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Proceedings Papers

Optimizing the Prioritization of First-Time ILIs Using Quantitative Risk and Machine Learning

Brenn Snider, Wei Xiang, Billy Zhang, Sergiu Lecut
Proc. ASME. IPC2022, Volume 1: Pipeline Safety Management Systems; Project Management, Design, Construction, and Environmental Issues; Strain-Based Design and Assessment; Risk and Reliability; Emerging Fuels and Greenhouse Gas Emissions, V001T07A030, September 26–30, 2022
Paper No: IPC2022-87309
DOI: https://doi.org/10.1115/IPC2022-87309
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Topics: Machine learning, Risk, Risk assessment, Risk reduction
Proceedings Papers

Estimating Pipeline Probability of Failure Due to External Interference Damage Using Machine Learning Algorithms Trained on In-Line Inspection Data

James White, Katherine Taylor, Jonny Martin, Steven Carrell, Roland Palmer-Jones
Proc. ASME. IPC2022, Volume 1: Pipeline Safety Management Systems; Project Management, Design, Construction, and Environmental Issues; Strain-Based Design and Assessment; Risk and Reliability; Emerging Fuels and Greenhouse Gas Emissions, V001T07A009, September 26–30, 2022
Paper No: IPC2022-87093
DOI: https://doi.org/10.1115/IPC2022-87093
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Topics: Algorithms, Damage, Failure, Inspection, Machine learning, Pipelines, Probability
Proceedings Papers

Machine Learning-Based Severity Assessment of Pipeline Dents

Huang Tang, Jialin Sun, Martin Di Blasi
Proc. ASME. IPC2022, Volume 1: Pipeline Safety Management Systems; Project Management, Design, Construction, and Environmental Issues; Strain-Based Design and Assessment; Risk and Reliability; Emerging Fuels and Greenhouse Gas Emissions, V001T07A019, September 26–30, 2022
Paper No: IPC2022-87211
DOI: https://doi.org/10.1115/IPC2022-87211
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Topics: Finite element analysis, Machine learning, Machinery, Pipelines, Engineering prototypes, Failure, Maintenance, Reliability, Damage, Deformation
Proceedings Papers

Building Digital Competencies and Cultivating Innovation in an Integrity Environment

Karmun Doucette, Janine Woo
Proc. ASME. IPC2022, Volume 1: Pipeline Safety Management Systems; Project Management, Design, Construction, and Environmental Issues; Strain-Based Design and Assessment; Risk and Reliability; Emerging Fuels and Greenhouse Gas Emissions, V001T01A001, September 26–30, 2022
Paper No: IPC2022-87051
DOI: https://doi.org/10.1115/IPC2022-87051
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Topics: Competencies, Innovation, Safety, Inspection, Performance, Teams, Computer software, Ethics, Leadership, Machine learning
Proceedings Papers

Incorporating Measurement Uncertainty Into Machine Learning-Based Grade Predictions

Joel Anderson, Nathan Switzner, Jeffrey Kornuta, Peter Veloo
Proc. ASME. IPC2022, Volume 1: Pipeline Safety Management Systems; Project Management, Design, Construction, and Environmental Issues; Strain-Based Design and Assessment; Risk and Reliability; Emerging Fuels and Greenhouse Gas Emissions, V001T07A032, September 26–30, 2022
Paper No: IPC2022-87347
DOI: https://doi.org/10.1115/IPC2022-87347
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Topics: Machine learning, Machinery, Measurement uncertainty, Pipes, Nondestructive evaluation, Probability, Simulation, Uncertainty, Pacific Ocean, Regulations
Proceedings Papers

Overcoming Challenges for Quantitative Risk Modeling Using Machine Learned Data Correlations and Predictive Modeling

Stephen F. Biagiotti, Jr, Dan Williams, Sergiy Kondratyuk, Brett Johnson
Proc. ASME. IPC2022, Volume 2: Pipeline and Facilities Integrity, V002T03A004, September 26–30, 2022
Paper No: IPC2022-87236
DOI: https://doi.org/10.1115/IPC2022-87236
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Topics: Machine learning, Machinery, Modeling, Risk
Proceedings Papers

Comparison of Machine Learning Models for Quantitative Risk Modelling of Pipeline Systems

Daryl Bandstra, Juan S. Rojas, Alex Fraser, Mari Shironishi
Proc. ASME. IPC2022, Volume 1: Pipeline Safety Management Systems; Project Management, Design, Construction, and Environmental Issues; Strain-Based Design and Assessment; Risk and Reliability; Emerging Fuels and Greenhouse Gas Emissions, V001T07A025, September 26–30, 2022
Paper No: IPC2022-87258
DOI: https://doi.org/10.1115/IPC2022-87258
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Topics: Machine learning, Modeling, Pipeline systems, Risk, Artificial neural networks, Failure, Fittings, Industrial research, Pipelines, Pipes
Proceedings Papers

Probabilistic Analysis Applied to the Risk of SCC Failure

Scott Riccardella, Owen Malinowski, Pete Riccardella, Steve Potts, Sean Moran, Kelly Thompson, Ann Reo
Proc. ASME. IPC2022, Volume 1: Pipeline Safety Management Systems; Project Management, Design, Construction, and Environmental Issues; Strain-Based Design and Assessment; Risk and Reliability; Emerging Fuels and Greenhouse Gas Emissions, V001T07A005, September 26–30, 2022
Paper No: IPC2022-86906
DOI: https://doi.org/10.1115/IPC2022-86906
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Topics: Artificial neural networks, Failure, Machine learning, Risk, Probability, Algorithms, Coating processes, Coatings, Compressors, Fracture mechanics
Proceedings Papers

Structured, Systematic Threat Based Approach to Evaluate and Improve Data Quality to Facilitate Digital Transformation

Pushpendra Tomar, Betsy Kruse, Samah Hasan, Sergiy Kondratyuk
Proc. ASME. IPC2022, Volume 2: Pipeline and Facilities Integrity, V002T03A005, September 26–30, 2022
Paper No: IPC2022-87872
DOI: https://doi.org/10.1115/IPC2022-87872
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Topics: Cloud computing, Coating processes, Coatings, Data storage systems, Machine learning, Pipeline integrity, Pipelines, Reliability, Risk, Risk assessment
Proceedings Papers

Distribution Pipeline Risk Framework

Jason Skow, Ryan Stewart, Rob McPherson, Kent Schoenroth
Proc. ASME. IPC2022, Volume 1: Pipeline Safety Management Systems; Project Management, Design, Construction, and Environmental Issues; Strain-Based Design and Assessment; Risk and Reliability; Emerging Fuels and Greenhouse Gas Emissions, V001T07A008, September 26–30, 2022
Paper No: IPC2022-87066
DOI: https://doi.org/10.1115/IPC2022-87066
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Topics: Machine learning, Pipelines, Risk
Proceedings Papers

Not All Data Is Good Data: The Challenges of Using Machine Learning With ILI

Adrian Belanger, Dane Burden, Paul Dalfonso
Proc. ASME. IPC2022, Volume 2: Pipeline and Facilities Integrity, V002T03A023, September 26–30, 2022
Paper No: IPC2022-86934
DOI: https://doi.org/10.1115/IPC2022-86934
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Topics: Machine learning, Nondestructive evaluation, Errors, Metals, Pipelines, Cycles, Inspection, Accuracy and precision, Dimensions, Ditches
Proceedings Papers

Addressing Data Gaps for Facility Reliability Assessments Using Non-Hierarchical Cluster Analysis

Ryan Stewart, Martin Di Blasi, Thomas Dessein
Proc. ASME. IPC2022, Volume 1: Pipeline Safety Management Systems; Project Management, Design, Construction, and Environmental Issues; Strain-Based Design and Assessment; Risk and Reliability; Emerging Fuels and Greenhouse Gas Emissions, V001T07A013, September 26–30, 2022
Paper No: IPC2022-87145
DOI: https://doi.org/10.1115/IPC2022-87145
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Topics: Machine learning, Reliability, Risk assessment, Risk, Algorithms, Compressors, Data collection, Materials properties, Pipelines, Pressure
Proceedings Papers

Pipeline Defect Detection and Fine-Scale Reconstruction From 3-D MFL Signal Analysis Using Object Detection and Physics-Constrained Machine Learning

W. Steven Rosenthal, Stephen Westwood, Kayte Denslow
Proc. ASME. IPC2022, Volume 2: Pipeline and Facilities Integrity, V002T03A060, September 26–30, 2022
Paper No: IPC2022-87313
DOI: https://doi.org/10.1115/IPC2022-87313
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Topics: Flaw detection, Machine learning, Physics, Pipelines, Signals
Proceedings Papers

Development of a Near-Neutral pH Stress Corrosion Cracking Growth Model for Pipelines Using Machine Learning Algorithms

Haotian Sun, Wenxing Zhou, Jidong Kang
Proc. ASME. IPC2022, Volume 2: Pipeline and Facilities Integrity, V002T03A059, September 26–30, 2022
Paper No: IPC2022-87207
DOI: https://doi.org/10.1115/IPC2022-87207
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Topics: Algorithms, Fracture (Materials), Machine learning, Pipelines, Stress corrosion cracking
Proceedings Papers

Machine Learning Tools to Predict the Burst Capacity of Pipelines Containing Dent-Gouges

Ziming He, Wenxing Zhou
Proc. ASME. IPC2022, Volume 2: Pipeline and Facilities Integrity, V002T03A058, September 26–30, 2022
Paper No: IPC2022-87176
DOI: https://doi.org/10.1115/IPC2022-87176
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Topics: Fitness-for-service, Graphical user interfaces, Machine learning, Pipelines
Proceedings Papers

Morphological Change Detection at Pipeline Crossings Using Remote Sensing: A Proof-of-Concept Study

Rudy Schueder, Jeanine Engelbrecht
Proc. ASME. IPC2022, Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining, V003T04A013, September 26–30, 2022
Paper No: IPC2022-87155
DOI: https://doi.org/10.1115/IPC2022-87155
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Topics: Geohazards, Imaging, Pipelines, Synthetic aperture radar, Rivers, Algorithms, Satellites, Computer programming languages, Computer software, Computers
Proceedings Papers

A Feature-Specific Probabilistic Assessment of Pipeline Defect Size From ILI MFL Signal Using Convolutional Neural Network

Jenny Jing Chen, Stephen Westwood, David Heaney
Proc. ASME. IPC2020, Volume 1: Pipeline and Facilities Integrity, V001T03A068, September 28–30, 2020
Paper No: IPC2020-9331
DOI: https://doi.org/10.1115/IPC2020-9331
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Topics: Artificial neural networks, Corrosion, Machine learning, Pipelines, Signals
Proceedings Papers

Concerted, Computing-Intense Novel MFL Approach Ensuring Reliability and Reducing the Need for Dig Verification

Johannes Palmer, Aaron Schartner, Andrey Danilov, Vincent Tse
Proc. ASME. IPC2020, Volume 1: Pipeline and Facilities Integrity, V001T03A069, September 28–30, 2020
Paper No: IPC2020-9361
DOI: https://doi.org/10.1115/IPC2020-9361
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Topics: Reliability, Metals, Signals, Optimization, Ambiguity, Artificial intelligence, Geometry, Leakage, Machine learning, Magnetic flux
Proceedings Papers

Distribution Reliability Using Machine Learning

Jason Skow, Rob McPherson, Kent Shoenroth
Proc. ASME. IPC2020, Volume 1: Pipeline and Facilities Integrity, V001T03A070, September 28–30, 2020
Paper No: IPC2020-9373
DOI: https://doi.org/10.1115/IPC2020-9373
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Topics: Machine learning, Reliability, Damage, Leakage, Pipelines
Proceedings Papers

Statistical Analysis of Dig Operations Leading to Productive Repairs

Yevgeniy Petrov, Jordan Dubuc, Michael H. Murray, Tim Edward
Proc. ASME. IPC2020, Volume 1: Pipeline and Facilities Integrity, V001T03A073, September 28–30, 2020
Paper No: IPC2020-9493
DOI: https://doi.org/10.1115/IPC2020-9493
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Topics: Maintenance, Statistical analysis, Inspection, Computer software, Corrosion, Data storage systems, Half-life, Machine learning, Modeling, Pipeline integrity