Pitting Degradation Modeling of Ocean Steel Structures Using Bayesian Network

2017 ◽  
Vol 139 (5) ◽  
Author(s):  
Jyoti Bhandari ◽  
Faisal Khan ◽  
Rouzbeh Abbassi ◽  
Vikram Garaniya ◽  
Roberto Ojeda
Keyword(s):  
Bayesian Network ◽  
Structural Steel ◽  
Marine Environment ◽  
Pitting Corrosion ◽  
Steel Structures ◽  
Poor Quality ◽  
Real World Data ◽  
Corrosion Depth ◽  
Operational Conditions

Modeling depth of long-term pitting corrosion is of interest for engineers in predicting the structural longevity of ocean infrastructures. Conventional models demonstrate poor quality in predicting the long-term pitting corrosion depth. Recently developed phenomenological models provide a strong understanding of the pitting process; however, they have limited engineering applications. In this study, a novel probabilistic model is developed for predicting the long-term pitting corrosion depth of steel structures in marine environment using Bayesian network (BN). The proposed BN model combines an understanding of corrosion phenomenological model and empirical model calibrated using real-world data. A case study, which exemplifies the application of methodology to predict the pit depth of structural steel in long-term marine environment, is presented. The result shows that the proposed methodology succeeds in predicting the time-dependent, long-term anaerobic pitting corrosion depth of structural steel in different environmental and operational conditions.

Stochastic modelling of corroded surface features of structural steel under sulfate attack

CORROSION ◽  
10.5006/3842 ◽  
2021 ◽  
Author(s):  
Youde Wang ◽  
Xiaodong Zhou ◽  
Shanhua Xu
Keyword(s):  
Structural Steel ◽  
Steel Structures ◽  
Stochastic Modelling ◽  
Corrosion Damage ◽  
Sulfate Attack ◽  
General Corrosion ◽  
Accelerated Corrosion ◽  
Corrosion Depth ◽  
Average Value ◽  
Pit Depth

Steel structures exposed to the sulfate corrosive environment for a long time will inevitably suffer random corrosion damage, which will lead to uncertain degradation in the mechanical properties of materials and structures. The safety and reliability assessments of corroded steel structures largely depend on the quantification of corrosion characteristics. In order to investigate the corrosion features of structural steel under sulfate attack, six batches of accelerated corrosion experiments were conducted on 18 steel specimens. The surface morphologies of all corroded coupons were firstly measured by a 3D surface profilometer, and then the surface parameters were calculated/extracted and analyzed by a self-written analysis algorithm to clarify the distribution characteristics and evolution laws of corrosion depth, pit depth and pit shape. The results revealed that the corrosion form of structural steel under sulfate attack was a rather uneven general corrosion, which exhibited the intersection of general corrosion and pits. The corrosion depth obeyed a normal distribution, and its average value, standard deviation, and the power spectrum peak increased as the corrosion age increased. The depth and aspect ratio of corrosion pits were both in accord with the lognormal distribution and showed the increasing and decreasing trend with the corrosion time going on, respectively. Besides, the probabilities of different pit shapes in different depth ranges were obtained, in which cone pits accounted for the largest proportion, and the pits gradually changed from cylindrical and hemispherical to conical with the increase of pit depth. In the end, the random models for corrosion depth and pits were established based on the statistical results, which realized the reconstruction and random modelling of corrosion characteristics of structural steel under sulfate attack.

scholarly journals Influence of pitting corrosion on fatigue and corrosion fatigue of ship structures Part I Pitting corrosion of ship structures

2013 ◽  
Vol 21 (1) ◽  
pp. 62-69 ◽  
Author(s):  
Marek Jakubowski
Keyword(s):  
Growth Rate ◽  
Marine Environment ◽  
Pitting Corrosion ◽  
Nucleation And Growth ◽  
Literature Survey ◽  
Growth Phases ◽  
Specific Kind ◽  
Ship Structures ◽  
Pit Nucleation

ABSTRACT The present paper is a literature survey focused on a specific kind of corrosion, i.e. pitting corrosion and its influence on fatigue of ship and offshore steels. Mechanisms of a shortand long-term pitting corrosion in marine environment have been described including pit nucleation and growth phases. Some models of pit growth versus time of exposure have been presented. Some factors which influence the pit growth rate have been discussed briefly

878-P: Real-World Data from U.S. Patients Using a Long-Term Implantable Continuous Glucose Monitoring (CGM) System: Age Effect on Glycemic Control

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 878-P
Author(s):  
KATHERINE TWEDEN ◽  
SAMANWOY GHOSH-DASTIDAR ◽  
ANDREW D. DEHENNIS ◽  
FRANCINE KAUFMAN
Keyword(s):  
Glycemic Control ◽  
Continuous Glucose Monitoring ◽  
Real World ◽  
Glucose Monitoring ◽  
Age Effect ◽  
Real World Data ◽  
World Data

scholarly journals A Unified View of Causal and Non-causal Feature Selection

2021 ◽  
Vol 15 (4) ◽  
pp. 1-46
Author(s):  
Kui Yu ◽  
Lin Liu ◽  
Jiuyong Li
Keyword(s):  
Feature Selection ◽  
Bayesian Network ◽  
Synthetic Data ◽  
Selection Methods ◽  
Bayesian Network Model ◽  
Real World Data ◽  
Feature Sets ◽  
Unified View ◽  
Optimal Feature ◽  
Different Levels

In this article, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature selection methods share the same objective. That is to find the Markov blanket of a class attribute, the theoretically optimal feature set for classification. We then examine the assumptions made by causal and non-causal feature selection methods when searching for the optimal feature set, and unify the assumptions by mapping them to the restrictions on the structure of the Bayesian network model of the studied problem. We further analyze in detail how the structural assumptions lead to the different levels of approximations employed by the methods in their search, which then result in the approximations in the feature sets found by the methods with respect to the optimal feature set. With the unified view, we can interpret the output of non-causal methods from a causal perspective and derive the error bounds of both types of methods. Finally, we present practical understanding of the relation between causal and non-causal methods using extensive experiments with synthetic data and various types of real-world data.

scholarly journals Chances and challenges of a long-term data repository in multiple sclerosis: 20th birthday of the German MS registry

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lisa-Marie Ohle ◽  
David Ellenberger ◽  
Peter Flachenecker ◽  
Tim Friede ◽  
Judith Haas ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Sociodemographic Factors ◽  
Healthcare Research ◽  
Structure Design ◽  
Data Repository ◽  
Research Projects ◽  
Real World Data ◽  
Data Repositories ◽  
Term Data

AbstractIn 2001, the German Multiple Sclerosis Society, facing lack of data, founded the German MS Registry (GMSR) as a long-term data repository for MS healthcare research. By the establishment of a network of participating neurological centres of different healthcare sectors across Germany, GMSR provides observational real-world data on long-term disease progression, sociodemographic factors, treatment and the healthcare status of people with MS. This paper aims to illustrate the framework of the GMSR. Structure, design and data quality processes as well as collaborations of the GMSR are presented. The registry’s dataset, status and results are discussed. As of 08 January 2021, 187 centres from different healthcare sectors participate in the GMSR. Following its infrastructure and dataset specification upgrades in 2014, more than 196,000 visits have been recorded relating to more than 33,000 persons with MS (PwMS). The GMSR enables monitoring of PwMS in Germany, supports scientific research projects, and collaborates with national and international MS data repositories and initiatives. With its recent pharmacovigilance extension, it aligns with EMA recommendations and helps to ensure early detection of therapy-related safety signals.

scholarly journals Long-term effectiveness in patients previously treated with cladribine tablets: a real-world analysis of the Italian multiple sclerosis registry (CLARINET-MS)

2020 ◽  
Vol 13 ◽  
pp. 175628642092268 ◽  
Author(s):  
Francesco Patti ◽  
Andrea Visconti ◽  
Antonio Capacchione ◽  
Sanjeev Roy ◽  
Maria Trojano ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Real World ◽  
Event Analysis ◽  
Treatment Change ◽  
Real World Data ◽  
Indirect Measure ◽  
Disease Modifying Drugs ◽  
Disease Modifying ◽  
Using Data

Background: The CLARINET-MS study assessed the long-term effectiveness of cladribine tablets by following patients with multiple sclerosis (MS) in Italy, using data from the Italian MS Registry. Methods: Real-world data (RWD) from Italian MS patients who participated in cladribine tablets randomised clinical trials (RCTs; CLARITY, CLARITY Extension, ONWARD or ORACLE-MS) across 17 MS centres were obtained from the Italian MS Registry. RWD were collected during a set observation period, spanning from the last dose of cladribine tablets during the RCT (defined as baseline) to the last visit date in the registry, treatment switch to other disease-modifying drugs, date of last Expanded Disability Status Scale recording or date of the last relapse (whichever occurred last). Time-to-event analysis was completed using the Kaplan–Meier (KM) method. Median duration and associated 95% confidence intervals (CI) were estimated from the model. Results: Time span under observation in the Italian MS Registry was 1–137 (median 80.3) months. In the total Italian patient population ( n = 80), the KM estimates for the probability of being relapse-free at 12, 36 and 60 months after the last dose of cladribine tablets were 84.8%, 66.2% and 57.2%, respectively. The corresponding probability of being progression-free at 60 months after the last dose was 63.7%. The KM estimate for the probability of not initiating another disease-modifying treatment at 60 months after the last dose of cladribine tablets was 28.1%, and the median time-to-treatment change was 32.1 (95% CI 15.5–39.5) months. Conclusion: CLARINET-MS provides an indirect measure of the long-term effectiveness of cladribine tablets. Over half of MS patients analysed did not relapse or experience disability progression during 60 months of follow-up from the last dose, suggesting that cladribine tablets remain effective in years 3 and 4 after short courses at the beginning of years 1 and 2.

scholarly journals The study of long-term durability and bio-colonization of concrete in marine environment

2021 ◽  
pp. 100120
Author(s):  
Marine Georges ◽  
Amel Bourguiba ◽  
Daniel Chateigner ◽  
Nassim Sebaibi ◽  
Mohamed Boutouil
Keyword(s):  
Marine Environment
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