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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

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. 2015;3(2):. doi:10.1061/AJRUA6.0000841.
Abstract 

Abstract  In stochastic analysis of engineering systems, the task of generating samples according to a target probability distribution involving some performance function of the system response often arises. This paper introduces an adaptive method for rejection sampling that uses adaptive kernel sampling densities (AKSD) as proposal densities for the rejection sampling algorithm in an iterative approach. The AKSD formulation relies on having available (1) a small number of samples from the target density, as well as (2) evaluations of the system performance function over some other sample set. This information is then used to establish the adaptive features of the stochastic sampling involving (1) an explicit optimization of the kernel characteristics for reduction of the computational burden, and so maximizing sampling efficiency, and (2) selection of the exact model parameters to target so that potential problems when forming proposal densities for high-dimensional vectors are avoided. Beyond this theoretical formulation of the adaptive stochastic sampling, its implementation within the context of Subset Simulation (SS) is also demonstrated, with the AKSD method utilized for generating independent conditional failure samples. Additionally, a modified rejection sampling algorithm is proposed for using AKSD in SS that can significantly reduce the required number of simulations of the system model response.

Topics:
Density , Simulation , Approximation , Sampling methods
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2016;3(2):. doi:10.1061/AJRUA6.0000881.
Abstract 

Abstract  Axle loading spectrum inputs obtained from existing weigh-in-motion (WIM) stations are one of the key data elements required in the pavement mechanistic-empirical (ME) design. Because of limited number of WIM stations within a state agency, it is critical to implement clustering approaches to identifying similar traffic patterns and developing cluster average Level 2 inputs for a particular pavement design. Even though several states have applied clustering methods for this purpose, they rely solely on hierarchical-based method. Many other types of clustering techniques based on different induction principles are available but have not been tested. In this paper, four types of clustering methods, including agglomerative hierarchical, partitional K-means, model-based, and fuzzy c-means algorithms, are implemented to cluster traffic attributes for pavement ME design using data sets from 39 WIM sites in Michigan. Two case studies, one flexible pavement and one rigid pavement, are conducted. The impacts of using various clustering methods for preparation of Level 2 traffic inputs on pavement performance are examined. Cosine similarity analyses reveal that the four clustering methodologies generate highly comparable traffic inputs and predicted pavement performance as compared to the Level 1 results. However, the equivalent single axle loads (ESALs) from the four clustering methods can result in 12.7 mm (0.5 in.) of difference of designed surface layer thickness. The hierarchical method consistently has the lowest cosine similarity values, the fuzzy-based method has the highest similarity, while the other two clustering methods generally outperform the hierarchical method if Level 1 site-specific results are set as the benchmark. This study raises the awareness that more research is desired to select the most appropriate clustering approach for the development of Level 2 traffic inputs based on existing WIM data sets for pavement ME design.

Topics:
Design , Traffic , Trucks , Pavement
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2016;3(2):. doi:10.1061/AJRUA6.0000889.
Abstract 

Abstract  Data mining is a discovery procedure to explore and visualize useful but less-than-obvious information or patterns in large collections of data. Given the amount and varying parameter types in a large data set such as that of the National Bridge Inventory (NBI), using traditional clustering techniques for discovery is impractical. As a consequence, the authors have applied a novel data discovery tool, called Two-step cluster analysis, to visualize associations between concrete bridge deck design parameters and bridge deck condition ratings. Two-step cluster analysis is a powerful knowledge discovery tool that can handle categorical and interval data simultaneously and is capable of reducing dimensions for large data sets. The analysis, of a total of 9,809 concrete highway bridge decks in the Northeast climatic region, found that bridges with cast-in-place decks that have a bituminous wearing surface, a preformed fabric membrane, and epoxy-coated reinforcement protection are strongly associated with the high condition ratings for bridge decks regardless of the average daily truck traffic (ADTT). Conversely, results show that bridges with cast-in-place bridge decks that have a bituminous wearing surface but have neither a deck membrane nor deck reinforcement protection are strongly associated with low condition ratings for bridge decks regardless of the ADTT. It was concluded that Two-step cluster analysis is a useful tool for bridge owners and agencies to visualize general trends in their concrete bridge deck condition data and to support them in their decision-making processes to effectively allocate limited funds for maintenance, repair, and design of bridge decks.

Topics:
Bridges (Structures) , Concretes , Data mining
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2015;3(2):. doi:10.1061/AJRUA6.0000859.
Abstract 

Abstract  As traditionally infrastructure-centric industries such as the railways deploy ever more complex information systems, data interoperability becomes a challenge that must be overcome in order to facilitate effective decision making and management. In this paper, the authors propose a system based on semantic data modeling techniques to allow integration of information from diverse and heterogeneous sources. The results of work, which aimed to demonstrate how semantic data models can be used in the rail industry, are presented. These include a novel domain ontology for the railways, and a proof-of-concept real time passenger information system based on semantic web technologies. Methods and patterns for creating such ontologies and real world systems are discussed, and ontology-based techniques for integrating data with legacy information systems are shown.

Topics:
Ontologies , Data fusion , Rails
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2016;3(2):. doi:10.1061/AJRUA6.0000868.
Abstract 

Abstract  A maintenance problem can be regarded as an optimization task, where the solution is a trade-off between the costs associated with inspection and repair activities and the benefits related to the faultless operation of the infrastructure. The optimization aims at minimizing the total cost while tuning some parameters, such as the number, time, and quality of inspections. Due to the unavoidable uncertainties, the expected cost of maintenance and failure can only be estimated by assessing the reliability of the system. The problem is, therefore, formulated as a time-variant reliability-based optimization, where both objective and constraint functions require the assessment of reliability with time. This paper proposes an efficient general numerical technique to solve this problem by means of just one single reliability analysis, while explicitly taking the diverse forms of uncertainty into account. The technique is generally applicable to any problem where the ageing or damage propagation process is known by means of input-output relationships, which apply to a great number of the cases. This technique exploits a Monte Carlo strategy derived from the concept of forced simulation, which significantly increases the efficiency of computing the optimal solution. The efficiency and accuracy of the proposed approach is shown by means of an example involving a fatigue-prone weld in a bridge girder.

Topics:
Inspection , Maintenance , Simulation , Design

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