Taufik Aditiyawarman, J. Soedarsono, A. Kaban, R. Riastuti, Haryo Rahmadani
The work reports the systematic approach to the study of Artificial Intelligence (AI) in addressing the complexity of ILI data management to forecast the risk in natural gas pipelines. A recent conventional standard may not be sufficient to address the variation data of corrosion defects and inherent human subjectivity. Such methodology undermines the accuracy assessment confidence and is ineffective in reducing inspection costs. In this work, a combination of Unsupervised and Supervised Machine Learning and Deep Learning has profoundly accelerated the Probability of Failure (PoF) assessment and analysis. K-Means Clustering and Gaussian Mixture Models show direct relevance between the corrosion depth and corrosion rate, while the overlapping PoF value is scattered in three clusters. Logistic Regression, Support Vector Machine, k-Nearest Neighbors, and ensemble classifiers of AdaBoost, Random Forest, and Gradient Boosting are constructed using particular features, labels, and hyperparameters. The algorithm correctly predicted the score of PoF from 4790 instances and confirmed the 25% metal loss at a location of 13.399 m. The Artificial Neural Network is designed with various layers (input, hidden, and output) architecture. It is optimized using an activation function to predict that 74% of the pipeline's anomalies that classified at low-medium and medium-high risk. Furthermore, it provides a quick and precise prediction about the external defects at 13.1 m and requires the personnel to conduct wrapping composite. This work can be used as a standard guideline for risk assessment based on ILI and applies to industry and academia.
{"title":"The Study of Artificial Intelligent in Risk-Based Inspection Assessment and Screening: A Study Case of ILI Inspection","authors":"Taufik Aditiyawarman, J. Soedarsono, A. Kaban, R. Riastuti, Haryo Rahmadani","doi":"10.1115/1.4054969","DOIUrl":"https://doi.org/10.1115/1.4054969","url":null,"abstract":"\u0000 The work reports the systematic approach to the study of Artificial Intelligence (AI) in addressing the complexity of ILI data management to forecast the risk in natural gas pipelines. A recent conventional standard may not be sufficient to address the variation data of corrosion defects and inherent human subjectivity. Such methodology undermines the accuracy assessment confidence and is ineffective in reducing inspection costs. In this work, a combination of Unsupervised and Supervised Machine Learning and Deep Learning has profoundly accelerated the Probability of Failure (PoF) assessment and analysis. K-Means Clustering and Gaussian Mixture Models show direct relevance between the corrosion depth and corrosion rate, while the overlapping PoF value is scattered in three clusters. Logistic Regression, Support Vector Machine, k-Nearest Neighbors, and ensemble classifiers of AdaBoost, Random Forest, and Gradient Boosting are constructed using particular features, labels, and hyperparameters. The algorithm correctly predicted the score of PoF from 4790 instances and confirmed the 25% metal loss at a location of 13.399 m. The Artificial Neural Network is designed with various layers (input, hidden, and output) architecture. It is optimized using an activation function to predict that 74% of the pipeline's anomalies that classified at low-medium and medium-high risk. Furthermore, it provides a quick and precise prediction about the external defects at 13.1 m and requires the personnel to conduct wrapping composite. This work can be used as a standard guideline for risk assessment based on ILI and applies to industry and academia.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":"320 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78272711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The present work focuses on in-situ residual unbalance estimation of the dual rotor system with implementation of Active Magnetic Bearing (AMB) as a controller and exciter. The excessive vibration generated due to the presence of residual unbalances limits the operating speed of the system. The compact structure of the dual rotor system provides constraints to the conventional balancing procedure that requires manual addition of the trial unbalances for balancing. In order to overcome the difficulty in balancing of dual rotor system, an identification algorithm based on Modified Influence Coefficient Method (MICM) is developed for the simultaneous estimation of residual unbalances in both inner and outer rotors with generation of virtual trial unbalances as magnetic excitation through AMB. The controlling action of AMB attenuates the vibrational response of the system within the required limit and allow the safe operation of the system in the presence of rotor faults and additional excitations. The vibrational responses of the system at the limited locations and the magnitude and phase of the virtual trial unbalances are only required in the MICM for the estimation of unbalances. To numerically illustrate the present methodology, the displacement response are obtained from the developed finite element model of the dual rotor system with discrete disc unbalances and randomly distributed shaft. The robustness of the algorithm in estimation of residual unbalances is verified with the addition of different percentage of measurement noises. After balancing, the dual rotor system is found to traverse its critical speed with less vibrational response.
{"title":"Robust Dynamic Balancing of Dual Rotor-AMB System Through Virtual Trial Unbalances as Low and High Frequency Magnetic Excitation","authors":"Gyan Ranjan, R. Tiwari, H. Nemade","doi":"10.1115/1.4054695","DOIUrl":"https://doi.org/10.1115/1.4054695","url":null,"abstract":"\u0000 The present work focuses on in-situ residual unbalance estimation of the dual rotor system with implementation of Active Magnetic Bearing (AMB) as a controller and exciter. The excessive vibration generated due to the presence of residual unbalances limits the operating speed of the system. The compact structure of the dual rotor system provides constraints to the conventional balancing procedure that requires manual addition of the trial unbalances for balancing. In order to overcome the difficulty in balancing of dual rotor system, an identification algorithm based on Modified Influence Coefficient Method (MICM) is developed for the simultaneous estimation of residual unbalances in both inner and outer rotors with generation of virtual trial unbalances as magnetic excitation through AMB. The controlling action of AMB attenuates the vibrational response of the system within the required limit and allow the safe operation of the system in the presence of rotor faults and additional excitations. The vibrational responses of the system at the limited locations and the magnitude and phase of the virtual trial unbalances are only required in the MICM for the estimation of unbalances. To numerically illustrate the present methodology, the displacement response are obtained from the developed finite element model of the dual rotor system with discrete disc unbalances and randomly distributed shaft. The robustness of the algorithm in estimation of residual unbalances is verified with the addition of different percentage of measurement noises. After balancing, the dual rotor system is found to traverse its critical speed with less vibrational response.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":"10 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74311094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The dynamic response of a Duffing system from self-induced resonance to system resonance is studied in this paper. From numerical simulation, it is found that the system response gradually transits from self-induced resonance to system resonance with the increase of the pulse amplitude of the signal. In order to describe this process, we define the quality factor of the system response. With the evolution from self-induced resonance to system resonance, the quality factor gradually increases from 0 to 1. Then, based on the evolution, a novel method is developed to evaluate the severity of rolling bearing early damage. The results show that the method can not only be used to describe the process of a rolling bearing from healthy to damaged, but also to evaluate the severity of the early damage of a rolling bearing. The quality factor is a key index to reflect the severity of a rolling bearing. In addition, the sensitivity of the quality factor is superior to other traditional indices former used in the early damage evaluation. The effective method gives a new way for rolling bearing early damage evaluation.
{"title":"Rolling Bearing Damage Evaluation by the Dynamic Process From Self-Induced Resonance to System Resonance of a Duffing System","authors":"Shuai Zhang, Zhongqiu Wang, Jianhua Yang","doi":"10.1115/1.4054694","DOIUrl":"https://doi.org/10.1115/1.4054694","url":null,"abstract":"\u0000 The dynamic response of a Duffing system from self-induced resonance to system resonance is studied in this paper. From numerical simulation, it is found that the system response gradually transits from self-induced resonance to system resonance with the increase of the pulse amplitude of the signal. In order to describe this process, we define the quality factor of the system response. With the evolution from self-induced resonance to system resonance, the quality factor gradually increases from 0 to 1. Then, based on the evolution, a novel method is developed to evaluate the severity of rolling bearing early damage. The results show that the method can not only be used to describe the process of a rolling bearing from healthy to damaged, but also to evaluate the severity of the early damage of a rolling bearing. The quality factor is a key index to reflect the severity of a rolling bearing. In addition, the sensitivity of the quality factor is superior to other traditional indices former used in the early damage evaluation. The effective method gives a new way for rolling bearing early damage evaluation.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":"163 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78713903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Inspection and Maintenance methods development have a pivotal role in preventing the uncertainty-induced risks in the oil and gas industry. A key aspect of inspection is evaluating the risk of equipment from the scheduled and monitored assessment in the dynamic system. This activity includes assessing the modification factor's Probability of Failure (PoF) and calculating the equipment's remaining useful life (RUL). The traditional inspection model constitutes a partial solution to grouping the vast amount of real-data inspection and observations at equal intervals. This literature review aims to offer a comprehensive review concerning the benefit of Machine Learning (ML) in managing the risk while incorporating time-series forecasting studies and an overview of Risk-Based Inspection (RBI) methods (e.g. quantitative, semi-quantitative, and qualitative). A literature review with a deductive approach is used to discuss the improvement of the clustering Gaussian Mixture Model (GMM) to overcome the non-circular shape data that may show in the K-Means models. Machine Learning classifiers such as Decision Trees, Logistic Regression, Support Vector Machines, K-nearest neighbours, and Random Forests were selected to provide a platform for risk assessment and give a promising prediction towards the actual condition and their severity level of equipment. This work approaches complementary tools and grows interest in embedded artificial intelligence in Risk Management systems and can be used as the basis of more robust guidance to organize complexity in handling inspection data, but further and future research is required.
{"title":"A Recent Review of Risk-Based Inspection Development to Support Service Excellence in the Oil and Gas Industry: An Artificial Intelligence Perspective","authors":"Taufik Aditiyawarman, A. Kaban, J. Soedarsono","doi":"10.1115/1.4054558","DOIUrl":"https://doi.org/10.1115/1.4054558","url":null,"abstract":"\u0000 Inspection and Maintenance methods development have a pivotal role in preventing the uncertainty-induced risks in the oil and gas industry. A key aspect of inspection is evaluating the risk of equipment from the scheduled and monitored assessment in the dynamic system. This activity includes assessing the modification factor's Probability of Failure (PoF) and calculating the equipment's remaining useful life (RUL). The traditional inspection model constitutes a partial solution to grouping the vast amount of real-data inspection and observations at equal intervals. This literature review aims to offer a comprehensive review concerning the benefit of Machine Learning (ML) in managing the risk while incorporating time-series forecasting studies and an overview of Risk-Based Inspection (RBI) methods (e.g. quantitative, semi-quantitative, and qualitative). A literature review with a deductive approach is used to discuss the improvement of the clustering Gaussian Mixture Model (GMM) to overcome the non-circular shape data that may show in the K-Means models. Machine Learning classifiers such as Decision Trees, Logistic Regression, Support Vector Machines, K-nearest neighbours, and Random Forests were selected to provide a platform for risk assessment and give a promising prediction towards the actual condition and their severity level of equipment. This work approaches complementary tools and grows interest in embedded artificial intelligence in Risk Management systems and can be used as the basis of more robust guidance to organize complexity in handling inspection data, but further and future research is required.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":"33 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88435826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Banyay, Matthew J. Palamara, Jessica Preston, Stephen D. Smith
Use of neutron noise analysis in pressurized water reactors to detect and diagnose degradation represents the practice of proactive structural health monitoring for reactor vessel internals. Recent enhancements to this remote condition monitoring and diagnostic computational framework quantify the sensitivity of the structural dynamics to different degradation scenarios. This methodology leverages benchmarked computational structural mechanics models and machine learning methods to enhance interpretability of neutron noise measurement results. The novelty of the methodology lies not in the particular technologies and algorithms but in our amalgamation into a holistic computational framework for structural health monitoring. Recent experience revealed successful deployment of this methodology to proactively diagnose different degradation scenarios, thus enabling prognostic asset management for reactor structures.
{"title":"Mechanics Informed Neutron Noise Monitoring to Perform Remote Condition Assessment for Reactor Vessel Internals","authors":"G. Banyay, Matthew J. Palamara, Jessica Preston, Stephen D. Smith","doi":"10.1115/1.4054444","DOIUrl":"https://doi.org/10.1115/1.4054444","url":null,"abstract":"\u0000 Use of neutron noise analysis in pressurized water reactors to detect and diagnose degradation represents the practice of proactive structural health monitoring for reactor vessel internals. Recent enhancements to this remote condition monitoring and diagnostic computational framework quantify the sensitivity of the structural dynamics to different degradation scenarios. This methodology leverages benchmarked computational structural mechanics models and machine learning methods to enhance interpretability of neutron noise measurement results. The novelty of the methodology lies not in the particular technologies and algorithms but in our amalgamation into a holistic computational framework for structural health monitoring. Recent experience revealed successful deployment of this methodology to proactively diagnose different degradation scenarios, thus enabling prognostic asset management for reactor structures.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":"2 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85362246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Special Section on Risk, Resilience and Reliability for Autonomous Vehicle Technologies: Trend, Techniques and Challenges","authors":"M. Pourgol-Mohammad, A. Veeramany, B. Ayyub","doi":"10.1115/1.4054384","DOIUrl":"https://doi.org/10.1115/1.4054384","url":null,"abstract":"\u0000 <jats:p>N/A</jats:p>","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":"66 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77959876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Y. L. Maia, P. F. F. Frutuoso e Melo, T.Q. de Linhares, B. Pinho
At the end of the nuclear-powered submarines (NS) operational life, they have to be defueled, decommissioned and their nuclear reactors have to be safely disposed of. Their specific decommis-sioning process may be adapted to suit the needs of the decommissioning and storage process of small modular reactors (SMR) and reactors installed in floating devices. This paper addres-ses: 1- the decommissioning of NS, 2- the safe interim storage of their reactor compartments (RC), and 3- proposes a multicriteria decision-making (MCDM) approach for the RC interim storage facility site selection process, all focused on the Brazilian case. This approach is based on the application of the Analytic Hierarchy Process (AHP).
{"title":"Decommissioning of Nuclear Submarines and the Interim Storage of Their Reactor Compartments","authors":"Y. L. Maia, P. F. F. Frutuoso e Melo, T.Q. de Linhares, B. Pinho","doi":"10.1115/1.4054385","DOIUrl":"https://doi.org/10.1115/1.4054385","url":null,"abstract":"\u0000 At the end of the nuclear-powered submarines (NS) operational life, they have to be defueled, decommissioned and their nuclear reactors have to be safely disposed of. Their specific decommis-sioning process may be adapted to suit the needs of the decommissioning and storage process of small modular reactors (SMR) and reactors installed in floating devices. This paper addres-ses: 1- the decommissioning of NS, 2- the safe interim storage of their reactor compartments (RC), and 3- proposes a multicriteria decision-making (MCDM) approach for the RC interim storage facility site selection process, all focused on the Brazilian case. This approach is based on the application of the Analytic Hierarchy Process (AHP).","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":"13 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84943478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Majed Hamed, Yujie Mao, B. Ayyub, Magdy Elsibaie, Tarek Omar
Freight rail networks serve a key role in transporting bulk goods to accommodate changing market demands and to serve public needs. Network analyses of such systems can provide important insights into enhancing transportation efficiency and system resilience. This paper develops and investigates a topological analysis model for network efficiency, which is associated with the connectedness of a network's nodes by its links and their corresponding network attributes. This model allows analyzing network topologies with or without assigned weights to their nodes and links based on different attributes. Key attributes include physical length of links, dwell-time at nodes, types of goods moved, and origins and destination of goods. The model presented here enables (1) defining distinctions that may be employed for the assignment of node and link weights, (2) gaining an understanding of node and link criticality, and (3) providing methods for objectively maintaining and enhancing network performance. Such analyses can inform rail managers and executives in planning expansions, route or freight changes, or preparations for potential node or link failures. A case study of an aggregated U.S. freight rail network along with other example topologies is presented to demonstrate the use of selected network attributes and their influence on connectedness efficiency and the impacts of node and link failures on the overall transport efficiency.
{"title":"Connectedness Efficiency Analysis of Weighted U. S. Freight Railroad Networks","authors":"Majed Hamed, Yujie Mao, B. Ayyub, Magdy Elsibaie, Tarek Omar","doi":"10.1115/1.4054326","DOIUrl":"https://doi.org/10.1115/1.4054326","url":null,"abstract":"\u0000 Freight rail networks serve a key role in transporting bulk goods to accommodate changing market demands and to serve public needs. Network analyses of such systems can provide important insights into enhancing transportation efficiency and system resilience. This paper develops and investigates a topological analysis model for network efficiency, which is associated with the connectedness of a network's nodes by its links and their corresponding network attributes. This model allows analyzing network topologies with or without assigned weights to their nodes and links based on different attributes. Key attributes include physical length of links, dwell-time at nodes, types of goods moved, and origins and destination of goods. The model presented here enables (1) defining distinctions that may be employed for the assignment of node and link weights, (2) gaining an understanding of node and link criticality, and (3) providing methods for objectively maintaining and enhancing network performance. Such analyses can inform rail managers and executives in planning expansions, route or freight changes, or preparations for potential node or link failures. A case study of an aggregated U.S. freight rail network along with other example topologies is presented to demonstrate the use of selected network attributes and their influence on connectedness efficiency and the impacts of node and link failures on the overall transport efficiency.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":"14 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74568267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Corrosion in hull structure of Coast Guard cutters is a primary degradation mode that accounts for a significant portion of depot budgets and the occasional unavailability of ships in general. Corrosion exhibits great variability spatially and temporally. This paper presents, summarizes, and analyzes a one-of-a-kind data set for end-of-life corrosion estimation and profile of ship hull structure. The data set was created over several years and on several vessels, and collected by maintenance personnel at several geographic locations. This study analyzes wastage data due to corrosion that were systematically collected in 2007 to 2008 from twelve 210-foot Medium Endurance Cutters, commissioned in 1964 to 1969, in the form of thickness measurement using visual inspection and ultrasonic testing methods. A total of 76,091 thickness measurements were analyzed at positions covering the entire hulls. The measured corrosion levels mean is about 0.02 to 0.04 inches (1 in. = 25.4 mm), i.e., 6 to 14% of the as-built thicknesses after no more than 43 years of use of these 12 cutters as of 2007; however, the analysis of outliers indicates that the average wastage values can be misleading in predicting extreme corrosion. A method is proposed for estimating the counts and intensity of outliers. Examining geographic locations of the operations of these cutters and corrosion revealed that southern warm water led to appreciably larger corrosion compared to the northern colder waters, at a ratio of about 1.25 to 1.5.
{"title":"End-of-Life Corrosion Estimation and Profile of Ship Hull Structure: Non-Parametric Statistical Analysis of Medium Endurance Cutters","authors":"B. Ayyub, K. Stambaugh, William L. McGill","doi":"10.1115/1.4054325","DOIUrl":"https://doi.org/10.1115/1.4054325","url":null,"abstract":"\u0000 Corrosion in hull structure of Coast Guard cutters is a primary degradation mode that accounts for a significant portion of depot budgets and the occasional unavailability of ships in general. Corrosion exhibits great variability spatially and temporally. This paper presents, summarizes, and analyzes a one-of-a-kind data set for end-of-life corrosion estimation and profile of ship hull structure. The data set was created over several years and on several vessels, and collected by maintenance personnel at several geographic locations. This study analyzes wastage data due to corrosion that were systematically collected in 2007 to 2008 from twelve 210-foot Medium Endurance Cutters, commissioned in 1964 to 1969, in the form of thickness measurement using visual inspection and ultrasonic testing methods. A total of 76,091 thickness measurements were analyzed at positions covering the entire hulls. The measured corrosion levels mean is about 0.02 to 0.04 inches (1 in. = 25.4 mm), i.e., 6 to 14% of the as-built thicknesses after no more than 43 years of use of these 12 cutters as of 2007; however, the analysis of outliers indicates that the average wastage values can be misleading in predicting extreme corrosion. A method is proposed for estimating the counts and intensity of outliers. Examining geographic locations of the operations of these cutters and corrosion revealed that southern warm water led to appreciably larger corrosion compared to the northern colder waters, at a ratio of about 1.25 to 1.5.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":"30 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89906568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Developing Climate Resilience Technologies for Infrastructure: Perspectives On Some Strategic Needs in Mechanical Engineering","authors":"B. Ayyub, D. Walker","doi":"10.1115/1.4054180","DOIUrl":"https://doi.org/10.1115/1.4054180","url":null,"abstract":"\u0000 <jats:p>N/A</jats:p>","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":"2 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89751760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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