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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

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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

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Topics: Artificial intelligence, Artificial neural networks, Condition monitoring, Machine learning, Maintenance, Pressure vessels

Proceedings Papers

On the Use of Artificial Neural Network Technique for Seismic Fragility Analysis of a Three-Dimensional Industrial Frame

Gianluca Quinci, Nam Hoang Phan, Fabrizio Paolacci

Paper No: PVP2022-83874

DOI: https://doi.org/10.1115/PVP2022-83874

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Topics: Artificial neural networks, Simulation

Proceedings Papers

A Convolution Neural Network Based Fault Diagnosis Model for Slug Flow Condition of Oil-Gas Separator

Jiaquan Liu, Lei Hou, Xin Wang, Lei Xu, Rui Zhang, Kai Yang

Proc. ASME. PVP2021, Volume 5: Operations, Applications, and Components; Seismic Engineering; Non-Destructive Examination, V005T07A001, July 13–15, 2021

Paper No: PVP2021-61681

DOI: https://doi.org/10.1115/PVP2021-61681

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Topics: Artificial neural networks, Fault diagnosis, Slug flows

Proceedings Papers

Digital Twin of Buried Oil Pipe in Permafrost Regions: A Multi-Source Monitoring and Numerical Simulation Model

Beilei Ji, Xiaoben Liu, Ning Shi, Jianping Liu, Pengchao Chen, Dong Zhang, Hong Zhang

Proc. ASME. PVP2021, Volume 5: Operations, Applications, and Components; Seismic Engineering; Non-Destructive Examination, V005T07A021, July 13–15, 2021

Paper No: PVP2021-61991

DOI: https://doi.org/10.1115/PVP2021-61991

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Topics: Computer simulation, Optimization, Permafrost, Pipes, Pipelines, Computer software, Displacement, Algorithms, Artificial neural networks, China

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

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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

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Topics: Artificial neural networks, Pipes, Materials properties, Finite element analysis, Fracture (Materials), Design, Displacement, Interpolation, Machine learning, Multilayer perceptron

Proceedings Papers

A Hybrid PSO-BPNN Model Approach for Crude Oil Pipeline Electrical Energy Consumption Forecasting

Lei Xu, Lei Hou, Yu Li, Zhenyu Zhu, Jiaquan Liu, Ting Lei

Proc. ASME. PVP2020, Volume 8: Operations, Applications, and Components, V008T08A025, August 3, 2020

Paper No: PVP2020-21227

DOI: https://doi.org/10.1115/PVP2020-21227

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Topics: Artificial neural networks, Crude oil, Energy consumption, Particle swarm optimization, Pipelines, Principal component analysis

Proceedings Papers

CNN Based Defect Recognition Model for Phased Array Ultrasonic Testing Images of Electrofusion Joints

Jianfeng Shi, Yangji Tao, Weican Guo, Jinyang Zheng

Proc. ASME. PVP2020, Volume 6: Materials and Fabrication, V006T06A026, August 3, 2020

Paper No: PVP2020-21272

DOI: https://doi.org/10.1115/PVP2020-21272

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Topics: Artificial neural networks, Ultrasonic testing

Proceedings Papers

A Framework for Underground Gas Storage System Reliability Assessment Considering Functional Failure of Repairable Components

Lei He, Kai Wen, Jing Gong, Changchun Wu

Proc. ASME. PVP2019, Volume 7: Operations, Applications, and Components, V007T07A019, July 14–19, 2019

Paper No: PVP2019-93066

DOI: https://doi.org/10.1115/PVP2019-93066

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Topics: Artificial neural networks, Failure, Reliability, Simulation, Storage

Proceedings Papers

A Multi-Factor Comprehensive Evaluation Method for Pipeline Integrity

Jing Yang, Xiaolin Wang

Proc. ASME. PVP2019, Volume 7: Operations, Applications, and Components, V007T07A015, July 14–19, 2019

Paper No: PVP2019-93257

DOI: https://doi.org/10.1115/PVP2019-93257

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Topics: Artificial neural networks, Evaluation methods, Pipeline integrity

Proceedings Papers

High Performance Structural Vibration Control by a Preview of the Future Seismic Waveform Generated With a Wave Transmission Network and an AI-Based Estimation System

Kazuhiko Hiramoto, Taichi Matsuoka, Katsuaki Sunakoda

Proc. ASME. PVP2019, Volume 8: Seismic Engineering, V008T08A006, July 14–19, 2019

Paper No: PVP2019-93184

DOI: https://doi.org/10.1115/PVP2019-93184

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Topics: Vibration control, Waves, Earthquakes, Artificial neural networks, Feedforward control, Simulation, State feedback

Proceedings Papers

How to Select the Optimized Time Step and Mesh Size for FEM Thermal Transients Simulations of PWR Vessels and Nozzles by Means of Artificial Neural Networks

Nicolas Santucho, Martin Chimenti, Jose Duo

Proc. ASME. PVP2019, Volume 2: Computer Technology and Bolted Joints, V002T02A025, July 14–19, 2019

Paper No: PVP2019-93199

DOI: https://doi.org/10.1115/PVP2019-93199

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Topics: Artificial neural networks, Engineering simulation, Finite element methods, Finite element model, Nozzles, Pressurized water reactors, Simulation, Transients (Dynamics), Vessels, Stress

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

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Topics: Machine learning, Uncertainty, Composite materials, Physics, Aircraft, Artificial neural networks, Control systems, Failure, Algorithms, Density

Proceedings Papers

Research on Risk Assessment Method of Stick-Slip Vibration of the Bit Based on BP Neural Network Algorithm

Chong Chen, Shimin Zhang, Hang Zhang, Xiaojun Li, Zichen He

Proc. ASME. PVP2018, Volume 7: Operations, Applications, and Components, V007T07A026, July 15–20, 2018

Paper No: PVP2018-84144

DOI: https://doi.org/10.1115/PVP2018-84144

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Topics: Algorithms, Artificial neural networks, Risk assessment, Stick-slip, Vibration, Drilling, Drill strings, Safety, Dimensions, Frequency-domain analysis

Proceedings Papers

Scheduling Strategy of Multiple Semi-Active Controllers With Information on the Disturbance and the Structural Response

Kazuhiko Hiramoto, Taichi Matsuoka, Katsuaki Sunakoda

Proc. ASME. PVP2016, Volume 8: Seismic Engineering, V008T08A004, July 17–21, 2016

Paper No: PVP2016-63453

DOI: https://doi.org/10.1115/PVP2016-63453

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Topics: Control equipment, Artificial neural networks, Earthquakes, Control systems, Optimal control, Design, Generators, Genetic algorithms, Robustness, Signals

Proceedings Papers

Impact Resistance of Filament Wound Composite Pipes: A Parametric Study

Abul Fazal M. Arif, M. Haris Malik, A. S. Al-Omari

Proc. ASME. PVP2014, Volume 3: Design and Analysis, V003T03A013, July 20–24, 2014

Paper No: PVP2014-28941

DOI: https://doi.org/10.1115/PVP2014-28941

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Topics: Composite materials, Pipes, Artificial neural networks, Carbon reinforced plastics, Damage, Design, Failure, Finite element analysis, Glass reinforced plastics, Laminates

Proceedings Papers

Optimised Neural Network Prediction of Residual Stress Profiles for Structural Integrity Assessment of Pipe Girth Welds

Jino Mathew, Richard J. Moat, P. John Bouchard

Proc. ASME. PVP2014, Volume 5: High-Pressure Technology; ASME NDE Division; 22nd Scavuzzo Student Paper Symposium and Competition, V005T11A028, July 20–24, 2014

Paper No: PVP2014-28845

DOI: https://doi.org/10.1115/PVP2014-28845

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Topics: Artificial neural networks, Pipes, Stress, Welded joints, American Petroleum Institute, Electromagnetic scattering, Fracture (Materials), Fracture (Process), Inspection, Maintenance

Proceedings Papers

Prediction of Pipe Girth Weld Residual Stress Profiles Using Artificial Neural Networks

Jino Mathew, Richard J. Moat, P. John Bouchard

Proc. ASME. PVP2013, Volume 6B: Materials and Fabrication, V06BT06A075, July 14–18, 2013

Paper No: PVP2013-97491

DOI: https://doi.org/10.1115/PVP2013-97491

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Topics: Artificial neural networks, Pipes, Stress, Welded joints, Algorithms, American Petroleum Institute, Electromagnetic scattering, Inspection, Maintenance, Neutron diffraction

Proceedings Papers

Prediction of Fracture Toughness Transition From Tensile Test Data Applying Neural Networks

Ivo Dlouhy´, Hynek Hadraba, Zdeneˇk Chlup, Libor Va´lka, Libor Zˇa´k

Proc. ASME. PVP2011, Volume 1: Codes and Standards, 101-105, July 17–21, 2011

Paper No: PVP2011-57932

DOI: https://doi.org/10.1115/PVP2011-57932

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Topics: Artificial neural networks, Fracture toughness, Temperature, Alloys, Deformation, Steel

NARROW

Machine Learning Models of Burst Strength for Defect-Free Pipelines

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

On the Use of Artificial Neural Network Technique for Seismic Fragility Analysis of a Three-Dimensional Industrial Frame

A Convolution Neural Network Based Fault Diagnosis Model for Slug Flow Condition of Oil-Gas Separator

Digital Twin of Buried Oil Pipe in Permafrost Regions: A Multi-Source Monitoring and Numerical Simulation Model

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

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

A Hybrid PSO-BPNN Model Approach for Crude Oil Pipeline Electrical Energy Consumption Forecasting

CNN Based Defect Recognition Model for Phased Array Ultrasonic Testing Images of Electrofusion Joints

A Framework for Underground Gas Storage System Reliability Assessment Considering Functional Failure of Repairable Components

A Multi-Factor Comprehensive Evaluation Method for Pipeline Integrity

High Performance Structural Vibration Control by a Preview of the Future Seismic Waveform Generated With a Wave Transmission Network and an AI-Based Estimation System

How to Select the Optimized Time Step and Mesh Size for FEM Thermal Transients Simulations of PWR Vessels and Nozzles by Means of Artificial Neural Networks

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

Research on Risk Assessment Method of Stick-Slip Vibration of the Bit Based on BP Neural Network Algorithm

Scheduling Strategy of Multiple Semi-Active Controllers With Information on the Disturbance and the Structural Response

Impact Resistance of Filament Wound Composite Pipes: A Parametric Study

Optimised Neural Network Prediction of Residual Stress Profiles for Structural Integrity Assessment of Pipe Girth Welds

Prediction of Pipe Girth Weld Residual Stress Profiles Using Artificial Neural Networks

Prediction of Fracture Toughness Transition From Tensile Test Data Applying Neural Networks

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