1-14 of 14
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

Machine Learning Models of Burst Strength for Defect-Free Pipelines

Xian-Kui Zhu, William R. Johnson, Robert Sindelar, Bruce Wiersma
Proc. ASME. PVP2022, Volume 4B: Materials and Fabrication, V04BT06A043, July 17–22, 2022
Paper No: PVP2022-84908
DOI: https://doi.org/10.1115/PVP2022-84908
View Paper titled, Machine Learning Models of Burst Strength for Defect-Free Pipelines
Click here to open pdf in another window PDF for Machine Learning Models of Burst Strength for Defect-Free Pipelines
Topics: Artificial neural networks, Machine learning, Pipelines
Proceedings Papers

A Recurrent Neural Network Method for Condition Monitoring and Predictive Maintenance of Pressure Vessel Components

Chris Halliday, Iain Palmer, Nigel Pready, Mark Joyce
Proc. ASME. PVP2022, Volume 5: Operations, Applications, and Components; Seismic Engineering; ASME Nondestructive Evaluation, Diagnosis and Prognosis (NDPD) Division, V005T07A003, July 17–22, 2022
Paper No: PVP2022-84696
DOI: https://doi.org/10.1115/PVP2022-84696
View Paper titled, A Recurrent Neural Network Method for Condition Monitoring and Predictive Maintenance of Pressure Vessel Components
Click here to open pdf in another window PDF for A Recurrent Neural Network Method for Condition Monitoring and Predictive Maintenance of Pressure Vessel Components
Topics: Artificial intelligence, Artificial neural networks, Condition monitoring, Machine learning, Maintenance, Pressure vessels
Proceedings Papers

Predicting the Projectile Velocity of a Two-Stage Gas Gun Using Machine Learning

Pouya Shojaei, Mohamed Trabia, Brendan O’Toole, Richard Jennings
Proc. ASME. PVP2022, Volume 3: Fluid-Structure Interaction; High Pressure Technology, V003T05A014, July 17–22, 2022
Paper No: PVP2022-79815
DOI: https://doi.org/10.1115/PVP2022-79815
View Paper titled, Predicting the Projectile Velocity of a Two-Stage Gas Gun Using Machine Learning
Click here to open pdf in another window PDF for Predicting the Projectile Velocity of a Two-Stage Gas Gun Using Machine Learning
Topics: Machine learning, Projectiles
Proceedings Papers

Data-Driven Stress Intensity Factor Solutions for Axial Outside Surface Cracks in Thick-Wall Cylinders

Xian-Kui Zhu, Jesse B. Zhu, Andrew J. Duncan
Proc. ASME. PVP2022, Volume 4A: Materials and Fabrication, V04AT06A032, July 17–22, 2022
Paper No: PVP2022-86164
DOI: https://doi.org/10.1115/PVP2022-86164
View Paper titled, Data-Driven Stress Intensity Factor Solutions for Axial Outside Surface Cracks in Thick-Wall Cylinders
Click here to open pdf in another window PDF for Data-Driven Stress Intensity Factor Solutions for Axial Outside Surface Cracks in Thick-Wall Cylinders
Topics: American Petroleum Institute, ASME Boiler and Pressure Vessel Code, Cylinders, Machine learning, Stress, Surface cracks
Proceedings Papers

Coupling of Neutron Noise and Dynamic Finite Element Analyses to Perform Remote Condition Monitoring for Reactor Vessel Internals

Gregory A. Banyay, Matthew J. Palamara, Stephen D. Smith
Proc. ASME. PVP2021, Volume 3: Fluid Structure Interaction; High Pressure Technology, V003T04A006, July 13–15, 2021
Paper No: PVP2021-62201
DOI: https://doi.org/10.1115/PVP2021-62201
View Paper titled, Coupling of Neutron Noise and Dynamic Finite Element Analyses to Perform Remote Condition Monitoring for Reactor Vessel Internals
Click here to open pdf in another window PDF for Coupling of Neutron Noise and Dynamic Finite Element Analyses to Perform Remote Condition Monitoring for Reactor Vessel Internals
Topics: Condition monitoring, Finite element analysis, Heat shielding, Machine learning, Neutrons, Noise (Sound), Reactor vessels, Structural dynamics, Structural health monitoring
Proceedings Papers

Machine Learning Modeling of Dynamic Strength of Resistance Spot Welds in High Strength Steels

Jesse B. Zhu, Xian-Kui Zhu, Wei Zhang
Proc. ASME. PVP2021, Volume 4: Materials and Fabrication, V004T06A020, July 13–15, 2021
Paper No: PVP2021-63093
DOI: https://doi.org/10.1115/PVP2021-63093
View Paper titled, Machine Learning Modeling of Dynamic Strength of Resistance Spot Welds in High Strength Steels
Click here to open pdf in another window PDF for Machine Learning Modeling of Dynamic Strength of Resistance Spot Welds in High Strength Steels
Topics: Artificial neural networks, Electrical resistance, High strength steel, Machine learning, Modeling, Welded joints, Welding
Proceedings Papers

Application of Artificial Neural Network to Multi-Variables Regression for Estimations of J-Integral for Surface Cracked Pipes

Youn-Young Jang, Nam-Su Huh, Ik-Joong Kim, Cheol-Man Kim, Young-Pyo Kim
Proc. ASME. PVP2020, Volume 6: Materials and Fabrication, V006T06A098, August 3, 2020
Paper No: PVP2020-21668
DOI: https://doi.org/10.1115/PVP2020-21668
View Paper titled, Application of Artificial Neural Network to Multi-Variables Regression for Estimations of J-Integral for Surface Cracked Pipes
Click here to open pdf in another window PDF for Application of Artificial Neural Network to Multi-Variables Regression for Estimations of J-Integral for Surface Cracked Pipes
Topics: Artificial neural networks, Pipes, Materials properties, Finite element analysis, Fracture (Materials), Design, Displacement, Interpolation, Machine learning, Multilayer perceptron
Proceedings Papers

Using Machine Learning Tools for Forecasting Natural Gas Market Demand

Kai Yang, Lei Hou
Proc. ASME. PVP2020, Volume 8: Operations, Applications, and Components, V008T08A028, August 3, 2020
Paper No: PVP2020-21378
DOI: https://doi.org/10.1115/PVP2020-21378
View Paper titled, Using Machine Learning Tools for Forecasting Natural Gas Market Demand
Click here to open pdf in another window PDF for Using Machine Learning Tools for Forecasting Natural Gas Market Demand
Topics: Load forecasting, Machine learning, Natural gas, Pipelines, Support vector machines
Proceedings Papers

Welding Simulation Integrated With Machine Learning to Train a Meta-Model for Fast Exploration of Various Weld Sequence Scenarios

Mahyar Asadi, Mohammad Mohseni, Majid Tanbakuei Kashani, Michael Fernandez, Mathew Smith
Proc. ASME. PVP2019, Volume 6B: Materials and Fabrication, V06BT06A061, July 14–19, 2019
Paper No: PVP2019-93672
DOI: https://doi.org/10.1115/PVP2019-93672
View Paper titled, Welding Simulation Integrated With Machine Learning to Train a Meta-Model for Fast Exploration of Various Weld Sequence Scenarios
Click here to open pdf in another window PDF for Welding Simulation Integrated With Machine Learning to Train a Meta-Model for Fast Exploration of Various Weld Sequence Scenarios
Topics: Machine learning, Simulation, Trains, Welding, Algorithms, Welded joints, Engineers, Transients (Dynamics)
Proceedings Papers

Application of Machine Learning in Pipeline Integrity Reliability

Eunice Yin, Phil Fernandes, Janine Woo, Doug Langer, Sherif Hassanien
Proc. ASME. PVP2019, Volume 2: Computer Technology and Bolted Joints, V002T02A027, July 14–19, 2019
Paper No: PVP2019-93623
DOI: https://doi.org/10.1115/PVP2019-93623
View Paper titled, Application of Machine Learning in Pipeline Integrity Reliability
Click here to open pdf in another window PDF for Application of Machine Learning in Pipeline Integrity Reliability
Topics: Machine learning, Pipeline integrity, Reliability, Pipelines, Safety, Engineers, Algorithms, Failure, Filtration, Inspection
Proceedings Papers

Estimating Collapse Pressure of Centralizer Subs From Machine Learning Models

Ishita Chakraborty
Proc. ASME. PVP2019, Volume 2: Computer Technology and Bolted Joints, V002T02A028, July 14–19, 2019
Paper No: PVP2019-93638
DOI: https://doi.org/10.1115/PVP2019-93638
View Paper titled, Estimating Collapse Pressure of Centralizer Subs From Machine Learning Models
Click here to open pdf in another window PDF for Estimating Collapse Pressure of Centralizer Subs From Machine Learning Models
Topics: Collapse, Machine learning, Pressure, Finite element analysis, String, Regression models, American Petroleum Institute, Natural gas wells, Testing, Trains
Proceedings Papers

Application of Gaussian Process Regression for the Accuracy Assessment of a Three-Dimensional Strain-Based Model

Yueying Li, Yong Li, Sherif Hassanien, Chike Okoloekwe, Samer Adeeb
Proc. ASME. PVP2019, Volume 3: Design and Analysis, V003T03A098, July 14–19, 2019
Paper No: PVP2019-94039
DOI: https://doi.org/10.1115/PVP2019-94039
View Paper titled, Application of Gaussian Process Regression for the Accuracy Assessment of a Three-Dimensional Strain-Based Model
Click here to open pdf in another window PDF for Application of Gaussian Process Regression for the Accuracy Assessment of a Three-Dimensional Strain-Based Model
Topics: Computer simulation, Engineering simulation, Errors, Finite element analysis, Geometry, Machine learning, Pipelines, Pipes, Probability, Simulation
Proceedings Papers

The Role of Uncertainty in Machine Learning As an Element of Control for Material Systems and Structures

Muthu Ram Prabhu Elenchezhian, Md Rassel Raihan, Kenneth Reifsnider
Proc. ASME. PVP2018, Volume 6B: Materials and Fabrication, V06BT06A077, July 15–20, 2018
Paper No: PVP2018-84930
DOI: https://doi.org/10.1115/PVP2018-84930
View Paper titled, The Role of Uncertainty in Machine Learning As an Element of Control for Material Systems and Structures
Click here to open pdf in another window PDF for The Role of Uncertainty in Machine Learning As an Element of Control for Material Systems and Structures
Topics: Machine learning, Uncertainty, Composite materials, Physics, Aircraft, Artificial neural networks, Control systems, Failure, Algorithms, Density
Proceedings Papers

Identification of Two-Phase Flow Patterns Using Support Vector Classification

Ines Benito, Njuki W. Mureithi
Proc. ASME. PVP2017, Volume 4: Fluid-Structure Interaction, V004T04A027, July 16–20, 2017
Paper No: PVP2017-65179
DOI: https://doi.org/10.1115/PVP2017-65179
View Paper titled, Identification of Two-Phase Flow Patterns Using Support Vector Classification
Click here to open pdf in another window PDF for Identification of Two-Phase Flow Patterns Using Support Vector Classification
Topics: Two-phase flow, Flow (Dynamics), Support vector machines, Boilers, Design, Flow measurement, Artificial intelligence, Dynamics (Mechanics), Flow visualization, Flow-induced vibrations