Probabilistic risk assessment (PRA) has been used in various technological fields to assist regulatory agencies, managerial decision makers, and systems designers in assessing and mitigating the ...risks inherent in these complex arrangements. Has PRA delivered on its promise? How do we gage PRA performance? Are our expectations about value of PRA realistic? Are there disparities between what we get and what we think we are getting form PRA and its various derivatives? Do current PRAs reflect the knowledge gained from actual events? How do we address potential gaps? These are some of the questions that have been raised over the years since the inception of the field more than forty years ago. This paper offers a brief assessment of PRA as a technical discipline in theory and practice, its key strengths and weaknesses, and suggestions on ways to address real and perceived shortcomings.
Traditional Probabilistic Risk Assessments (PRAs) model dependencies through deterministic relationships in fault trees and event trees and parametric models of common cause failure (CCF) events. ...Popular CCF models do not recognize system specific defenses against dependencies and are restricted to identical components in redundant configurations. While this has allowed prediction of system reliability with little or no data, it is a limiting factor in many applications such as modeling the characteristics of a specific system design or incorporating the characteristics of failure when assessing a failure event׳s risk significance (known as an Event Assessment, or Significance Determination). This paper proposes the General Dependency Model (GDM), which uses Bayesian Network to model the probabilistic dependencies between components. This is done through the introduction of three parameters for each failure cause, which relate to physical attributes of the system being modeled, i.e., cause condition probability, component fragility, and coupling factor strength. Finally this paper demonstrates the development and use of the GDM in traditional applications of PRA, and for Event Assessments and Significance Determination. Examples of the quantification of the GDM model parameters for these applications are provided.
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•Current common cause failure modeling methods are limited to identical components.•Current Event Assessments cannot include information about the failure cause.•A General Dependency Model (GDM) is proposed using Bayesian Network modeling.•GDM can use historic data and causal information from qualitative assessments.
•A hierarchical method for reliability analysis of PMSs is proposed.•The method is based on the BDD models.•The generation of models is from the aspect of failure mechanism accumulation.•Four ...accumulation rules among failure mechanisms are provided.
Phased-mission systems (PMSs) are featured by the variety of system configuration, environment or load condition in different phases which can attribute to the alteration of system behavior. From the view of failure mechanism development, the damage accumulation of each mechanism in different phases should be considered for calculating the system reliability. A hierarchical model based on the binary decision diagram (BDD) is proposed to integrate four types of damage accumulation rules into PMS reliability modeling, including the homogeneous failure mechanisms with phase constant stress or combinational profile and inhomogeneous failure mechanisms which have the same damage effect in one phase or different phases. An extended BDD was combined into the traditional BDD model to integrate three different levels of the failure accumulation, including the failure mechanism level, the phase level and the mission level. As a study case, the reliability of a phased-mission control and drive system was modeled and analyzed. Results showed that the proposed method can effectively describe the changing behavior of a PMS and obtain the system reliability by calculating the model.
AbstractSoil reinforcement using synthetic fibers is a proven technique for enhancing engineering properties to meet the requirements of subgrade materials for pavement projects. The safe and ...economical design of fiber-reinforced pavement layers requires better insights into their unsaturated behavior. The soil–water characteristic curve (SWCC) is a measure of the water content inside soil pores at a given suction and is considered an essential property to better understand or predict the behavior of unsaturated fiber-reinforced soils. This paper centers on investigating the soil–water characteristic curves for typical expansive clay reinforced with two different types of polypropylene fibers having different surface morphological properties. The effect of an increase in the fiber dosage (0.2% and 0.6% by dry weight of soil) and fiber lengths (6 and 12 mm) on the resultant SWCC patterns is investigated. The addition of lime is considered to enhance the bonding between clay particles and fiber elements to ensure better friction mobilization levels. SWCCs were determined for all experimental mixtures using a combination of axis translation and filter paper techniques that cover the entire matric suction range during measurements. Volume changes undergone during suction measurements have been duly highlighted and discussed. Furthermore, the one-dimensional deformation (swelling/consolidation) characteristics of these lime-blended fiber-reinforced mixtures at different levels of initial matric suction are studied, and the correlations between initial matric suction with both compression index (Cc) and swell index (Cs) are established. The results indicate a significant change in the shape of the SWCC for lime-blended fiber-reinforced mixtures compared with the shape of the SWCC for natural clay. The contribution of fiber inclination to altering the shape of the SWCC curve is observed as slight regardless of the type, length, and dosage of fiber. Compression and swell indices for tested mixtures increased proportionately with an increase in initial matric suction levels. Doubling the length of the fiber also induced an increase in the compression index. Under no stress conditions did both fiber type and fiber length have a marginal effect on the swell potential and SWCCs.
Gas pipeline systems are one of the largest energy infrastructures in the world and are known to be very efficient and reliable. However, this does not mean they are prone to no risk. Corrosion is a ...significant problem in gas pipelines that imposes large risks such as ruptures and leakage to the environment and the pipeline system. Therefore, various maintenance actions are performed routinely to ensure the integrity of the pipelines. The costs of the corrosion-related maintenance actions are a significant portion of the pipeline’s operation and maintenance costs, and minimizing this large cost is a highly compelling subject that has been addressed by many studies. In this paper, we investigate the benefits of applying reinforcement learning (RL) techniques to the corrosion-related maintenance management of dry gas pipelines. We first address the rising need for a simulated testbed by proposing a test bench that models corrosion degradation while interacting with the maintenance decision-maker within the RL environment. Second, we propose a condition-based maintenance management approach that leverages a data-driven RL decision-making methodology. An RL maintenance scheduler is applied to the proposed test bench, and the results show that applying the proposed condition-based maintenance management technique can reduce up to 58% of the maintenance costs compared to a periodic maintenance policy while securing pipeline reliability.
AbstractThe current study aims at examining the effect of different calcium-based stabilizers {calcium hydroxide Ca(OH)2, calcium carbonate CaCO3, and calcium chloride CaCl2} on the geotechnical ...properties and lime-leaching behavior of expansive clay. The lime-leaching tests were conducted on clay samples that were compacted at maximum Proctor dry density values relying on specially fabricated molds and subjected to water flow conditions for a specific period (up to 7 days). In order to study the effect of pore fluid on the lime-leachability patterns, the pH of the leaching solution was kept at 7 (pH of deionized double-distilled water) and at 4 (adjusted using 0.1 N HNO3). The effect of curing period (1, 7, 14, and 28 days) on the lime-leaching pattern and unconfined compression strength (UCS) behavior were also studied. One-dimensional fixed-ring consolidation tests were performed to quantify the effect of various stabilizers (at fixed dosage of 4% by weight of dry sample) on the swell and deformation characteristics. The results of the study indicate that with an increase in curing period, irrespective of the type of stabilizer, the calcium concentration in the leachate reduced. The flow duration (7 days was adopted in the study) had an insignificant effect on the resultant lime-leaching values. When acidic water was used as flushing solution, relatively higher concentrations of calcium were noticed, which further establishes the fact that amorphous pozzolanic compounds formed in the early hydration reaction stages are easily soluble. Furthermore, the associated anions in respective stabilizers have a direct bearing on the resultant UCS and consolidation behavior.
Gamma shielding parameters for Bi2O3–B2O3 and Bi2O3–BaO–Na2O–MgO–B2O3 glasses have been investigated at medical diagnostic energies (20, 30, 40 and 60 keV). Mass attenuation coefficients calculated ...using the FLUKA code and XCOM program have been shown to be in very good agreement with each other. The evaluated parameters have indicated that the gamma shielding performance, at a specific energy, of each of the glass systems increases with the increment of Bi2O3. At 20 and 30 keV (40 and 60 keV), 55Bi2O3–45B2O3 (40Bi2O3–20BaO–10Na2O–10MgO–20B2O3) sample exhibits the best gamma shielding performance. At 40 and 60 keV, mass attenuation coefficients of all the glass samples are comparable to that of lead. Furthermore, both of the glass systems exhibit better shielding characteristics than the ordinary concrete. Finally, calculations of the macroscopic removal cross-sections of the glass systems have shown that 45Bi2O3–55B2O3, among all the samples, has the best shielding ability against fast neutrons.
•Each glass system studied has superior gamma shielding ability at different energy.•Increase of Bi2O3 in the sample offers better gamma shielding.•FLUKA mass attenuation coefficients are in good agreement with XCOM ones.
The hydraulic properties of expansive soils are affected due to the formation of visible cracks in the dry state. Chemical stabilization coupled with fiber reinforcement is often considered an ...effective strategy to improve the geotechnical performance of such soils. In this study, hydraulic conductivity tests have been conducted on expansive clay using two different types of fibers (fiber cast (FC) and fiber mesh (FM)) exhibiting different surface morphological properties. The fiber parameters include their dosage (added at 0.2% to 0.6% by dry weight of soil) and length (6 and 12 mm). Commercially available lime is added to ensure proper bonding between clay particles and fiber materials, and its dosage was fixed at 6% (by dry weight of the soil). Saturated hydraulic conductivity tests were conducted relying on a flexible wall permeameter on lime-treated fiber-blended soil specimens cured for 7 and 28 days. The confining pressures were varied from 50 to 400 kPa, and the saturated hydraulic conductivity values (ksat) were determined. For FC fibers, an increase in fiber dosage caused ksat values to increase by 9.5% and 94.3% for the 6 and 12 mm lengths, respectively, at all confining pressures and curing periods. For FM fibers, ksat values for samples mixed with 6 mm fiber increased by 12 and 99.2% for 6 and 12 mm lengths, respectively for all confining pressures at the end of the 28-day curing period. The results obtained from a flexible wall permeameter (FWP) were compared with those of a rigid wall permeameter (RWP) available in the literature, and the fundamental mechanism responsible for such variations is explained.
Oil pipeline network system health monitoring is important primarily due to the high cost of failure consequences. Optimal sensor selection helps provide more effective system health information from ...the perspective of economic and technical constraints. Optimization models confront different issues. For instance, many oil pipeline system performance models are inherently nonlinear, requiring nonlinear modelling. Optimization also confronts modeling uncertainties. Oil pipeline systems are among the most complicated and uncertain dynamic systems, as they include human elements, complex failure mechanisms, control systems, and most importantly component interactions. In this paper, an entropy-based Bayesian network optimization methodology for sensor selection and placement under uncertainty is developed. Entropy is a commonly used measure of information often been used to characterize uncertainty, particularly to quantify the effectiveness of measured signals of sensors in system health monitoring contexts. The entropy based Bayesian network optimization outlined herein also incorporates the effect that sensor reliability has on system information entropy content, which can also be related to the sensor cost. This approach is developed further by incorporating system information entropy and sensor costs in order to evaluate the performance of sensor combinations. The paper illustrates the approach using a simple oil pipeline network example. The so-called particle swarm optimization algorithm is used to solve the multi-objective optimization model, establishing the Pareto frontier.
Spare parts management is a critical issue in the industrial field, alongside planning maintenance and logistics activities. For accurate classification in particular, the decision-makers can ...determine the optimal inventory management strategy. However, problems such as criteria selection, rules explanatory, and learning ability arise when managing thousands of spare parts for modern industry. This paper presents a deep convolutional neural network based on graph (G-DCNN) which will realize multi-criteria classification through image identification based on an explainable hierarchical structure. In the first phase, a hierarchical classification structure is established according to the causal relationship of multiple criteria; in the second phase, nodes are colored according to their criteria level status so that the traditional numerical information can be visible through graph style; in the third phase, the colored structures are transferred into images and processed by structure-modified convolutional neural network, to complete the classification. Finally, the proposed method is applied in a real-world case study to validate its effectiveness, feasibility, and generality. This classification study supplies a good decision support to improve the monitor-focus on critical component and control inventory which will benefit the collaborative maintenance.
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