Probabilistic Assessment of Minor Mechanical Damage

Author(s):  
Patrick H. Vieth ◽  
Clifford J. Maier ◽  
William V. Harper ◽  
Elden Johnson ◽  
Bhaskar Neogi ◽  
...  

In-line inspection (ILI) of the Trans Alaska Pipeline System (TAPS) using high resolution metal loss tools indicated 77 locations with suspected minor mechanical damage features (MDF). The tools used are able to detect the presence of a suspected feature, and measure indented dimensions, but are insufficient to detect the presence of cracks or gouges needed to reliably assess feature severity based solely on the ILI data. Excavations of 42 sites deemed most severe provided important field data characterizing residual deformation dimensions, the occurrence of gouges or cracks, and allowing a reliable field assessment of defect severity. Upon completion of the excavations, 35 possible MDF locations remained unexcavated. An engineering evaluation was undertaken to assess whether or not these remaining minor MDF pose a threat that is significant enough to warrant excavation. Multiple assessment methods were utilized including deterministic, probabilistic, and risk assessment methods. The probabilistic assessment of 35 unexcavated MDFs was performed using PCFStat; or Pressure Cycle Fatigue Statistical Assessment, which uses Monte Carlo simulation to estimate remaining fatigue life. PCFStat performs 1,000’s of simulations for each case where the input parameters are randomly selected from expected distributions. Of particular importance is the fatigue environment of the location. The results of the probabilistic assessment were used to estimate the potential for failure of remaining MDFs. The results suggest that 25 of 35 unexpected damage features had a POF of less than 10−4 over the remaining expected pipeline life cycle and thus are unlikely to fail. Alyeska considered a combination of probabilistic, deterministic and risk assessment results to decide on the actual locations to be examined. The results of probabilistic analysis also were found to support the outcome of the operator’s risk-based evaluation process.

Author(s):  
M. J. Rosenfeld ◽  
Alan Beckett ◽  
Bhaskar Neogi ◽  
U. J. Baskurt ◽  
Elden Johnson

In-line inspection (ILI) of the Trans Alaska Pipeline System (TAPS) using high resolution metal loss and caliper tools indicated 77 locations with suspected minor mechanical damage features (MDFs). The tools used are able to detect the presence of a suspected feature, and measure indented dimensions, but are insufficient to detect the presence of cracks or gouges needed to reliably assess feature severity based solely on the ILI data. Excavations of 42 sites deemed most severe provided important field data characterizing residual deformation dimensions, revealed the occurrence of generally surficial gouges or cracks, and allowed a reliable field assessment of defect severity. Upon completion of the excavations, 35 possible MDF locations remained unexcavated. An engineering evaluation was undertaken to assess whether or not the remaining MDFs pose a threat that is significant enough to warrant excavation. Multiple assessment methods were utilized including deterministic, probabilistic, and risk assessment methods. A deterministic mechanics model was developed to estimate the safe operating life of the pipeline at each of the remaining uninvestigated locations considering the characteristics of previously observed damage, the perceived severity of uninvestigated damage, material properties of the pipe, and the fatigue environment resulting from expected modes of pipeline operation. The results strongly suggested that 33 of 35 damage features were extremely minor, with remaining life well in excess of the remaining project life cycle. None of these features were judged to threaten the immediate integrity of the line, and are unlikely to do so in the foreseeable operating life of the facility. The results also were found to support the outcome of the operator’s risk-based evaluation process.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sanghee Lee ◽  
Yoon Jung Chang ◽  
Hyunsoon Cho

Abstract Background Cancer patients’ prognoses are complicated by comorbidities. Prognostic prediction models with inappropriate comorbidity adjustments yield biased survival estimates. However, an appropriate claims-based comorbidity risk assessment method remains unclear. This study aimed to compare methods used to capture comorbidities from claims data and predict non-cancer mortality risks among cancer patients. Methods Data were obtained from the National Health Insurance Service-National Sample Cohort database in Korea; 2979 cancer patients diagnosed in 2006 were considered. Claims-based Charlson Comorbidity Index was evaluated according to the various assessment methods: different periods in washout window, lookback, and claim types. The prevalence of comorbidities and associated non-cancer mortality risks were compared. The Cox proportional hazards models considering left-truncation were used to estimate the non-cancer mortality risks. Results The prevalence of peptic ulcer, the most common comorbidity, ranged from 1.5 to 31.0%, and the proportion of patients with ≥1 comorbidity ranged from 4.5 to 58.4%, depending on the assessment methods. Outpatient claims captured 96.9% of patients with chronic obstructive pulmonary disease; however, they captured only 65.2% of patients with myocardial infarction. The different assessment methods affected non-cancer mortality risks; for example, the hazard ratios for patients with moderate comorbidity (CCI 3–4) varied from 1.0 (95% CI: 0.6–1.6) to 5.0 (95% CI: 2.7–9.3). Inpatient claims resulted in relatively higher estimates reflective of disease severity. Conclusions The prevalence of comorbidities and associated non-cancer mortality risks varied considerably by the assessment methods. Researchers should understand the complexity of comorbidity assessments in claims-based risk assessment and select an optimal approach.


Parasitology ◽  
1999 ◽  
Vol 117 (7) ◽  
pp. 205-212 ◽  
Author(s):  
C. J. GIBSON ◽  
C. N. HAAS ◽  
J. B. ROSE

Throughout the past decade much research has been directed towards identifying the occurrence, epidemiology, and risks associated with waterborne protozoa. While outbreaks are continually documented, sporadic cases of disease associated with exposure to low levels of waterborne protozoa are of increasing concern. Current methodologies may not be sensitive enough to define these low levels of disease. However, risk assessment methods may be utilised to address these low level contamination events. The purpose of this article is to provide an introduction to microbial risk assessment for waterborne protozoa. Risk assessment is a useful tool for evaluating relative risks and can be used for development of policies to decrease risks. Numerous studies have been published on risk assessment methods for pathogenic protozoa including Cryptosporidium and Giardia. One common notion prevails: microbial risk assessment presents interesting complications to the traditional chemical risk assessment paradigm. Single microbial exposures (non-threshold) are capable of causing symptomatic illness unlike traditional chemical exposures, which require a threshold to be reached. Due to the lack of efficient recovery and detection methods for protozoa, we may be underestimating the occurrence, concentration and distribution of these pathogenic micro-organisms. To better utilize the tool of microbial risk assessment for risk management practices, future research should focus in the area of exposure assessment.


Author(s):  
Peter Song ◽  
Doug Lawrence ◽  
Sean Keane ◽  
Scott Ironside ◽  
Aaron Sutton

Liquids pipelines undergo pressure cycling as part of normal operations. The source of these fluctuations can be complex, but can include line start-stop during normal pipeline operations, batch pigs by-passing pump stations, product injection or delivery, and unexpected line shut-down events. One of the factors that govern potential growth of flaws by pressure cycle induced fatigue is operational pressure cycles. The severity of these pressure cycles can affect both the need and timing for an integrity assessment. A Pressure Cycling Monitoring (PCM) program was initiated at Enbridge Pipelines Inc. (Enbridge) to monitor the Pressure Cycling Severity (PCS) change with time during line operations. The PCM program has many purposes, but primary focus is to ensure the continued validity of the integrity assessment interval and for early identification of notable changes in operations resulting in fatigue damage. In conducting the PCM program, an estimated fatigue life based on one month or one quarter period of operations is plotted on the PCM graph. The estimated fatigue life is obtained by conducting fatigue analysis using Paris Law equation, a flaw with dimensions proportional to the pipe wall thickness and the outer diameter, and the operating pressure data queried from Enbridge SCADA system. This standardized estimated fatigue life calculation is a measure of the PCS. Trends in PCS overtime can potentially indicate the crack threat susceptibility the integrity assessment interval should be updated. Two examples observed on pipeline segments within Enbridge pipeline system are provided that show the PCS change over time. Conclusions are drawn for the PCM program thereafter.


2012 ◽  
Vol 14 (4) ◽  
pp. 918-936 ◽  
Author(s):  
Julián Garrido ◽  
Ignacio Requena ◽  
Stefano Mambretti

Risk assessment involves the study of vulnerability and hazards. When focused on flood events, such an analysis should evidently include the theoretical and practical study of floods and their behavior. Nevertheless, risk assessment is not useful if the results are not subsequently used for more effective management and planning by local authorities and qualified personnel. The risk evaluation process is composed of a set of actions, each of which requires different inputs. In fact, the results of one action are used as the input for another. This paper describes a semantic model for the study and management of floods with a view to elaborating a conceptual framework and designing a knowledge base. The model is based on the environmental assessment ontology and demonstrates how a brief ontology can be generated.


2021 ◽  
Vol 3 (3) ◽  
pp. 23-29
Author(s):  
Tagir Fabarisov ◽  
Georg Siedel ◽  
Silvia Vock ◽  
Andrey Morozov

2014 ◽  
Vol 72 (3) ◽  
pp. 1057-1068 ◽  
Author(s):  
Enric Cortés ◽  
Elizabeth N. Brooks ◽  
Kyle W. Shertzer

Abstract We review three broad categories of risk assessment methodology used for cartilaginous fish: productivity-susceptibility analysis (PSA), demographic methods, and quantitative stock assessments. PSA is generally a semi-quantitative approach useful as an exploratory or triage tool that can be used to prioritize research, group species with similar vulnerability or risk, and provide qualitative management advice. Demographic methods are typically used in the conservation arena and provide quantitative population metrics that are used to quantify extinction risk and identify vulnerable life stages. Stock assessments provide quantitative estimates of population status and the associated risk of exceeding biological reference points, such as maximum sustainable yield. We then describe six types of uncertainty (process, observation, model, estimation, implementation, and institutional) that affect the risk assessment process, identify which of the three risk assessment methods can accommodate each type of uncertainty, and provide examples mostly for sharks drawn from our experience in the United States. We also review the spectrum of stock assessment methods used mainly for sharks in the United States, and present a case study where multiple methods were applied to the same species (dusky shark, Carcharinus obscurus) to illustrate differing degrees of model complexity and type of uncertainty considered. Finally, we address the common and problematic case of data-poor bycatch species. Our main recommendation for future work is to use Management Strategy Evaluation or similar simulation approaches to explore the effect of different sources of uncertainty, identify the most critical data to satisfy predetermined management objectives, and develop harvest control rules for cartilaginous fish. We also propose to assess the performance of data-poor and -rich methods through stepwise model construction.


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