.

Wind velocity , Climate change
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2017;3(3):. doi:10.1061/AJRUA6.0000904.

Abstract  In some regions, sea level rise due to climate change is expected to increase saltwater intrusion in coastal aquifers, leading to increased salt levels in drinking water wells relying on these supplies. Seawater contains elevated concentrations of bromide, which has been shown to increase the formation and alter the speciation of disinfection by-products (DBPs) during the treatment process. DBPs have been associated with increased risk of cancer and negative reproductive outcomes, and they are regulated under drinking water standards to protect human health. This paper incorporates statistical simulation of changes in source water bromide concentrations as a result of potential increased saltwater intrusion to assess the associated impact on trihalomethane (THM) formation and speciation. Additionally, the health risk associated with these changes is determined using cancer slope factors and odds ratios. The analysis indicates that coastal utilities treating affected groundwater sources will likely meet regulatory levels for THMs, but even small changes in saltwater intrusion can have significant effects on finished water concentrations and may exceed desired health risk threshold levels due to the extent of bromination in the THM. As a result of climate change, drinking water utilities using coastal groundwater or estuaries should consider the implications of treating high bromide source waters. Additionally, extra consideration should be taken for surface water utilities considering mixing with groundwater sources, as elevated source water bromide could pose additional challenges for health risk, despite meeting regulatory requirements for THM.

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2017;3(3):. doi:10.1061/AJRUA6.0000902.

Abstract  The conventional simulation model used in the prediction of long-term infrastructure development systems such as public–private partnership (PPP)–build-operate-transfer (BOT) projects assumes single probabilistic values for all of the input variables. Traditionally, all the input risks and uncertainties in Monte Carlo simulation (MCS) are modeled based on probability theory. Its result is shown by a probability distribution function (PDF) and a cumulative distribution function (CDF), which are utilized for analyzing and decision making. In reality, however, some of the variables are estimated based on expert judgment and others are derived from historical data. Further, the parameters’ data of the probability distribution for the simulation model input are subject to change and difficult to predict. Therefore, a simulation model that is capable of handling both types of fuzzy and probabilistic input variables is needed and vital. Recently fuzzy randomness, which is an extension of classical probability theory, provides additional features and improvements for combining fuzzy and probabilistic data to overcome aforementioned shortcomings. Fuzzy randomness–Monte Carlo simulation (FR-MCS) technique is a hybrid simulation method used for risk and uncertainty evaluation. The proposed approach permits any type of risk and uncertainty in the input values to be explicitly defined prior to the analysis and decision making. It extends the practical use of the conventional MCS by providing the capability of choosing between fuzzy sets and probability distributions. This is done to quantify the input risks and uncertainties in a simulation. A new algorithm for generating fuzzy random variables is developed as part of the proposed FR-MCS technique based on the α-cut. FR-MCS output results are represented by fuzzy probability and the decision variables are modeled by fuzzy CDF. The FR-MCS technique is demonstrated in a PPP-BOT case study. The FR-MCS results are compared with those obtained from conventional MCS. It is shown that the FR-MCS technique facilitates decision making for both the public and private sectors’ decision makers involved in PPP-BOT projects. This is done by determining a negotiation bound for negotiable concession items (NCIs) instead of precise values as are used in conventional MCS results. This approach prevents prolonged and costly negotiations in the development phase of PPP-BOT projects by providing more flexibility for decision makers. Both parties could take advantage of this technique at the negotiation table.

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2017;3(3):. doi:10.1061/AJRUA6.0000906.

Abstract  Global climate change has triggered studies across various science and engineering fields. This study demonstrates the need to account for climate change in assessing structural reliability. Civil engineering infrastructure is generally expected to function and serve over decades, and it should be able to withstand the various environmental changes that will occur in its lifetime. The authors study the impacts of climate change on the long-term resistance and loading of infrastructure by using global climate projections through the end of this century. The individual effects on resistance and loading are studied and then aggregated to estimate the projected net structural reliability. These results are compared with those of the case with no climate change to investigate relative effects. Global mean changes in natural hazard events are used to account for changes in loading patterns. The effect on resistance is studied by using time-dependent structural aging through a proposed degradation function accounting for different modes of degradation, including temperature effects, carbonation, corrosion, and fatigue. Global means are used in this study with results that can be applied to the conditions at specific locations for reliability assessment of particular structures. The authors show that seemingly small changes in climate have significant impacts on long-term structural reliability.

Reliability , Climate change

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