1-20 of 48
Machine learning
Close
Save search
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Close Modal
Sort by
Proceedings Papers

Managing Scaleup: Installation of a Novel Integrity Monitoring System Through Challenging Terrain

Available to Purchase

Ehsan Jalilian, Steven Koles, Biju Kochatt
Proc. ASME. IPC2024, Volume 1: Pipeline Safety Management Systems; Project Management, Design, Construction, and Environment, V001T02A006, September 23–27, 2024
Publisher: American Society of Mechanical Engineers
Paper No: IPC2024-131772
https://doi.org/10.1115/IPC2024-131772
View Paper titled, Managing Scaleup: Installation of a Novel Integrity Monitoring System Through Challenging Terrain
Open the PDF for Managing Scaleup: Installation of a Novel Integrity Monitoring System Through Challenging Terrain in another window
Topics: Monitoring systems, Pipelines, Acoustics, Fibers, Leakage, Architecture, Construction, Algorithms, Augers, Cables
Proceedings Papers

Development of an Advanced 3D Profile Matching Approach for Pipeline Dent FEA Assessment

Available to Purchase

Luyao Xu, Alexander Brust, David Kemp, Shanshan Wu, Shane Finneran, Joseph P. Bratton
Proc. ASME. IPC2024, Volume 2A: Pipeline and Facilities Integrity, V02AT03A024, September 23–27, 2024
Publisher: American Society of Mechanical Engineers
Paper No: IPC2024-133117
https://doi.org/10.1115/IPC2024-133117
View Paper titled, Development of an Advanced 3D Profile Matching Approach for Pipeline Dent FEA Assessment
Open the PDF for Development of an Advanced 3D Profile Matching Approach for Pipeline Dent FEA Assessment in another window
Topics: Finite element analysis, Machine learning, Pipelines
Proceedings Papers

Accounting for Corrosion Growth and Interaction in Future Severity Assessments

Available to Purchase

Jane Dawson, Steve Farnie
Proc. ASME. IPC2024, Volume 2A: Pipeline and Facilities Integrity, V02AT03A005, September 23–27, 2024
Publisher: American Society of Mechanical Engineers
Paper No: IPC2024-129041
https://doi.org/10.1115/IPC2024-129041
View Paper titled, Accounting for Corrosion Growth and Interaction in Future Severity Assessments
Open the PDF for Accounting for Corrosion Growth and Interaction in Future Severity Assessments in another window
Topics: Accounting, Corrosion, Machine learning, Maintenance, Pipelines, Reliability, Safety, Three-dimensional modeling
Proceedings Papers

Trial and Error — Applications of Machine Learning Models to Pipeline Integrity Data

Available to Purchase

Mia Kalivoda, Parth Iyer, Wesley Allanbrook, Josh Goertzen
Proc. ASME. IPC2024, Volume 2B: Pipeline and Facilities Integrity, V02BT03A045, September 23–27, 2024
Publisher: American Society of Mechanical Engineers
Paper No: IPC2024-134109
https://doi.org/10.1115/IPC2024-134109
View Paper titled, Trial and Error — Applications of Machine Learning Models to Pipeline Integrity Data
Open the PDF for Trial and Error — Applications of Machine Learning Models to Pipeline Integrity Data in another window
Topics: Bayesian network, Errors, Machine learning, Natural language processing, Pipeline integrity
Proceedings Papers

Non-Destructive Evaluation of Pipe Seam Toughness via Frictional Sliding

Available to Purchase

Intisar Rizwan I Haque, Ryan Lacy, Simon Bellemare
Proc. ASME. IPC2024, Volume 2A: Pipeline and Facilities Integrity, V02AT03A037, September 23–27, 2024
Publisher: American Society of Mechanical Engineers
Paper No: IPC2024-133343
https://doi.org/10.1115/IPC2024-133343
View Paper titled, Non-Destructive Evaluation of Pipe Seam Toughness via Frictional Sliding
Open the PDF for Non-Destructive Evaluation of Pipe Seam Toughness via Frictional Sliding in another window
Topics: Fracture toughness, Nondestructive evaluation, Pipes, Phase transition temperature, American Petroleum Institute, Machine learning, Operating temperature, Temperature, Testing, Electrical resistance
Proceedings Papers

Development of an ILI Service for Heavy Wall Pipelines Based on EMAT Technology

Available to Purchase

Jay Upadhyaya, Clint Garth, Richard Kania, Aaron Schartner, Markus Hoeving, Timo Moritz, Thomas Beuker, Jens Voss
Proc. ASME. IPC2024, Volume 2A: Pipeline and Facilities Integrity, V02AT03A041, September 23–27, 2024
Publisher: American Society of Mechanical Engineers
Paper No: IPC2024-133450
https://doi.org/10.1115/IPC2024-133450
View Paper titled, Development of an ILI Service for Heavy Wall Pipelines Based on EMAT Technology
Open the PDF for Development of an ILI Service for Heavy Wall Pipelines Based on EMAT Technology in another window
Topics: Pipelines, Inspection, Testing, Teams, Acoustics, Design, Fracture (Materials), Industrial research, Machine learning, Magnetization
Proceedings Papers

Near-Neutral pH SCCDA Based on Data Analytics and Machine Learning

Available to Purchase

Larissa Monteiro, Pedro Géa, Ramon Loback, João Abal, João Pedro, Rodrigo Miranda, Sigmundo Preissler Junior
Proc. ASME. IPC2024, Volume 2B: Pipeline and Facilities Integrity, V02BT03A005, September 23–27, 2024
Publisher: American Society of Mechanical Engineers
Paper No: IPC2024-133742
https://doi.org/10.1115/IPC2024-133742
View Paper titled, Near-Neutral pH SCCDA Based on Data Analytics and Machine Learning
Open the PDF for Near-Neutral pH SCCDA Based on Data Analytics and Machine Learning in another window
Topics: Machine learning, Fracture (Materials), Pipes, Soil, Coating processes, Coatings, Pipelines, Stress, Dimensions, Geometry
Proceedings Papers

Dent Safe Excavation Pressure and Fracture Reliability Assessment With Machine Learning

Available to Purchase

Huang Tang, Martin Di Blasi, Chike Okoloekwe, Nader Yoosef-Ghodsi
Proc. ASME. IPC2024, Volume 2B: Pipeline and Facilities Integrity, V02BT03A017, September 23–27, 2024
Publisher: American Society of Mechanical Engineers
Paper No: IPC2024-133928
https://doi.org/10.1115/IPC2024-133928
View Paper titled, Dent Safe Excavation Pressure and Fracture Reliability Assessment With Machine Learning
Open the PDF for Dent Safe Excavation Pressure and Fracture Reliability Assessment With Machine Learning in another window
Topics: Finite element analysis, Fracture (Materials), Fracture (Process), Machine learning, Pipeline integrity, Pressure, Reliability
Proceedings Papers

Applying Advanced Machine Learning to Enhance Indication Discrimination and Data Analysis Results in Ultrasound Crack Diagnosis Surveys

Available to Purchase

Katja Träumner, Victor Ferrer, Ricard Mas, Achim Notheisen
Proc. ASME. IPC2024, Volume 2B: Pipeline and Facilities Integrity, V02BT03A018, September 23–27, 2024
Publisher: American Society of Mechanical Engineers
Paper No: IPC2024-133929
https://doi.org/10.1115/IPC2024-133929
View Paper titled, Applying Advanced Machine Learning to Enhance Indication Discrimination and Data Analysis Results in Ultrasound Crack Diagnosis Surveys
Open the PDF for Applying Advanced Machine Learning to Enhance Indication Discrimination and Data Analysis Results in Ultrasound Crack Diagnosis Surveys in another window
Topics: Fracture (Materials), Machine learning, Ultrasound
Proceedings Papers

Advancing Data Completeness and Strategically Directing Record Reviews With a Machine Learning Approach

Available to Purchase

Xiaoyu Sun, Daryl Bandstra, Ziming He, Carly Li, Miaad Safari
Proc. ASME. IPC2024, Volume 2B: Pipeline and Facilities Integrity, V02BT03A051, September 23–27, 2024
Publisher: American Society of Mechanical Engineers
Paper No: IPC2024-134143
https://doi.org/10.1115/IPC2024-134143
View Paper titled, Advancing Data Completeness and Strategically Directing Record Reviews With a Machine Learning Approach
Open the PDF for Advancing Data Completeness and Strategically Directing Record Reviews With a Machine Learning Approach in another window
Topics: Machine learning, Pipelines, Stress, Steel, Databases, Decision making, Pipeline integrity management, Pipeline systems, Pipes, Pressure
Proceedings Papers

Predictive Analytics Model for Natural Gas Transportation Consumption

Available to Purchase

Rachel Miranda, Rafaela Pedrassani, Marcus Nogueira, Wallthynay Arruda, Leticia Rodrigues, Rafael Paes
Proc. ASME. IPC2024, Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining, V003T04A013, September 23–27, 2024
Publisher: American Society of Mechanical Engineers
Paper No: IPC2024-133467
https://doi.org/10.1115/IPC2024-133467
View Paper titled, Predictive Analytics Model for Natural Gas Transportation Consumption
Open the PDF for Predictive Analytics Model for Natural Gas Transportation Consumption in another window
Topics: Machine learning, Natural gas, Transportation systems
Proceedings Papers

Modernizing Aerial Patrol - Real Life Remote Sensing for Liquid Leak and Threat Detection Along the Right of Way: First Detection Demonstrations of a Real Pipeline Seeper Leak, Encroachments, and Exposed Pipes Using a Traditional Aerial Patrol Plane

Available to Purchase

Paige Chenevey, Alexandre Thibeault, Joshua Beard, Eric Bergeron
Proc. ASME. IPC2024, Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining, V003T04A001, September 23–27, 2024
Publisher: American Society of Mechanical Engineers
Paper No: IPC2024-123410
https://doi.org/10.1115/IPC2024-123410
View Paper titled, Modernizing Aerial Patrol - Real Life Remote Sensing for Liquid Leak and Threat Detection Along the Right of Way: First Detection Demonstrations of a Real Pipeline Seeper Leak, Encroachments, and Exposed Pipes Using a Traditional Aerial Patrol Plane
Open the PDF for Modernizing Aerial Patrol - Real Life Remote Sensing for Liquid Leak and Threat Detection Along the Right of Way: First Detection Demonstrations of a Real Pipeline Seeper Leak, Encroachments, and Exposed Pipes Using a Traditional Aerial Patrol Plane in another window
Topics: Leakage, Machine learning, Photogrammetry, Pipelines, Pipes, Soil
Proceedings Papers

Strain Demand Prediction Model of Buried Pipeline Subjected to Tectonic Fault Displacement Via Deep Learning Models

Available to Purchase

Mengkai Fu, Hong Zhang, Dong Zhang, Jia Shao, Luyao Bai, Pengchao Chen, Hao Wang, Xiaoben Liu
Proc. ASME. IPC2024, Volume 4: Geohazards Management and Strain-Based Design and Assessment, V004T06A042, September 23–27, 2024
Publisher: American Society of Mechanical Engineers
Paper No: IPC2024-134243
https://doi.org/10.1115/IPC2024-134243
View Paper titled, Strain Demand Prediction Model of Buried Pipeline Subjected to Tectonic Fault Displacement Via Deep Learning Models
Open the PDF for Strain Demand Prediction Model of Buried Pipeline Subjected to Tectonic Fault Displacement Via Deep Learning Models in another window
Topics: Deep learning, Displacement, Finite element methods, Hazards, Machine learning, Pipelines
Proceedings Papers

Leak Detection Using Pressure Transmitters for Pipeline Networks Carrying Multi-Phase Fluids

Available to Purchase

Michael Roxas, Matthew Girard, Prakash Gunasekaran, Satya Mokamati
Proc. ASME. IPC2024, Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining, V003T04A028, September 23–27, 2024
Publisher: American Society of Mechanical Engineers
Paper No: IPC2024-134132
https://doi.org/10.1115/IPC2024-134132
View Paper titled, Leak Detection Using Pressure Transmitters for Pipeline Networks Carrying Multi-Phase Fluids
Open the PDF for Leak Detection Using Pressure Transmitters for Pipeline Networks Carrying Multi-Phase Fluids in another window
Topics: Emulsions, Fluids, Internet of things, Leakage, Machine learning, Pipelines, Pressure sensors, Algorithms, Pressure, American Petroleum Institute
Proceedings Papers

A Novel Strain Capacity Prediction Model of Girth Welded Joints of High-Grade Steel Pipeline Via Machine Learning Techniques and Refined FE Model

Available to Purchase

Xiaoben Liu, Dong Zhang, Haonan Zhang, Qingshan Feng, Dongying Wang, Chong Wang, Yue Yang, Hong Zhang
Proc. ASME. IPC2024, Volume 4: Geohazards Management and Strain-Based Design and Assessment, V004T06A038, September 23–27, 2024
Publisher: American Society of Mechanical Engineers
Paper No: IPC2024-134223
https://doi.org/10.1115/IPC2024-134223
View Paper titled, A Novel Strain Capacity Prediction Model of Girth Welded Joints of High-Grade Steel Pipeline Via Machine Learning Techniques and Refined FE Model
Open the PDF for A Novel Strain Capacity Prediction Model of Girth Welded Joints of High-Grade Steel Pipeline Via Machine Learning Techniques and Refined FE Model in another window
Topics: Finite element model, Fracture (Materials), Fracture toughness, Machine learning, Pipelines, Steel, Welded joints, Errors, Algorithms, Artificial neural networks
Proceedings Papers

Application of Gaussian Process Classification to the Integrity Management of Energy Pipelines

Available to Purchase

Haotian Sun
Proc. ASME. IPC2024, Volume 5: Risk and Reliability; Offshore, Upstream, and Production Pipelines; Emerging Fuels and Greenhouse Gas Emissions, V005T07A030, September 23–27, 2024
Publisher: American Society of Mechanical Engineers
Paper No: IPC2024-134103
https://doi.org/10.1115/IPC2024-134103
View Paper titled, Application of Gaussian Process Classification to the Integrity Management of Energy Pipelines
Open the PDF for Application of Gaussian Process Classification to the Integrity Management of Energy Pipelines in another window
Topics: Energy management, Failure mechanisms, Ignition, Machine learning, Pipeline integrity management, Pipelines, Probability, Algorithms, Decision making, Natural gas
Proceedings Papers

Assessing Likelihood of Failure for Unpigged Pipelines Using Supervised Machine Learning

Available to Purchase

Wei Xiang, Hassan Tayyab, Dongliang Lu, Colin Dooley, Sergiu Lucut
Proc. ASME. IPC2024, Volume 5: Risk and Reliability; Offshore, Upstream, and Production Pipelines; Emerging Fuels and Greenhouse Gas Emissions, V005T07A006, September 23–27, 2024
Publisher: American Society of Mechanical Engineers
Paper No: IPC2024-132363
https://doi.org/10.1115/IPC2024-132363
View Paper titled, Assessing Likelihood of Failure for Unpigged Pipelines Using Supervised Machine Learning
Open the PDF for Assessing Likelihood of Failure for Unpigged Pipelines Using Supervised Machine Learning in another window
Topics: Corrosion, Failure, Machine learning, Pipelines, Risk assessment
Proceedings Papers

Multiscale Multidisciplinary Machine Learning Modeling for Pipeline Risk Assessment

Available to Purchase

Guanlan Liu, Francois Ayello, Oleg Shabarchin, Gabriel Langlois-Rahme, Dongliang Lu
Proc. ASME. IPC2024, Volume 5: Risk and Reliability; Offshore, Upstream, and Production Pipelines; Emerging Fuels and Greenhouse Gas Emissions, V005T07A014, September 23–27, 2024
Publisher: American Society of Mechanical Engineers
Paper No: IPC2024-133536
https://doi.org/10.1115/IPC2024-133536
View Paper titled, Multiscale Multidisciplinary Machine Learning Modeling for Pipeline Risk Assessment
Open the PDF for Multiscale Multidisciplinary Machine Learning Modeling for Pipeline Risk Assessment in another window
Topics: Bayesian network, Machine learning, Modeling, Pipelines, Risk, Risk assessment
Proceedings Papers

In-Situ Concentration Measurement of Blended Hydrogen Gas Using Sensor Fusion Enhanced by Machine Learning Model

Available to Purchase

Marcos Costa, Hansaem Lee, Seonghwan Kim, Ron Hugo, Simon S. Park
Proc. ASME. IPC2024, Volume 5: Risk and Reliability; Offshore, Upstream, and Production Pipelines; Emerging Fuels and Greenhouse Gas Emissions, V005T09A001, September 23–27, 2024
Publisher: American Society of Mechanical Engineers
Paper No: IPC2024-120952
https://doi.org/10.1115/IPC2024-120952
View Paper titled, In-Situ Concentration Measurement of Blended Hydrogen Gas Using Sensor Fusion Enhanced by Machine Learning Model
Open the PDF for In-Situ Concentration Measurement of Blended Hydrogen Gas Using Sensor Fusion Enhanced by Machine Learning Model in another window
Topics: Hydrogen, Machine learning, Sensors, Thermal conductivity
Proceedings Papers

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

Available to Purchase

W. Steven Rosenthal, Stephen Westwood, Kayte Denslow
Proc. ASME. IPC2022, Volume 2: Pipeline and Facilities Integrity, V002T03A060, September 26–30, 2022
Publisher: American Society of Mechanical Engineers
Paper No: IPC2022-87313
https://doi.org/10.1115/IPC2022-87313
View Paper titled, Pipeline Defect Detection and Fine-Scale Reconstruction From 3-D MFL Signal Analysis Using Object Detection and Physics-Constrained Machine Learning
Open the PDF for Pipeline Defect Detection and Fine-Scale Reconstruction From 3-D MFL Signal Analysis Using Object Detection and Physics-Constrained Machine Learning in another window
Topics: Flaw detection, Machine learning, Physics, Pipelines, Signals