Full-Scale Pipe Strain Test Quality and Safety Factor Determination for Strain-Based Engineering Critical Assessment

2016 ◽  
Author(s):  
Douglas P. Fairchild ◽  
Justin M. Crapps ◽  
Wentao Cheng ◽  
Huang Tang ◽  
Svetlana Shafrova
Keyword(s):  
Prediction Model ◽  
Data Base ◽  
Model Validation ◽  
Safety Factor ◽  
Accuracy Assessment ◽  
Critical Assessment ◽  
Full Scale ◽  
Lessons Learned ◽  
Test Quality ◽  
New Model

Generating a tensile strain capacity (TSC) prediction model is a difficult challenge in applied mechanics. Because current models are relatively new and extensive strain-based design (SBD) pipeline service experience does not exist, rigorous model validation using full-scale tests (FSTs) is paramount. The lessons learned from 159 FSTs were presented previously and the data base has grown to 173 tests. This data base is used to assess the accuracy of a relatively new TSC prediction model. The new model simulates a single, surface breaking weld flaw; however, some of the FSTs contained interacting or embedded flaws or unintentional weld defects, while others failed by brittle fracture, and still others experienced welding problems rendering them unsuitable for model validation. Of 173 tests, a smaller number (122, 101, or 89 depending on the goal) is used for comparison to the new model. This paper describes (1) the importance of reliable FSTs, (2) how the 173 tests were judged for suitability in model accuracy assessment, and (3) the use of the FST data to develop a safety factor for strain-based engineering critical assessment (SBECA). The safety factor is generated from a 95% upper confidence limit on the ratio of predicted-to-measured TSC. The safety factor is 1.88. Using the new model and this safety factor, a TSC prediction equation is provided for use in SBECA. The practical meaning of this is that if either TSC or tolerable defect size is calculated using the new model, then the probability of being non-conservative is estimated to be 5%.

An Epidemic Avian Influenza Prediction Model Based on Google Trends

2019 ◽  
Vol 16 (4) ◽  
pp. 303-310 ◽  
Author(s):  
Yi Lu ◽  
Shuo Wang ◽  
Jianying Wang ◽  
Guangya Zhou ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Avian Influenza ◽  
Prediction Model ◽  
Multiple Linear Regression ◽  
Google Trends ◽  
Health Providers ◽  
Human Beings ◽  
Success Rates ◽  
New Model ◽  
Regression Approach

The occurrence of epidemic avian influenza (EAI) not only hinders the development of a country's agricultural economy, but also seriously affects human beings’ life. Recently, the information collected from Google Trends has been increasingly used to predict various epidemics. In this study, using the relevant keywords in Google Trends as well as the multiple linear regression approach, a model was developed to predict the occurrence of epidemic avian influenza. It was demonstrated by rigorous cross-validations that the success rates achieved by the new model were quite high, indicating the predictor will become a very useful tool for hospitals and health providers.

scholarly journals Forecasting the Number of New Coronavirus Infections Us-ing an Improved Grey Prediction Model

2021 ◽  
Author(s):  
Hui Li ◽  
Bo Zeng ◽  
Jianzhou Wang ◽  
Hua’an Wu
Keyword(s):  
Prediction Model ◽  
Particle Swarm Optimization Algorithm ◽  
Percentage Error ◽  
Small Data ◽  
Grey Prediction ◽  
Matlab Simulation ◽  
Short Term ◽  
New Model ◽  
Grey Prediction Model ◽  
Coronavirus Infections

Background: Recently, a new coronavirus has been rapidly spreading from Wuhan, China. Forecasting the number of infections scientifically and effectively is of great significance to the allocation of medical resources and the improvement of rescue efficiency. Methods: The number of new coronavirus infections was characterized by “small data, poor information” in the short term. The grey prediction model provides an effective method to study the prediction problem of “small data, poor information”. Based on the order optimization of NHGM(1,1,k), this paper uses particle swarm optimization algorithm to optimize the background value, and obtains a new improved grey prediction model called GM(1,1|r,c,u). Results: Through MATLAB simulation, the comprehensive percentage error of GM(1,1|r,c,u), NHGM(1,1,k), UGM(1,1), DGM(1,1) are 2.4440%, 11.7372%, 11.6882% and 59.9265% respectively, so the new model has the best prediction performance. The new coronavirus infections was predicted by the new model. Conclusion: The number of new coronavirus infections in China increased continuously in the next two weeks, and the final infections was nearly 100 thousand. Based on the prediction results, this paper puts for-ward specific suggestions.

Building Model Credibility

2020 ◽  
pp. 133-140
Keyword(s):  
Model Validation ◽  
Adaptive Behavior ◽  
Individual Behavior ◽  
Lessons Learned ◽  
Building Model ◽  
Individual Based Models ◽  
The Many ◽  
Primary Basis ◽  
Nontraditional Approach

This chapter assesses how state- and prediction-based theory (SPT), as a nontraditional approach to modeling adaptive behavior embedded in a nontraditional population modeling approach, faces a significant credibility challenge. This challenge is complicated by the many ways that models can gain or lose credibility, and widespread confusion surrounding the term model validation. The chapter then addresses the task of testing, improving, and establishing the credibility of individual-based models (IBMs) that contain adaptive individual behavior. The experience with the trout and salmon models provides the primary basis for this discussion, but other long-term modeling projects have produced similar experiences. The chapter summarizes some of the issues and challenges that typically arise and how they have been dealt with, before presenting lessons learned from two decades of empirical and simulation studies addressing credibility of the salmonid models.

scholarly journals A Hybrid Grey Prediction Model for Small Oscillation Sequence Based on Information Decomposition

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Meng Zhou ◽  
Bo Zeng ◽  
Wenhao Zhou
Keyword(s):  
Prediction Model ◽  
Small Oscillation ◽  
New Method ◽  
Superior Performance ◽  
Small Data ◽  
Grey Prediction ◽  
New Model ◽  
Grey Prediction Model ◽  
Proposed Model ◽  
Oscillation Characteristic

Grey prediction model has good performance in solving small data problem, and has been widely used in various research fields. However, when the data show oscillation characteristic, the effect of grey prediction model performs poor. To this end, a new method was proposed to solve the problem of modelling small data oscillation sequence with grey prediction model. Based on the idea of information decomposition, the new method employed grey prediction model to capture the trend characteristic of complex system, and ARMA model was applied to describe the random oscillation characteristic of the system. Crops disaster area in China was selected as a case study and the relevant historical eight-year data published by government department were substituted to the proposed model. The modelling results of the new model were compared with those of other traditional mainstream prediction models. The results showed that the new model had evidently superior performance. It indicated that the proposed model will contribute to solve small oscillation problems and have positive significance for improving the applicability of grey prediction model.

A Critical Assessment of Canadian Earthquake Monitoring and Alerting Practice versus the Initial Challenges of the 2020 COVID-19 Experience

2020 ◽  
Vol 92 (1) ◽  
pp. 60-66 ◽  
Author(s):  
David A. McCormack ◽  
Allison L. Bent ◽  
Reid Van Brabant ◽  
Lorne McKee
Keyword(s):  
Cost Effectiveness ◽  
Critical Assessment ◽  
Lessons Learned ◽  
H1n1 Pandemic ◽  
Earthquake Monitoring ◽  
Seismograph Network

Abstract We describe the regular pre-COVID mode of operations for the Canadian National Seismograph Network and the associated monitoring, alerting, and analysis for earthquakes in Canada; we describe how the current operational posture evolved and discuss the ways in which the posture was and was not suitable to respond to the challenges and constraints of the COVID-19 situation in Canada. We find that many of the design and operation decisions that have been taken over the last several decades for earthquake monitoring in Canada, collectively driven largely by considerations of resilience and cost-effectiveness and further refined after the experience of the H1N1 pandemic, resulted in a system that continued to function effectively under lockdown conditions. There were many earthquakes in Canada that required seismologist response during the lockdown, all of which were handled remotely without issue. Specific challenges and lessons learned from the first few months of the pandemic are noted.

scholarly journals Wnt Binding Affinity Prediction for Putative Frizzled-Type Cysteine-Rich Domains

2019 ◽  
Vol 20 (17) ◽  
pp. 4168 ◽  
Author(s):  
Mark Agostino ◽  
Sebastian Öther-Gee Pohl
Keyword(s):  
Binding Energy ◽  
Prediction Model ◽  
Binding Affinity ◽  
Wnt Signalling ◽  
Binding Affinity Prediction ◽  
New Model ◽  
Affinity Prediction ◽  
Frizzled Receptors ◽  
Canonical Wnt ◽  
Related Proteins

Several proteins other than the frizzled receptors (Fzd) and the secreted Frizzled-related proteins (sFRP) contain Fzd-type cysteine-rich domains (CRD). We have termed these domains “putative Fzd-type CRDs”, as the relevance of Wnt signalling in the majority of these is unknown; the RORs, an exception to this, are well known for mediating non-canonical Wnt signalling. In this study, we have predicted the likely binding affinity of all Wnts for all putative Fzd-type CRDs. We applied both our previously determined Wnt‒Fzd CRD binding affinity prediction model, as well as a newly devised model wherein the lipid term was forced to contribute favourably to the predicted binding energy. The results obtained from our new model indicate that certain putative Fzd CRDs are much more likely to bind Wnts, in some cases exhibiting selectivity for specific Wnts. The results of this study inform the investigation of Wnt signalling modulation beyond Fzds and sFRPs.

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